Development and Validation of an Efficient Multiplex PCR Assay for Simultaneous Detection of Six Common Foodborne Pathogens and Hygiene Indicators

Abstract

Microbial contamination in foods could lead to illnesses and substantial losses in both food industry and public health sectors. Rapid detection of microbial hazards (i.e., pathogens, hygiene indicator microorganisms) can accelerate surveillance and diagnostic processes reducing transmission and minimizing undesirable consequences. This study developed a multiplex PCR (m-PCR) for the detection of six common foodborne pathogens and hygiene indicators using specific primers for uidA of Escherichia coli, stx2 of Escherichia coli O157:H7, invA of Salmonella spp., int of Shigella spp., ntrA of Klebsiella pneumoniae, and ail of Yersinia enterocolitica and Yersinia pseudotuberculosis. Sensitivity of the m-PCR was 100 fg or ∼20 bacterial cells. Each primer set amplified only the targeted strain, and specificity was demonstrated by lack of nonspecific bands with DNA from 12 other bacterial strains. Following ISO 16140-2:2016, the relative limit of detection of the m-PCR was comparable to that of the gold-standard method; however, the processing time was five times faster. The m-PCR was applied to detect the six pathogens in 100 natural samples (50 pork meat and 50 local fermented food samples) and compared to results of the gold-standard method. Positive cultures for Klebsiella, Salmonella, and E. coli were 66%, 82%, and 88%, respectively, of meat samples and 78%, 26%, and 56%, respectively, of fermented food samples. Escherichia coli O157:H7, Shigella, and Yersinia were not detected in any of the samples by both standard and m-PCR methods. The developed m-PCR assay showed comparable results with the traditional culture technique proving its rapid and reliable detection of six foodborne pathogens and hygiene indicators in food.

Introduction

Effective surveillance for foodborne diseases has raised a major global public health concern over increasing rates of infection, underestimation of the problem, and the spread of drug resistant strains. In 2021, 538,608 cases of foodborne disease were reported in Thailand, with an associated morbidity rate of 813.96/100,000 population (Bureau of Epidemiology, 2021). During 2017, the Foodborne Diseases Active Surveillance Network (Foodnet) reported that the top five causes of infection in the United States included Salmonella spp., Shigella spp., Shiga toxin–producing Escherichia coli, and Yersinia spp. (Marder et al., 2018).

The abovementioned problematic species belong to Enterobacterales order. Salmonella spp. is the most common foodborne pathogen found in contaminated food. In Thailand the involved foods are notably eggs, dairy products, and meat (Ananchaipattana et al., 2012). E. coli, used as a fecal indicator index for food or drink, includes both commensal and pathogenic bacteria depending on strain (Jang et al., 2017). Pathogenic E. coli found in food can be divided into five groups: Shiga toxin–producing E. coli or enterohemorrhagic E. coli (STEC/EHEC), enteropathogenic E. coli, enteroinvasive E. coli, enteroaggregative E. coli, and enterotoxigenic E. coli.

Outbreaks of Escherichia coli O157:H7, one serotype of STEC/EHEC, caused by contaminated vegetables or beef were commonly reported in Europe, America, and Canada during 2006–2020 (CDC, 2020). A high detection rate (8%) of STEC was found in raw beef sold in markets in southern Thailand in 2016, a surprising increase from year 2015 (Sirikaew et al., 2016). In the past decade, non-O157 STEC serotypes have emerged and become more widespread around the world. These serotypes (namely O26, O103, O111, and O145) have also shown to cause hemolytic uremic syndrome in invasive cases (Bosilevac and Koohmaraie, 2011; Eichhorn et al., 2015; Hoyle et al., 2021).

Shigella spp. is a major cause of acute diarrhea in children and a serious problem in developing countries (Bodhidatta et al., 2010). Yersinia enterocolitica infections can cause gastrointestinal disease (yersiniosis) acquired by eating contaminated raw meat and poultry products (Rahman et al., 2011). Klebsiella spp., although uncommonly identified as a foodborne pathogen, in foods are good indicators for food hygiene, and multidrug-resistant Klebsiella pneumoniae found in raw and ready-to-eat foods have been shown to be a potential pool of antibiotic resistance genes (Hartantyo et al., 2020). Rising prevalence of K. pneumoniae in foods worldwide, that is, Singapore, Denmark, France, and Greece, is alarming in that the bacterium might not be only an indicator of poor hygiene given the strict food safety measures in these industrialized countries (Rodrigues et al., 2022; Theocharidi et al., 2022). Detection and elimination of these pathogens and indicators in food supply chain can significantly improve food safety as a whole.

Conventional culture is the gold-standard method to identify foodborne pathogenic and indicator bacteria. However, the process is multistep and time consuming due to long incubation times, laborious, and requires several biochemical tests. To better control transmission and lessen undesirable effects, a rapid and sensitive diagnostic method is needed to simultaneously detect and identify the most common foodborne pathogens and indicators. Monitoring for the bloody diarrhea-causing bacterium, EHEC serotype O157:H7, and for the underestimated gastrointestinal K. pneumoniae should be done in parallel to avoid undesirable loss.

A molecular method, multiplex PCR (m-PCR), provides an efficient alternative because it allows rapid and low-cost detection with high specificity and sensitivity and could be made readily available. m-PCR is among the approved techniques exploited to detect a large group of foodborne bacteria and shows comparable results with conventional culture methods while having a significantly shorter processing time (Huang et al., 2018). Several attempts to develop m-PCR assays that can detect and differentiate common foodborne pathogens have been made (Babu et al., 2013; Lee et al., 2014; Lei et al., 2008; Nguyen et al., 2016; Zhang et al., 2009) but, to our knowledge, no m-PCR focused on the six commonly found foodborne pathogens and indicators.

The aim of this study was, therefore, to develop the m-PCR which simultaneously targets E. coli, Escherichia coli O157:H7, Salmonella spp., Shigella spp., K. pneumoniae, and Y. enterocolitica. The six-plex PCR target genes previously reported to be specific to corresponding species and subspecies are uidA of E. coli (Anbazhagan et al., 2011), stx2 of Escherichia coli O157:H7 (Li et al., 2017; Van Giau et al., 2016), invA of Salmonella spp. (Chiu and Ou, 1996), int of Shigella spp. (Ranjbar et al., 2016), ntrA of K. pneumoniae (Anbazhagan et al., 2011), and ail of Y. enterocolitica and Yersinia pseudotuberculosis (Wiemer et al., 2011). The m-PCR was then validated in-house and subsequently used in natural sample detection in comparison to the culture technique.

Materials and Methods

Bacterial strains and culture conditions

The bacterial strains used in this study are listed in Table 1. For the m-PCR development, sensitivity testing, and validation, the following strains were used: Escherichia coli DMST 4212 (ATCC 25922), Escherichia coli O157:H7 MT, Klebsiella pneumoniae ATCC 27736, Salmonella Typhimurium DMST 562 (ATCC 13311), and Shigella dysenteriae DMST 15111. Bacterial strains were propagated and grown on tryptic soy agar and incubated at 37°C. After overnight incubation, single colonies were inoculated into tryptic soy broth and incubated at 37°C with shaking at 200 rpm for 18 h.

Table 1.

Bacterial Strains Used in This Study

Bacteria	Strain (equivalent to ATCC)
Bacillus cereus	ATCC 14579
Citrobacter freundii	MT
Enterobacter cloacae	MT
Escherichia coli ^a	DMST 4212 (ATCC 25922)
E. coli (ETEC)	DMST 30543
Escherichia coli O157:H7^a	MT
Klebsiella pneumoniae ^a	ATCC 27736
Listeria monocytogenes	DMST 20093
Proteus vulgaris	MT
Pseudomonas aeruginosa	DMST 4739 (ATCC 27853)
Salmonella Typhimurium^a	DMST 562 (ATCC 13311)
Salmonella Typhimurium	LT2
Shigella dysenteriae ^a	DMST 15111
Staphylococcus aureus	DMST 8840 (ATCC 25923)
Streptococcus suis	MT
Vibrio cholerae NAG	DMST 2873
Vibrio cholerae O1	DMST 4278 (ATCC 14035)
Vibrio cholerae O139	DMST 9701
Vibrio parahaemolyticus	DMST 21243 (ATCC 17802)

These strains were used for the m-PCR development, sensitivity testing, and validation.

ATCC, American Type Culture Collection; DMST, Department of Medical Sciences Thailand; ETEC, enterotoxigenic E. coli; LT, Lilleengen type; m-PCR, multiplex PCR; MT, Collection of Department of Medical Technology, Thammasat University.

DNA template preparation

For all strains except Y. enterocolitica, genomic DNA (gDNA) was extracted from 1 mL of an overnight culture using an E.Z.N.A Bacterial DNA Isolation Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer's protocol. After purification, the qualities (A260/230 and A260/280) and concentration (ng/μL) of gDNA were determined using a spectrophotometer (DeNovix, Wilmington, DE, USA). For Y. enterocolitica, the 700 bp synthetic oligonucleotide sequence of the ail gene from Yersinia enterocolitica ATCC27729 was used as the DNA template.

m-PCR primer design

Two pairs of primers for detection of Salmonella spp. (Chiu and Ou, 1996) and Y. enterocolitica (Wiemer et al., 2011) were obtained from literature reviews; four pairs of primers for detection of E. coli, Escherichia coli O157:H7, Shigella spp., and K. pneumoniae were designed in the present study using BioEdit 7.2 program. The sequences are shown in Table 2. Size and position of each PCR product were evaluated by single PCR amplification.

Table 2.

Primers Used in This Study

Species	Target gene	Primers	Sequence (5′-3′)	PCR product (bp)	References
Escherichia coli	uidA	Ecol600F	CTGGTATCAGCGCGAAGT	600	This study
Escherichia coli	uidA	Ecol600R	AGCGGGTAGATATCACACT	600
Shigella spp.	int	Shi438F	TGCATGGATCTTGCACTG	438	This study
Shigella spp.	int	Shi438R	TTTCCATTCCCTCCCTCG	438
Escherichia coli O157:H7	stx2	Ecol321F	CACATCGGTRTCTGTTATTA	321	This study
Escherichia coli O157:H7	stx2	Ecol321R	ACGCCAGATATGATGAAACC	321
Salmonella spp.	invA	Sal244F	ACAGTGCTCGTTTACGACCTGAAT	244	Chiu and Ou (1996)
Salmonella spp.	invA	Sal244R	AGACGACTGGTACTGATCGATAAT	244	Chiu and Ou (1996)
Yersinia enterocolitica	ail	Yer185F	GCAT(T/C)AACGA(A/G)TATGTTAGC	185	Wiemer et al. (2011)
Yersinia enterocolitica	ail	Yer185R	ATCGAGTTTGGAGTATTCAT	185	Wiemer et al. (2011)
Klebsiella spp.	ntrA	Kleb130F	CAACAGCTTGCCATGACG	130	This study
Klebsiella spp.	ntrA	Kleb130R	CGTGAAGATCGGTTTGCTC	130

The m-PCRs consisting of six primer sets were performed in a total volume of 25 μL containing 0.5 μM of each forward and reverse primer, 1 ng of each DNA template, and 1 × PCR Master Mix Kit (BiotechRabbit, Berlin, Germany). The m-PCR amplification was optimized by varying the annealing temperature from 50°C to 60°C in the Applied Biosystems SimpliAmp™ Thermal Cycler PCR (Life Technologies, South San Francisco, CA, USA). The amplification steps were followed by: 95°C for 2 min, 30 cycles each of 94°C for 30 s, 50–60°C for 45 s, and 72°C for 45 s, with a final extension at 72°C for 5 min. The PCR products were separated using 1.5% agarose gel electrophoresis, stained with 1 × GelRed (Merck, Darmstadt, Germany), and visualized under a UV transilluminator (Bio-Rad, Hercules, CA, USA).

Sensitivity and specificity tests of m-PCR

m-PCR sensitivity was determined by a serial dilution method. The DNA template of six species was 10-fold serially diluted from 100 pg to 1 fg (of each species) and applied into the m-PCR. m-PCR specificity was determined by testing the amplification on the genomes from 12 species of both Gram-negative and -positive bacteria (Table 1). The m-PCR condition for both sensitivity and specificity tests was 95°C for 2 min, 30 cycles each of 94°C for 30 s, 58°C for 45 s, and 72°C for 45 s, with a final extension at 72°C for 5 min.

In-house validation of m-PCR

Following ISO 16140-2:2016, the level of detection at 50% (LOD₅₀) of the developed m-PCR (LOD₅₀ m-PCR) was determined and compared with the standard method (LOD₅₀ Ref) using six samples of canned processed meat. Each analysis was replicated thrice along with the reference method for the six pathogens as previously described.

The process was performed as follows: (1) taking 25 g of each sample into sterile plastic bags and adding 225 mL pre-enrichment broth (brain-heart infusion for E. coli, Shigella broth for S. dysenteriae, buffered peptone water for Escherichia coli O157:H7, Salmonella Typhimurium, and K. pneumoniae, and peptone sorbitol bile salt for Y. enterocolitica); (2) spiking an inoculum of bacteria at 100 μL (low concentration, ∼0.7 colony-forming unit [CFU]/mL), 500 μL (medium concentration, ∼4 CFU/mL), and 1 mL (high concentration, ∼13 CFU/mL) of working stock culture (at ∼10 CFU/mL) into the sample, the bacterial concentration of spike inoculum was calculated using aerobic plate count technique; (3) incubating at appropriate conditions following the standard methods [BAM 2017 for E. coli (Feng et al., 2020), ISO21567:2004(E) for S. dysenteriae, AOAC Official Method, 2017 for Escherichia coli O157:H7, ISO6579-1:2017(E) for Salmonella Typhimurium, BAM 2017 for K. pneumoniae (Feng et al., 2020), and ISO10273:2017 for Y. enterocolitica].

The pre-enrichment samples were tested with the m-PCR and compared with standard methods; LOD₅₀ was calculated using the PODLOD calculation program. POD is probability of detection and LOD is limit of detection. The relative limit of detection (RLOD) was calculated using this equation:

Prevalence of six foodborne pathogens as determined by standard methods and m-PCR

One hundred samples (50 pork meats and 50 local fermented food [i.e., fermented pork, fish, or soybean typically consumed raw]) were collected from five different regions (north, northeast, east, south, and central) of Thailand. Detection of six bacterial strains was carried out following standard methods. Food samples (25 g each) were transferred into sterile plastic bags with 225 mL pre-enrichment broth, mixed with a stomacher, and incubated under appropriate conditions following standard methods. Enrichment broth was used for both direct culture and m-PCR techniques.

Total gDNA was extracted from pre-enrichment broths obtained during standard culture techniques by a boiling method (Ngamwongsatit et al., 2008). The following PCR cycling condition was used: a denaturation step at 95°C for 2 min, 30 cycles of 30 s at 94°C, 45 s at 58°C, 45 s at 72°C, and finally 5 min at 72°C. Spiked samples were used as positive controls. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the developed m-PCR were calculated from the results [including true positives (a), false negatives (b), false positives (c), and true negatives (d)] with the MedCalC program (https://www.medcalc.org/calc/diagnostic_test.php) using following equations: $% r e l a t i v e s e n s i t i v i t y = [a ∕ (a + b)] \times 100$ $% r e l a t i v e s p e c i f i c i t y = [d ∕ (c + d)] \times 100$

% r e l a t i v e a c c u r a c y = [(a + d) ∕ (a + b + c + d)] \times 100

% r e l a t i v e p o s i t i v e p r e d i c t i v e v a l u e = [a ∕ (a + c)] \times 100

% r e l a t i v e n e g a t i v e p r e d i c t i v e v a l u e = [d ∕ (c + d)] \times 100

Results and Discussion

m-PCR primers and assay development using purified templates

An efficient and sensitive m-PCR was developed to detect and identify six commonly found foodborne pathogens. Two primer pairs were obtained from literature reviews: Sal244F and Sal244R, specific primers to invasion protein A of Salmonella spp. (Chiu and Ou, 1996); Yer185F and Yer185R, specific primers to attachment-inversion locus protein of Y. enterocolitica (Wiemer et al., 2011). Ecol600F and Ecol600R, specific primers to beta-glucuronidase gene of E. coli, were designed in our study. Three primer pairs amplifying the integrase gene (int) of Shigella spp., Shiga toxin 2 (stx2) of Escherichia coli O157:H7, and nitrogen regulation gene (ntrA) of K. pneumoniae were also designed in this study.

As shown in Figure 1, all six primer pairs were combined in m-PCRs, and a total of six different sized PCR products were amplified in each reaction with the optimal annealing temperature at 58°C. The amplicon sizes of uidA, int, stx2, invA, ail, and ntrA are 600, 438, 321, 244, 185, and 130 bp, respectively, which can be easily discriminated by gel electrophoresis. Only expected positive bands were observed after m-PCRs were performed on one template DNA. Using a mixed DNA template from the six pathogens, six bands were observed in the gel representing a successful m-PCR.

FIG. 1.

Agarose gel electrophoresis analysis of m-PCR amplification products. Lane M, 100 bp DNA marker; Lane P, mixed DNA templates of the six pathogens. DNA samples from specific pathogens or hygiene indicators are indicated. m-PCR, multiplex PCR.

Although m-PCR assays that can differentiate common foodborne pathogens have already been developed (Babu et al., 2013; Lee et al., 2014; Lei et al., 2008; Nguyen et al., 2016; Zhang et al., 2009), an m-PCR designed for these six foodborne pathogens or hygiene indicator bacteria has not been done. Other quantitative m-PCRs (Liu et al., 2019; Van Lint et al., 2015) or more sophisticated techniques such as gene chip (Feng et al., 2016), m-PCR coupled with analyzer (Zhou et al., 2013) or with high-performance liquid chromatography (Xu et al., 2012) are reported. However, these techniques must be run in expensive instruments which are unaffordable for many laboratories and field studies.

Sensitivity and specificity of the developed m-PCR

The m-PCR was tested with 10-fold serially diluted, mixed DNA template to determine the sensitivity. The detection sensitivity of the m-PCR was 100 fg or ∼20 bacterial cells for all targeted strains (Fig. 2). To evaluate the primer specificity, the six pairs of primers were tested by PCR on DNA templates isolated from 12 strains of other bacteria (Citrobacter freundii, Enterobacter cloacae, Pseudomonas aeruginosa, Proteus vulgaris, Vibrio cholerae, Vibrio parahaemolyticus, Bacillus cereus, Bacillus subtilis, Enterococcus faecalis, Listeria monocytogenes, Staphylococcus aureus, and Staphylococcus epidermidis). The results showed that each primer pair amplified only the targeted strain and did not cross-react with DNA from any of the other 12 bacterial strains nor produce nonspecific bands and thus demonstrated its specificity (data not shown). The specificity of the diagnostic method provided confidence that the assay would avoid false positive results and unexpected signals.

FIG. 2.

Sensitivity of the m-PCR assay. Lane M, 100 bp DNA marker; Lane P, mixed DNA template of the six pathogens; Lane N, negative control or no DNA template.

In-house validation of m-PCR

The LOD₅₀ of the developed m-PCR was evaluated with six samples of canned processed meat inoculated with three concentrations (low, medium, and high) of each targeted bacterium, and the presence of the six foodborne bacteria was assessed by standard culture methods. Negative controls used for validation were samples without bacterial contamination. The pre-enrichment broth from each sample was tested with the developed m-PCR. The m-PCR results were analyzed and are shown in Table 3. Using the PODLOD program, the LOD₅₀ values of both the standard culture method and the m-PCR were calculated. The RLOD of the m-PCR was found to be comparable to that of the gold-standard method; however, the processing time was five times shorter. Thus, the m-PCR appeared suitable for a quick in-house analysis for contamination detection and prompt response.

Table 3.

Validation Results of Multiplex PCR

Targeted bacterial strain	LOD₅₀ culture (CFU/g)	LOD₅₀ m-PCR (CFU/g)	RLOD ^a
Escherichia coli DMST 30543 (ETEC)	0.27	0.27	1
Shigella dysenteriae DMST 15111	0.44	0.44	1
Escherichia coli O157:H7 (EHEC)	0.65	0.65	1
Salmonella Typhimurium LT2	0.36	0.36	1
Yersinia enterocolitica DMST 8012 (ATCC 27729)	0.68	0.68	1
Klebsiella pneumoniae ATCC 27736	0.64	0.64	1

RLOD = LOD₅₀ culture/LOD₅₀ m-PCR.

CFU, colony-forming unit; EHEC, enterohemorrhagic E. coli; LOD₅₀, level of detection at 50%; RLOD, relative limit of detection.

Prevalence of six foodborne target organisms as determined by standard methods and m-PCR

To assess the detection capability of the m-PCR in natural food samples, 100 meats and fermented foods from five different parts of Thailand were sampled and used to determine the prevalence of the six foodborne pathogens by both m-PCR and standard culture methods (Table 4).

Table 4.

Frequency of Six Pathogens in Food Samples as Determined by Standard Methods and Multiplex PCR

Sample	Collection area (region of Thailand)	Number of samples with bacterial contamination as determined by culture and m-PCR
		Escherichia coli	Escherichia coli O157:H7	Salmonella spp.	Shigella spp.	Klebsiella spp.	Yersinia enterocolitica
		Culture/m-PCR	Culture/m-PCR	Culture/m-PCR	Culture/m-PCR	Culture/m-PCR	Culture/m-PCR
Meat^a	North	9/10	0/0	9/7	0/0	7/9	0/0
	Northeast	9/9	0/0	6/3	0/0	7/9	0/0
	Central	10/9	0/0	10/10	0/0	5/8	0/0
	East	9/10	0/0	10/10	0/0	7/8	0/0
	South	7/10	0/0	6/6	0/0	7/6	0/0
Total (n = 50)		44/48	0/0	41/36	0/0	33/40	0/0
Fermented food^a	North	7/7	0/0	5/2	0/0	9/4	0/0
	Northeast	8/10	0/0	3/4	0/0	9/4	0/0
	Central	6/7	0/0	2/0	0/0	8/5	0/0
	East	6/6	0/0	3/2	0/0	8/5	0/0
	South	1/3	0/0	0/0	0/0	5/2	0/0
Total (n = 50)		28/33	0/0	13/8	0/0	39/30	0/0
Total (n = 100)		72/81	0/0	54/44	0/0	72/70	0/0

Meat refers to fresh pork, and fermented food refers to local fermented pork, fish, or soybean typically consumed raw.

Using BAM 2017, E. coli was detected in 72 (72%) samples, including 44 (88%) from meat and 28 (56%) from fermented foods. K. pneumoniae and Salmonella spp. were found in 72 (72%) samples (33 meat and 39 fermented food samples) and 54 (54%) samples (41 meat and 13 fermented food samples), respectively. Presumptive colonies were randomly chosen and confirmed for their biochemical characteristics. Total DNA was extracted from pre-enrichment broths obtained during standard culture and used as templates for m-PCR detection.

Prevalence of E. coli, K. pneumoniae, and Salmonella spp. in 100 meat and fermented food samples, as determined by the developed m-PCR, was 81%, 70%, and 44%, respectively. From 100 samples, there was no Escherichia coli O157:H7, Shigella spp., nor Y. enterocolitica detected by either culture technique or m-PCR. The m-PCR assay showed comparable results with those of the traditional culture technique proving its capacity to rapidly and reliably detect these six target organisms in foods. In addition, the developed m-PCR was able to detect multiple target organisms per food samples, for example, 30 (of 50) meat samples and 8 (of 50) fermented foods were positive for all E. coli, K. pneumoniae, and Salmonella. Sixty-four food samples (of 100) were positive for more than one target organism as detected by m-PCR.

Discrepancies in positive samples between culture and m-PCR techniques could be due to multiple aspects: (1) the lower gDNA release during boiling extraction, (2) bacterial cell wall physiology (particularly high capsule production in K. pneumoniae), and (3) nature of food sample (i.e., less positive samples were identified for Salmonella and K. pneumoniae by m-PCR than by culture).

Analysis by the MedCalC program (Table 5) showed that, in combined sample types, the developed m-PCR had high (>85%) sensitivity, accuracy, as well as high positive and negative predictive values for detection of E. coli. The uid primers still made up the best detection set in comparison to invA and ntrA. For Salmonella spp. detection, the pitfalls are relative sensitivity and negative predictive value, suggesting that this invA primer set is excellent in high contamination detection. Unfortunately, the ntrA primer set for K. pneumoniae showed only positive predictive value in acceptable range. The accuracy and negative predictive value by m-PCR for detection of K. pneumoniae were the lowest compared with that of E. coli and Salmonella spp. By taking a closer look at the calculated parameters, we have seen the effects of food types, that is, fermented food samples lowered the sensitivity in E. coli, Salmonella spp., and Klebsiella spp. (i.e., down to 96.13%, 46.15%, and 52.63%, respectively). This finding confirms the discrepancies in positive samples between culture and m-PCR techniques in that nature of food sample plays a role in detection.

Table 5.

The Relative Sensitivity, Specificity, Accuracy, Positive Predictive Value, and Negative Predictive Value of Developed Multiplex PCR Analyzed by MedCalC Program

Targeted bacteria	Sensitivity	Specificity	Accuracy	PPV	NPV
Escherichia coli	97.18	60.71	86.87	86.25	89.47
Salmonella spp.	74.07	91.30	82.00	90.91	75.00
Klebsiella spp.	69.44	60.71	67.00	81.97	43.59

NPV, negative predictive value; PPV, positive predictive value.

In conclusion, our results showed that 72% of specimens were contaminated by E. coli, more so in meats. High prevalence of E. coli is commonly found in food samples collected in Thailand (Minami et al., 2010), pointing out the importance of good hygiene and safe handling of raw foods. Salmonella spp. were detected at 54%, notably in meats from both retail and wholesale markets, while contamination of fermented foods was comparatively low. Ananchaipattana et al. (2012) revealed that ∼80% of meat samples were contaminated with Salmonella spp. while only 9% of fermented food samples were contaminated.

Prevalence of K. pneumoniae in food is underestimated in Thailand. This bacterium can contaminate various foods and contribute to food spoilage and human disease. In a recent study, K. pneumoniae strains were isolated from 9.9% of raw and cooked food samples, with fresh raw chicken showing the highest frequency (13.8%) (Guo et al., 2016). K. pneumoniae was found to contaminate 21% of raw and ready-to-eat foods (Hartantyo et al., 2020), while in the present study it was up to 72%. Contamination of K. pneumoniae in food might not cause any harm, but nonetheless it could act as a reservoir of antimicrobial-resistant K. pneumoniae (Guo et al., 2016). We developed an m-PCR assay which would provide a useful tool for monitoring and ensuring safety along the food chain with high sensitivity and specificity.

Footnotes

Authors' Contributions

N.N.: Conceptualization, funding acquisition, investigation, methodology, validation, writing–review and editing. S.C.: Conceptualization, funding acquisition, investigation, methodology, validation, writing–review and editing. R.A.: Conceptualization, funding acquisition, investigation, validation, writing–original draft, writing–review and editing.

Ethics Approval

This article does not contain data from any studies with human participants or animals which were performed by any of the authors.

Disclosure Statement

The authors declare that they have no conflicts of interest related to the studies reported here.

Funding Information

This work was supported by National Science and Technology Development Agency (Grant No. P-16-51223).

References

Ananchaipattana

, Hosotani

, Kawasaki

, et al. Prevalence of foodborne pathogens in retailed foods in Thailand. Foodborne Pathog Dis, 2012; 9:835–840; doi: 10.1089/fpd.2012.1169

Anbazhagan

, Mui

, Mansor

, et al. Development of conventional and real-time multiplex PCR assays for the detection of nosocomial pathogens. Braz J Microbiol, 2011; 42:448–458; doi: 10.1590/S1517-83822011000200006

AOAC Official Method 2017.01. Escherichia coli O157:H7 in Selected Foods 3M^TM Molecular Detection Assay (MDA) 2—E. coli O157 (Including H7) Method. First Action 2017. AOAC International: Rockville, MD, USA.

Babu

, Reddy

, Murali

, et al. Optimization and evaluation of a multiplex PCR for simultaneous detection of prominent foodborne pathogens of Enterobacteriaceae . Ann Microbiol, 2013; 63:1591–1599; doi: 10.1007/s13213-013-0622-0

Bodhidatta

, McDaniel

, Sornsakrin

, et al. Case-control study of diarrheal disease etiology in a remote rural area in Western Thailand. Am J Trop Med Hyg, 2010; 83:1106–1109; doi: 10.4269/ajtmh.2010.10-0367

Bosilevac

, Koohmaraie

. Prevalence and characterization of non-O157 shiga toxin-producing Escherichia coli isolates from commercial ground beef in the United States. Appl Environ Microbiol, 2011; 77:2103–2112; doi: 10.1128/AEM.02833-10

Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health. Epidemiology of Foodborne Disease. Mueng Nonthaburi, Nonthaburi; 2021. Available from: http://doe.moph.go.th/surdata/506wk/y64/d02_5264.pdf [Last accessed: September 12, 2022].

Centers for Disease Control and Prevention. Timeline for Reporting Cases of E. coli O157 Infection. Atlanta, GA; 2020. Available from: https://www.cdc.gov/ecoli/reporting-timeline.html [Last accessed: September 12, 2022].

Chiu

, Ou

. Rapid identification of Salmonella serovars in feces by specific detection of virulence genes, invA and spvC, by an enrichment broth culture-multiplex PCR combination assay. J Clin Microbiol, 1996; 34:2619–2622; doi: 10.1128/JCM.34.10.2619-2622.1996

10.

Eichhorn

, Heidenmanns

, Semmler

, et al. Highly virulent non-O157 enterohemorrhagic Escherichia coli (EHEC) serotypes reflect similar phylogenetic lineages, providing new insights into the evolution of EHEC. Appl Environ Microbiol, 2015; 81:7041–7047; doi: 10.1128/AEM.01921-15

11.

Feng

, Hu

, Huang

, et al. Study on rapid detection of seven common foodborne pathogens by gene chip. Afr J Microbiol Res, 2016; 10:285–291; doi: 10.5897/AJMR2015.7495

12.

Feng

, Weagant

, Grant

, et al. BAM chapter 4: Enumeration of Escherichia coli and the coliform bacteria. Bacteriological Analytical Manual. Content current as of: October 9, 2020. Available from: https://www.fda.gov/food/laboratory-methods-food/bam-chapter-4-enumeration-escherichia-coli-and-coliform-bacteria [Last accessed: September 20, 2022].

13.

Guo

, Zhou

, Qin

, et al. Frequency, antimicrobial resistance and genetic diversity of Klebsiella pneumoniae in food samples. PLoS One, 2016; 11:e0153561; doi: 10.1371/journal.pone.0153561

14.

Hartantyo

SHP

, Chau

, Koh

, et al. Foodborne Klebsiella pneumoniae: Virulence potential, antibiotic resistance, and risks to food safety. J Food Prot, 2020; 83:1096–1103; doi: 10.4315/JFP-19-520

15.

Hoyle

, Keith

, Williamson

, et al. Prevalence and epidemiology of non-O157 Escherichia coli serogroups O26, O103, O111, and O145 and shiga-toxin gene carriage in Scottisch cattle, 2014–2015. Appl Environ Microbiol, 2021; 87(10):e03142-20.

16.

Huang

, Lin

, Tsai

, et al. Detection of common diarrhea-causing pathogens in Northern Taiwan by multiplex polymerase chain reaction. Medicine (Baltimore), 2018; 97:e11006; doi: 10.1097/MD.0000000000011006

17.

ISO10273:2017. Microbiology of f the food chain—horizontal method for the detection of pathogenic Yersinia enterocolitica. International Organization for Standardization: Geneva, Switzerland.

18.

ISO21567:2004(E). Microbiology of food and animal feeding stuffs-horizontal method for the detection of Shigella spp. First edition 2004-11-01.

19.

ISO6579-1:2017(E). Microbiology of the food chain—horizontal method for the detection, enumeration and serotyping of Salmonella. First edition 2017-02.

20.

Jang

, Hur

, Sadowsky

, et al. Environmental Escherichia coli: ecology and public health implications—A review. J Appl Microbiol, 2017; 123:570–581; doi: 10.1111/jam.13468

21.

Lee

, Kwon

, Oh

, et al. A multiplex PCR assay for simultaneous detection of Escherichia coli O157:H7, Bacillus cereus, Vibrio parahaemolyticus, Salmonella spp., Listeria monocytogenes, and Staphylococcus aureus in Korean ready-to-eat food. Foodborne Pathog Dis, 2014; 11:574–580; doi: 10.1089/fpd.2013.1638

22.

Lei

IFM

, Roffey

, Blanchard

, et al. Development of a multiplex PCR method for the detection of six common foodborne pathogens. J Food Drug Anal, 2008; 16:37–43; doi: 10.3821/2/2224-6614.2341

23.

, Liu

, Wang

. Multiplex real-time PCR assay for detection of Escherichia coli O157:H7 and screening for non-O157 Shiga toxin-producing E. coli . BMC Microbiol, 2017; 17:215; doi: 10.1186/s12866-017-1123-2

24.

Liu

, Cao

, Wang

, et al. Detection of 12 common food-borne bacterial pathogens by TaqMan Real-Time PCR using a single set of reaction conditions. Front Microbiol, 2019; 10:222; doi: 10.3389/fmicb.2019.00222

25.

Marder

, Griffin

, Cieslak

, et al. Preliminary incidence and trends of infections with pathogens transmitted commonly through food—foodborne diseases active surveillance network, 10 U.S. sites, 2006–2017. MMWR Morb Mortal Wkly Rep, 2018; 67:324–328; doi: 10.1558/5/mmwr.mm6711a3

26.

Minami

, Chaicumpa

, Chongsa-Nguan

, et al. Prevalence of foodborne pathogens in open markets and supermarkets in Thailand. Food Control, 2010; 3:221–226; doi: 10.1016/j.foodcont.2009.05.011

27.

Ngamwongsatit

, Buasri

, Pianariyanon

, et al. Broad distribution of enterotoxin genes (hblCDA, nheABC, cytK, and entFM) among Bacillus thuringiensis and Bacillus cereus as shown by novel primers. Int J Food Microbiol, 2008; 121:352–356; doi: 10.1016/j.ijfoodmicro.2007.11.013

28.

Nguyen

, Giau

, Vo

. Development and evaluation of a multiplex PCR for simultaneous detection of five foodborne pathogens. J Adv Res Biotech, 2016; 1:9; doi: 10.1522/6/2475-4714/1/3/00114

29.

Rahman

, Bonny

, Stonsaovapak

, et al. Yersinia enterocolitica: Epidemiological studies and outbreaks. J Pathog, 2011; 2011:239391; doi: 10.4061/2011/239391

30.

Ranjbar

, Naghoni

, Afshar

, et al. Rapid molecular approach for simultaneous detection of Salmonella spp., Shigella spp., and Vibrio cholerae . Osong Public Health Res Perspect, 2016; 7:373-377; doi: 10.1016/j.phrp.2016.10.002

31.

Rodrigues

, Hauser

, Cahill

, et al. High prevalence of Klebsiella pneumoniae in European food products: A multicentric study comparing culture and molecular detection methods. Microbiol Spectr, 2022; 10:e0237621; doi: 10.1128/spectrum.02376-21

32.

Sirikaew

, Sukkua

, Rattanachuay

, et al. High level of shiga toxin-producing Escherichia coli and occurrence of stx-negative E. coli O157 from raw meats: characterization of virulence profile and genetic relatedness. Southeast Asian J Trop Med Public Health, 2016; 47:1008–1019.

33.

Theocharidi

, Balta

, Houhoula

, et al. High prevalence of Klebsiella pneumoniae in Greek meat products: Detection of virulence and antimicrobial resistance genes by molecular techniques. Foods, 2022; 11:708; doi: 10.3390/foods11050708

34.

Van Giau

, Nguyen

TKO

, et al. A novel multiplex PCR method for the detection of virulence-associated genes of Escherichia coli O157:H7 in food. 3 Biotech, 2016; 6(1):5; doi: 10.1007/s13205-015-0319-0

35.

Van Lint

, De Witte

, De Henau

, et al. 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, 2015; 34:535–542; doi: 10.1007/s10096-014-2257-x

36.

Wiemer

, Loderstaedt

, von Wulffen

, et al. Real-time multiplex PCR for simultaneous detection of Campylobacter jejuni, Salmonella, Shigella and Yersinia species in fecal samples. Int J Med Microbiol, 2011; 301:577–584; doi: 10.1016/j.ijmm.2011.06.001

37.

, Cui

, Tian

, et al. A multiplex polymerase chain reaction coupled with high-performance liquid chromatography assay for simultaneous detection of six foodborne pathogens. Food Control, 2012; 25:778–783; doi: 10.1016/j.foodcont.2011.12.014

38.

Zhang

, Zhang

, Yang

, et al. Simultaneous detection of Listeria monocytogenes, Staphylococcus aureus, Salmonella enterica and Escherichia coli O157:H7 in food samples using multiplex PCR method. J Food Safety, 2009; 29:348–363; doi: 10.1111/j.1745-4565.2009.00161.x

39.

Zhou

, Xiao

, Liu

, et al. Simultaneous detection of six food-borne pathogens by multiplex PCR with a GeXP analyzer. Food Control, 2013; 32:198–204; doi: 10.1016/j.foodcont.2012.11.044