Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes
Institute of Life Sciences & Resources, Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea
*
Author to whom correspondence should be addressed.
Foods 2021, 10(11), 2839; https://doi.org/10.3390/foods10112839
Submission received: 16 October 2021
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Revised: 9 November 2021
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Accepted: 15 November 2021
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Published: 17 November 2021
(This article belongs to the Special Issue New Challenges for Detection and Control of Food-Borne Pathogens: From Tools to People)
Abstract
:Coagulase-positive Staphylococcus aureus is a foodborne pathogen considered one of the causes of food-related disease outbreaks. Like S. aureus, Staphylococcus capitis, Staphylococcus caprae, and S. epidermidis are opportunistic pathogens causing clinical infections and food contamination. The objective of our study was to develop a rapid, accurate, and monitoring technique to detect four Staphylococcus species in food. Four novel molecular targets (GntR family transcriptional regulator for S. aureus, phosphomannomutase for S. epidermidis, FAD-dependent urate hydroxylase for S. capitis, and Gram-positive signal peptide protein for S. caprae) were mined based on pan-genome analysis. Primers targeting molecular target genes showed 100% specificity for 100 non-target reference strains. The detection limit in pure cultures and artificially contaminated food samples was 102 colony-forming unit/mL for S. aureus, S. capitis, S. caprae, and S. epidermidis. Moreover, real-time polymerase chain reaction successfully detected strains isolated from various food matrices. Thus, our method allows an accurate and rapid monitoring of Staphylococcus species and may help control staphylococcal contamination of food.
1. Introduction
Staphylococci are common pathogens, widely distributed in nature and frequently isolated from food. Staphylococcus aureus is one of the most important foodborne pathogens, producing staphylococcal enterotoxins causing diarrhea and vomiting through direct invasion or systemic transmission, adversely affecting human health [1,2]. Unlike foodborne pathogenic S. aureus, which is a coagulase-positive Staphylococcus, S. capitis, S. caprae, and S. epidermidis are coagulase-negative staphylococci (CoNS) [3]. Traditionally considered commensals, CoNS species are now recognized as opportunistic pathogens [4]. Moreover, CoNS species contaminate foods and have become a prominent pathogenic strain in ready-to-eat foods [1]. In the CoNS group, S. epidermidis is the most common pathogen associated with human infections, such as bacteremia and endocarditis in immunocompromised patients [5]. S. capitis is occasionally associated with hospital-acquired meningitis and native and prosthetic valve endocarditis. At the same time, S. caprae infections include bacteremia and prosthetic infections [4,5,6]. S. saccharolyticus has been reported to be associated with human infection but has rarely been isolated from food. Phylogenetic analysis using the 16S rRNA gene classified S. epidermidis into the S. epidermidis group, including S. capitis, S. caprae, and S. saccharolyticus [5]. These species are closely phenotypically related, so it is difficult to distinguish them. Therefore, accurate methods to detect and discriminate Staphylococcus species contamination in food are needed to reduce disease outbreaks and ensure food safety.
Traditional methods for detecting pathogenic bacteria require multiple steps, including pre-enrichment, selective growth, and selective plating, which can then be characterized by analyzing additional biochemical tests [7]. The process is labor-intensive and time-consuming, is not cost-effective, and is also unable to detect viable but nonculturable cells [8,9]. Various staphylococci detection methods exist, both phenotypic and genotypic. Other detection methods, such as the Staph-Zym test, the API Staph test, and the BF Phenix Automated Microbiology system, have been used for the detection of staphylococci based on their phenotypic properties [10,11]. Recently, an attempt has been made to detect staphylococci using matrix-assisted laser desorption ionization–time-of-flight mass spectrometry based on protein profiles [12]. However, the low accuracy (50–70%) of the biochemical reaction of the API kit and the high initial acquisition cost of automatic mass spectrometry systems restrict their application [13,14]. Compared to genotypic detection methods, such as amplified fragment length polymorphic fingerprinting and polymerase chain reaction (PCR)-based methods, phenotypic tests are less accurate [1,14,15].
The development of simple and rapid methods with high specificity and sensitivity is critical for detecting pathogenic bacteria. In recent decades, molecular detection methods, such as PCR, have been widely employed for pathogen detection because they are faster and simpler than conventional culture methods [16,17]. Among the PCR-based methods, real-time PCR has become a useful tool for detecting and quantifying bacterial species associated with foods due to its superiority, such as high sensitivity and efficiency [8,17,18,19,20,21]. At present, real-time PCR is used for monitoring staphylococci in food processing [1,22,23].
The selection of appropriate genes or sequences is critical for pathogenic bacteria detection using PCR. Various molecular target genes were used to detect Staphylococcus species, namely, 16S rRNA gene, tuf (elongation factor Tu), sodA (superoxide dismutase A), nuc (thermostable nuclease), and dnaJ (chaperone dnaJ) [11,24,25,26]. However, some of these genes cannot discriminate between phylogenetically closely related species, such as the S. epidermidis and S. aureus cluster groups. The lack of exclusivity and inclusivity of molecular target genes is one major drawback of implementing the PCR-based method in food inspection [27]. Advances in high-throughput sequencing technology have allowed increasing the number of whole-genome sequences available in public databases, making it easier to obtain target genes specific for Staphylococcus species using bioinformatics approaches [27]. This study aimed to develop a real-time PCR assay targeting molecular target genes to detect the staphylococci S. aureus, S. capitis, S. caprae, and S. epidermidis, thus allowing their accurate and rapid monitoring in food matrix.
2. Materials and Methods
2.1. Evaluation of Staphylococci Genomes
The genome sequences of 155 Staphylococcus strains were obtained from the National Center for Biotechnology Information (NCBI). Staphylococcus genomes consist of the S. aureus cluster group (8 S. argenteus, 35 S. aureus, 9 S. schweitzeri, 9 S. simiae), the S. epidermidis cluster group (16 S. capitis, 15 S. caprae, 28 S. epidermidis, 18 S. saccharolyticus), 9 S. pasteuri, 8 S. warneri. Detailed genome information is shown in Table S1. Phylogeny analysis was performed using anvi’o software version 7.0 [28] to confirm the taxonomic position of the genomes obtained from publicly available databases. Assembled genomes were first used to generate genome storage files using the “anvi-gene-genomes-storage” command. Genome storage was then used for phylogeny analysis based on pan-genome using the “anvi-pan-genome” command. The average nucleotide identity (ANI) value was calculated using the “anvi-compute-ani” command, which uses PyANI [29].
2.2. Mining of Molecular Target Genes
The molecular target genes were mined using bacterial pan-genome analysis (BPGA) v1.3 [30]. The criteria for selecting molecular target genes of four species were 100% presence in the respective target species and absence in non-target species. The genomes of staphylococci were constructed using two databases: a core gene database for target species and a pan gene database for non-target species. Then, the two databases were compared to search for target genes with a cut-off value of 50%, default parameter. The candidate target genes were searched using BLAST to further identify the genes specific to each species. The genes that were absent, with 72,899,005 other bacterial sequences, were considered molecular target genes. The specificity of the discovered molecular target genes and of the reported target genes (tuf, sodA, nuc, and dnaJ) was confirmed by aligning them with 94 S. aureus, S. capitis, S. caprae, and S. epidermidis genomes.
2.3. Design of Specific Primers
Based on the sequences of the target genes, primer pairs were designed using Primer Designer (Scientific and Education Software, Durham, NC, USA) with the following criteria: G + C content, 40–60%, Tm value, 65 °C and 75 °C, and no ability to form dimers. The specificity of the primer pairs was checked using the primer-BLAST tool [31].
2.4. DNA Extraction
The reference strains, including 39 Staphylococcus strains and 73 non-Staphylococcus strains, are listed in Table 1. All reference strains were grown in tryptic soy broth (TSB, Difco, Becton & Dickinson, Sparks, MD, USA) at 37 °C for 24 h. Genomic DNA of all staphylococci and non-staphylococci strains was extracted using the G-spin genomic DNA extraction kit (Intron Biotechnology, Seongnam, Korea) according to the manufacturer’s instructions. The DNA purity and concentration were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
2.5. Real-Time PCR Condition
Real-time PCR was performed in a 20 µL reaction mixture containing 10 µL of 2× Thunderbird SYBR® qPCR mix (Toyobo, Osaka, Japan), 1 µL of each primer (10 pmol/µL), 1 µL of template (20 ng/µL), and 7 µL of distilled water. A CFX96 Touch Deep (Bio-Rad, Hercules, CA, USA) was used for thermal cycling as follows: denaturation at 95 °C for 5 min, followed by 35 cycles at 95 °C for 5 s and 60 °C for 30 s. The melting curve was obtained by increasing the temperature between 60 °C and 95 °C in 0.5 °C increments while holding for 15 s at each step.
2.6. Specificity of the Primer Pairs
The specificity of the primer pairs was confirmed by 112 bacterial strains (Table 1). If a primer pair successfully produced an amplification plot from strains of the corresponding species, it was then tested against other Staphylococcus strains and non-Staphylococcus strains. For sensitivity testing, cultures of S. aureus ATCC 6538, S. epidermidis KACC 13234, S. capitis KACC 13242, and S. caprae KCTC 3583 were serially diluted (102–108 colony-forming units (CFU/mL), and genomic DNA was extracted as described in Section 2.4, followed by real-time PCR.
2.7. Application in Artificially Contaminated Food Samples
The reliability of the established real-time PCR assay for pathogenic Staphylococcus detection in the food matrix was determined according to previous studies [1,19]. All strains were grown in TSB for 24 h at 37 °C, and plate counting was performed in TSA medium. For the spiking experiment, milk samples were purchased, and the absence of four Staphylococcus species was confirmed. Spiked samples were prepared by inoculating the cocktail of S. aureus ATCC 6538, S. epidermidis KACC 13234, S. capitis KACC 13242, and S. caprae KCTC 3583 at a concentration of 102–108 CFU/mL each in 25 mL of milk samples. The prepared spiked samples were then homogenized for 1 min. The genomic DNA of each homogenized sample was extracted under the conditions described in Section 2.4. Real-time PCR analysis was conducted at the conditions described in Section 2.5.
2.8. Application in Isolated Staphylococcus Strains
Twenty-two samples (4 chicken, 3 beef, 5 pork, 3 fish, 5 salted fish, and 2 raw milk) were collected in Korea to confirm the feasibility of the detection of staphylococci by real-time PCR. Twenty-five grams of food sample was transferred to stomacher bags and then homogenized with 225 mL saline and mixed thoroughly to isolate Staphylococcus strains. Serial dilutions of the homogenized samples were prepared, and a small volume of each of them was spread on mannitol salt agar (Difco) media and incubated at 37 °C for 24 h. Then, the genomic DNAs of unknown isolates were used for identification by the real-time PCR method developed in this study.
2.9. Detection of Staphylococcus Species in Food Samples
Fifty samples, including 2 samples of ready-to-eat vegetables (1 cucumber and 1 lettuce), 10 samples of meat (2 beef and 8 pork), 34 samples of raw milk, and 4 samples of salted fish, were randomly collected from local markets in Korea. Ten grams of each sample was homogenized using a blender. The genomic DNA of the food samples was extracted under the conditions described in Section 2.4. Detection of four Staphylococcus species in unknown samples was performed under the conditions described in Section 2.5.
3. Results
3.1. Evaluation of Staphylococci Whole-Genome Sequences
Misclassified genomes for closely related bacteria are often reported in public database [20,32,33]. The phylogenetic tree based on pan-genome cluster frequencies was clustered according to the species name, and two clusters were generated (Figure 1). The first cluster included S. aureus, S. argenteus, and S. schweitzeri, and the second cluster included S. epidermidis, S. simiae, S. warneri, S. pasteuri, S. saccharolyticus, S. caprae, and S. capitis. However, some genomes, such as those of S. pasteuri SP1, S. saccharolyticus IIF7SG_B1, 151250007-1-258-46, and IIF6SC-B4A, clustered with those of other species. S. pasteuri SP1 clustered with S. warneri. S. saccharolyticus IIF7SG_B1, 151250007-1-258-46, and IIF6SC-B4A clustered with S. pasteuri. In OrthoANI analysis, S. pasteuri SP1 showed 95.95% identity with an S. warneri-type strain (NCTC 11044) and 83.37% identity with an S. pasteuri-type strain (DSM 10656). In addition, S. saccharolyticus IIF7SG_B1, 151250007-1-258-46, and IIF6SC-B4A showed 98.93–98.98% identity with an S. pasteuri-type strain (DSM 10656) and 76.72–76.95% identity with an S. saccharolyticus-type strain (NCTC 11807). These results suggest that these strains should be corrected in the public database to avoid further misidentification.
3.2. Pan-Genome Analysis
One hundred fifty-five genomes were retrieved for pan-genome analysis. To screen target genes for detecting the four Staphylococcus species, 388,852 coding genes from 155 staphylococci genomes yielded a pan-genome of 9461 genes consisting of 464 core-genes, 6772 accessory-genes, and 2225 unique-genes. Of the 6772 accessory genes, 4, 13, 19, and 18 genes were common to 35 S. aureus, 16 to S. capitis genomes, 15 to S. caprae, and 28 to S. epidermidis. Fifty-four candidate genes were aligned with other sequences through the blast program. The molecular target genes were mined based on their 100% presence in all genomes of the target species and their absence in the genomes of non-target staphylococci. The functions of the molecular target genes selected for the detection of Staphylococcus species are as follows: GntR family transcriptional regulator (accession no. ABD29409.1) for S. aureus, FAD-dependent urate hydroxylase (accession no. AKL93396.1) for S. capitis, Gram-positive signal peptide protein, YSIRK family (accession no. BBD88784.1) for S. caprae, and phosphomannomutase (accession no. AAO05683.1) for S. epidermidis.
The specificity of the discovered molecular target genes in comparison with that of the previously reported target genes in detecting four Staphylococcus species was confirmed; we observed higher specificity for our molecular target genes (Table S2). All target genes except the nuc gene were present with high similarities (86–100%) not only in the target species but also in the non-target species. These results indicated that the four molecular target genes are suitable for the identification of Staphylococcus species.
Specific primers were designed for each molecular target gene, and the specificity was confirmed through in silico analysis. As a result, all primer pairs produced amplicons only in the target species (Table S3). For S. aureus-specific primers, we found that certain genome sequences, including those of Staphylococcus species MZ1, MZ3, MZ7, MZ8, MZ9, T93, SM3655, and SM9054, produced an amplicon of 145 bp. Eight genomes of Staphylococcus species showed 98.83 to 99.87% identity with an S. aureus-type strain (DSM 20231), suggesting that these genomes belong to S. aureus.
3.3. Specificity and Sensitivity of Real-Time PCR
A real-time PCR was designed to identify the four staphylococci species that cause food poisoning and are often isolated from food [34]. The specificity was tested using 12 target Staphylococcus strains, 27 non-target Staphylococcus strains, and 73 non-Staphylococcus strains (Table 1). The primer sequences and amplicon size are shown in Table 2. All S. aureus, S. capitis, S. caprae, and S. epidermidis strains yielded detectable amplicons for the corresponding primer pairs, whereas no amplifications were generated with the non-target Staphylococcus strains and non-Staphylococcus strains, indicating the high specificity of the four primer pairs (Figure 2).
The sensitivity analysis was performed using DNA from serial dilutions of the target bacterial species (102–108 CFU/mL) as a template. All tests were repeated thrice, and the standard curves are presented in Figure 3. For the detection of S. aureus, the sensitivity was 1.5 × 102 CFU/mL. Similarly, the limit of detection of S. capitis, S. caprae, and S. epidermidis was 2.6 × 102 CFU/mL, 1.4 × 102 CFU/mL, and 1.09 × 102 CFU/mL, respectively. The R2 values were higher than 0.997, indicating that the amounts of DNA showed high linearity with the corresponding Ct values [35]. The equations for S. aureus, S. capitis, S. caprae, and S. epidermidis were y = −3.582x + 41.759, y = −3.293x + 39.33, y = −3.522x + 41.263, and y = −3.134x + 40.753 respectively. The amplification efficiencies for the four Staphylococcus species ranged from 90.20% to 108.50%, indicating high efficiency [35].
3.4. Detection of Staphylococcus Species in Artificially Contaminated Food Samples
DNA present in a food matrix can impair the efficiency of real-time PCR, as its concentration can be underestimated [36]. In this study, the quantification in food matrix was conducted by artificially adding four Staphylococcus species to milk, a food product they mainly inhabit. Simultaneously, the milk samples were inoculated with a cocktail of four pathogenic Staphylococcus species to induce competition between strains for the same nutrients. Food samples artificially inoculated with Staphylococcus species (average Ct values: 14.11–33.27) had Ct values similar to those of pure cultured bacteria (average Ct values: 13.7–33.82). All standard curves showed high efficiency, with R2 of 0.998 for S. aureus, 0.997 for S. capitis and S. epidermidis, and 0.994 S. caprae (Figure 4). The limit of detection values were 1.5 × 102 CFU/mL for S. aureus, 2.6 × 102 CFU/mL for S. capitis, 1.4 × 102 CFU/mL for S. caprae, and 1.2 × 102 CFU/mL for S. epidermidis. The detection limits for the four Staphylococcus species in artificially contaminated milk samples were similar to those in pure cultures. These results suggested that the real-time PCR method could detect the four Staphylococcus species almost without any interference from the food matrix.
3.5. Real-Time PCR Detection of Isolates
A total of 103 strains were isolated from chicken, beef, pork, fish, salted fish, and raw milk. All isolates produced one amplification curve: 36 isolates (34.95%), 63 isolates (61.17%), and 4 isolates (3.88%) were identified as S. aureus, S. epidermidis, and S. capitis (Table 3). S. aureus was isolated from meat such as chicken, beef, pork, and fish. S. epidermidis was isolated from pork, salted fish, and raw milk. S. capitis was isolated only from salted fishes, and S. caprae was not isolated from any sample.
3.6. Detection of Contamination by the Four Staphylococcus Species in Food Samples
To confirm the efficacy of real-time PCR for the detection of Staphylococcus species contamination in food samples, 50 samples were tested using the real-time PCR method developed in this study. S. aureus was detected in 11 samples of pork and raw milk, and S. epidermidis was detected in 9 samples of raw milk. S. capitis was detected in three samples of fermented fish and raw milk. S. caprae was not present in any of the food samples. The result showed that the detection rates of the Staphylococcus species were 22% for S. aureus, 6% for S. capitis, and 18% for S. epidermidis (Table 4). These results are consistent with a previous study suggesting that S. epidermidis and S. aureus are the main Staphylococcus species contaminating food [1].
4. Discussion
S. aureus and S. epidermidis are important pathogenic bacteria that cause clinical infections and food contamination [7,37]. These two pathogens are isolated in a wide range of foods, such as vegetables, meat, and fish [38]. S. capitis and S. caprae are species closely related to S. epidermidis, an opportunistic CoNS. They contaminate milk or meat and have been isolated from fermented foods such as cheese [34]. Therefore, developing reliable and rapid methods to detect these four pathogenic Staphylococcus species has become increasingly important to protect public health and ensure food safety [17]. Here, we developed a rapid and accurate detection method for four pathogenic Staphylococcus species based on novel molecular target genes.
Molecular detection methods play an important role in rapidly detecting pathogenic bacteria [27]. The usefulness of molecular detection methods is dependent on the target genes or sequences and the specificity of specific primer pairs [27]. The current PCR methods for pathogenic staphylococci target 16S rRNA genes, housekeeping genes, or virulence genes [26,38]. However, a previous study has reported that the 16S rRNA genes of the S. epidermidis group share high sequence similarities (≥97%) and do not exhibit sufficient variability to allow differentiating the species [39]. In addition, the lack of virulence genes can result in misclassification, posing a potential threat of food poisoning [1,40]. As numerous whole-genome sequences become available with the development of genome sequencing technologies, many researchers are committed to the search for novel molecular target genes to replace the current markers that exhibit poor specificity [20,21,40,41]. In this study, pan-genome analysis was utilized for discovering molecular target genes of Staphylococcus species. We successfully identified molecular target genes specific for four Staphylococcus species via a pan-genome analysis. At the same time, we found misclassified staphylococci genomes. Through pan-genome analysis, we found that four molecular target genes were 100% specific for identifying Staphylococcus species and did not match other bacterial genes.
PCR methods are specific for detecting causative pathogens of infectious diseases and for discriminating closely related species [42]. Previous studies have reported that the PCR-based detection method of Staphylococcus species is more rapid, easier, and sensitive than traditional methods [1,7,11,43]. Real-time PCR methods provide a tool for the sensitive and accurate quantification of target bacteria, which could be applied to detect Staphylococcus species in foods [22,23,44]. Although several real-time PCR methods for detecting Staphylococcus species have been reported, their target genes or sequences have been shown poor specificity [1]. Recently, a real-time PCR method targeting novel specific genes obtained by a pan-genome analysis for the accurate detection of Staphylococcus species has been developed [1]. This method displayed a better specificity than the previous real-time PCR method. However, for monitoring Staphylococcus species using existing real-time PCR methods, previous studies focused on discovering novel genes for S. aureus and S. epidermidis [1], while no target genes for S. capitis and S. caprae, which are closely related species to S. epidermidis, have been reported. In this study, we successfully identified molecular target genes for S. capitis and S. caprae with high specificity and sensitivity. More surprisingly, the detection limit of S. aureus (102 CFU/mL) was equivalent to that of previously reported target genes [1]. In contrast, the detection limit for S. epidermidis molecular target gene showed an obvious advantage in this study. The real-time PCR method developed in this study maintained a good consistency in detecting the four Staphylococcus species, without interference from the food matrix. Moreover, this method was successfully applied to 103 strains isolated from chicken, beef, pork, fish, salted fish, and raw milk. These results indicate that the molecular target genes discovered in this study have specificity in real-time PCR analysis, allowing the rapid, accurate, and sensitive detection of the four Staphylococcus species in food matrix.
In conclusion, we successfully mined four molecular target genes for the four Staphylococcus species S. aureus, S. capitis, S. caprae, and S. epidermidis. We developed a real-time PCR to detect the four Staphylococcus species with high specificity and high sensitivity. Our real-time PCR method was able to successfully detect the four pathogenic Staphylococcus species in food. Our data show that the method has a great potential as an accurate, rapid, and sensitive tool to monitor potential pathogenic Staphylococcus species in food samples.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/foods10112839/s1, Table S1: Summary of the genome features of 155 staphylococci strains, Table S2: Presence of novel and reported molecular target genes for target and non-target genomes, Table S3: Specificity of the designed primer pairs using primer-BLAST tool.
Author Contributions
Conceptualization, E.K. and H.-Y.K.; methodology, E.K.; software, S.-M.Y.; validation, D.-S.K.; investigation, J.-E.W. and D.-Y.K.; data curation, S.-M.Y. and D.-Y.K.; writing—original draft preparation, E.K.; writing—review and editing, H.-Y.K.; visualization, S.-M.Y.; supervision, H.-Y.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number 2020R1A6A3A01100168).
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Zhou, B.; Ye, Q.; Chen, M.; Li, F.; Xiang, X.; Shang, Y.; Wang, C.; Zhang, J.; Xue, L.; Wang, J.; et al. Novel species-specific targets for real-time PCR detection of four common pathogenic Staphylococcus spp. Food Control 2022, 131, 108478. [Google Scholar] [CrossRef]
- Argudín, M.Á.; Mendoza, M.C.; Rodicio, M.R. Food Poisoning and Staphylococcus aureus Enterotoxins. Toxins 2010, 2, 1751–1773. [Google Scholar] [CrossRef]
- Becker, K.; Heilmann, C.; Peters, G. Coagulase-negative staphylococci. Clin. Microbiol. Rev. 2014, 27, 870–926. [Google Scholar] [CrossRef] [Green Version]
- Cameron, D.R.; Jiang, J.H.; Hassan, K.A.; Elbourne, L.D.H.; Tuck, K.L.; Paulsen, I.T.; Peleg, A.Y. Insights on virulence from the complete genome of Staphylococcus capitis. Front. Microbiol. 2015, 6, 980. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Watanabe, S.; Aiba, Y.; Tan, X.E.; Li, F.Y.; Boonsiri, T.; Thitiananpakorn, K.; Cui, B.; Sato’O, Y.; Kiga, K.; Sasahara, T.; et al. Complete genome sequencing of three human clinical isolates of Staphylococcus caprae reveals virulence factors similar to those of S. epidermidis and S. capitis. BMC Genom. 2018, 19, 810. [Google Scholar] [CrossRef]
- Ahle, C.M.; Stødkilde, K.; Afshar, M.; Poehlein, A.; Ogilvie, L.A.; Söderquist, B.; Hüpeden, J.; Brüggemann, H. Staphylococcus saccharolyticus: An overlooked human skin colonizer. Microorganisms 2020, 8, 1105. [Google Scholar] [CrossRef] [PubMed]
- Geng, Y.; Liu, S.; Wang, J.; Nan, H.; Liu, L.; Sun, X.; Li, D.; Liu, M.; Wang, J.; Tan, K. Rapid Detection of Staphylococcus aureus in Food Using a Recombinase Polymerase Amplification-Based Assay. Food Anal. Methods 2018, 11, 2847–2856. [Google Scholar] [CrossRef]
- Xie, X.; Liu, Z. Simultaneous enumeration of Cronobacter sakazakii and Staphylococcus aureus in powdered infant foods through duplex TaqMan real-time PCR. Int. Dairy J. 2021, 117, 105019. [Google Scholar] [CrossRef]
- Zhao, X.; Zhong, J.; Wei, C.; Lin, C.W.; Ding, T. Current perspectives on viable but non-culturable state in foodborne pathogens. Front. Microbiol. 2017, 8, 580. [Google Scholar] [CrossRef] [Green Version]
- Ćirković, I.; Hauschild, T.; Ježek, P.; Dimitrijević, V.; Vuković, D.; Stepanović, S. Identification and antimicrobial susceptibility testing of Staphylococcus vitulinus by the BD Phoenix automated microbiology system. Curr. Microbiol. 2008, 57, 158–160. [Google Scholar] [CrossRef]
- Kim, J.; Hong, J.; Lim, J.A.; Heu, S.; Roh, E. Improved multiplex PCR primers for rapid identification of coagulase-negative staphylococci. Arch. Microbiol. 2018, 200, 73–83. [Google Scholar] [CrossRef] [Green Version]
- Kim, E.; Kim, H.J.; Yang, S.M.; Kim, C.G.; Choo, D.W.; Kim, H.Y. Rapid identification of Staphylococcus species isolated from food samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Microbiol. Biotechnol. 2019, 29, 548–557. [Google Scholar] [CrossRef] [PubMed]
- Koop, G.; De Visscher, A.; Collar, C.A.; Bacon, D.A.C.; Maga, E.A.; Murray, J.D.; Supré, K.; De Vliegher, S.; Haesebrouck, F.; Rowe, J.D.; et al. Short communication: Identification of coagulase-negative Staphylococcus species from goat milk with the API Staph identification test and with transfer RNA-intergenic spacer PCR combined with capillary electrophoresis. J. Dairy Sci. 2012, 95, 7200–7205. [Google Scholar] [CrossRef]
- González-Domínguez, M.S.; Carvajal, H.D.; Calle-Echeverri, D.A.; Chinchilla-Cárdenas, D. Molecular Detection and Characterization of the mecA and nuc Genes From Staphylococcus Species (S. aureus, S. pseudintermedius, and S. schleiferi) Isolated From Dogs Suffering Superficial Pyoderma and Their Antimicrobial Resistance Profiles. Front. Vet. Sci. 2020, 7, 376. [Google Scholar] [CrossRef]
- Szaluś-Jordanow, O.; Chrobak, D.; Pyrgiel, M.; Lutyńska, A.; Kaba, J.; Czopowicz, M.; Witkowski, L.; Kizerwetter-Świda, M.; Binek, M.; Frymus, T. PFGE and AFLP genotyping of Staphylococcus aureus subsp. anaerobius isolated from goats with Morel’s disease. Arch. Microbiol. 2013, 195, 37–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mandal, P.K.; Biswas, A.K.; Choi, K.; Pal, U.K. Methods for rapid detection of foodborne pathogens: An overview. Am. J. Food Technol. 2011, 6, 87–102. [Google Scholar] [CrossRef] [Green Version]
- Wei, S.; Daliri, E.B.M.; Chelliah, R.; Park, B.J.; Lim, J.S.; Baek, M.A.; Nam, Y.S.; Seo, K.H.; Jin, Y.G.; Oh, D.H. Development of a multiplex real-time PCR for simultaneous detection of Bacillus cereus, Listeria monocytogenes, and Staphylococcus aureus in food samples. J. Food Saf. 2019, 39, e12558. [Google Scholar] [CrossRef] [Green Version]
- Yang, S.-M.; Kim, E.; Kim, D.; Kim, H.-B.; Baek, J.; Ko, S.; Kim, D.; Yoon, H.; Kim, H.-Y. Rapid Real-Time Polymerase Chain Reaction for Salmonella Serotyping Based on Novel Unique Gene Markers by Pangenome Analysis. Front. Microbiol. 2021, 12, 750379. [Google Scholar] [CrossRef] [PubMed]
- Kim, E.; Kim, H.B.; Yang, S.M.; Kim, D.; Kim, H.Y. Real-time PCR assay for detecting Lactobacillus plantarum group using species/subspecies-specific genes identified by comparative genomics. LWT 2021, 138, 110789. [Google Scholar] [CrossRef]
- Kim, E.; Yang, S.M.; Kim, H.Y. Differentiation of lacticaseibacillus zeae using pan-genome analysis and real-time pcr method targeting a unique gene. Foods 2021, 10, 2112. [Google Scholar] [CrossRef]
- Kim, E.; Yang, S.M.; Kim, D.; Kim, H.Y. Real-time PCR method for qualitative and quantitative detection of Lactobacillus sakei group species targeting novel markers based on bioinformatics analysis. Int. J. Food Microbiol. 2021, 355, 109335. [Google Scholar] [CrossRef] [PubMed]
- Rodríguez, A.; Gordillo, R.; Andrade, M.J.; Córdoba, J.J.; Rodríguez, M. Development of an efficient real-time PCR assay to quantify enterotoxin-producing staphylococci in meat products. Food Control 2016, 60, 302–308. [Google Scholar] [CrossRef]
- Kilic, A.; Basustaoglu, A.C. Double triplex real-time PCR assay for simultaneous detection of Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus hominis, and Staphylococcus haemolyticus and determination of their methicillin resistance directly from positive blood cult. Res. Microbiol. 2011, 162, 1060–1066. [Google Scholar] [CrossRef]
- Shah, M.M.; Iihara, H.; Noda, M.; Song, S.X.; Nhung, P.H.; Ohkusu, K.; Kawamura, Y.; Ezaki, T. dnaJ gene sequence-based assay for species identification and phylogenetic grouping in the genus Staphylococcus. Int. J. Syst. Evol. Microbiol. 2007, 57, 25–30. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Z.; Liu, W.; Xu, H.; Aguilar, Z.P.; Shah, N.P.; Wei, H. Propidium monoazide combined with real-time PCR for selective detection of viable Staphylococcus aureus in milk powder and meat products. J. Dairy Sci. 2015, 98, 1625–1633. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Okolie, C.E.; Wooldridge, K.G.; Turner, D.P.J.; Cockayne, A.; James, R. Development of a heptaplex PCR assay for identification of Staphylococcus aureus and CoNS with simultaneous detection of virulence and antibiotic resistance genes. BMC Microbiol. 2015, 15, 157. [Google Scholar] [CrossRef] [PubMed]
- Shang, Y.; Ye, Q.; Cai, S.; Wu, Q.; Pang, R.; Yang, S.; Xiang, X.; Wang, C.; Zha, F.; Ding, Y.; et al. Loop-mediated isothermal amplification (LAMP) for rapid detection of Salmonella in foods based on new molecular targets. LWT 2021, 142, 110999. [Google Scholar] [CrossRef]
- Eren, A.M.; Esen, O.C.; Quince, C.; Vineis, J.H.; Morrison, H.G.; Sogin, M.L.; Delmont, T.O. Anvi’o: An advanced analysis and visualization platformfor ’omics data. PeerJ 2015, 3, e1319. [Google Scholar] [CrossRef]
- Pritchard, L.; Glover, R.H.; Humphris, S.; Elphinstone, J.G.; Toth, I.K. Genomics and taxonomy in diagnostics for food security: Soft-rotting enterobacterial plant pathogens. Anal. Methods 2016, 8, 12–24. [Google Scholar] [CrossRef]
- Chaudhari, N.M.; Gupta, V.K.; Dutta, C. BPGA-an ultra-fast pan-genome analysis pipeline. Sci. Rep. 2016, 6, 24373. [Google Scholar] [CrossRef] [Green Version]
- Ye, J.; Coulouris, G.; Zaretskaya, I.; Cutcutache, I.; Rozen, S.; Madden, T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 2012, 13, 134. [Google Scholar] [CrossRef] [Green Version]
- Tran, P.N.; Savka, M.A.; Gan, H.M. In-silico taxonomic classification of 373 genomes reveals species misidentification and new genospecies within the genus Pseudomonas. Front. Microbiol. 2017, 8, 1296. [Google Scholar] [CrossRef]
- Wuyts, S.; Wittouck, S.; De Boeck, I.; Allonsius, C.N.; Pasolli, E.; Segata, N.; Lebeer, S. Large-Scale Phylogenomics of the Lactobacillus casei Group Highlights Taxonomic Inconsistencies and Reveals Novel Clade-Associated Features. mSystems 2017, 2, e00061-17. [Google Scholar] [CrossRef] [Green Version]
- Irlinger, F. Safety assessment of dairy microorganisms: Coagulase-negative staphylococci. Int. J. Food Microbiol. 2008, 126, 302–310. [Google Scholar] [CrossRef]
- Broeders, S.; Huber, I.; Grohmann, L.; Berben, G.; Taverniers, I.; Mazzara, M.; Roosens, N.; Morisset, D. Guidelines for validation of qualitative real-time PCR methods. Trends Food Sci. Technol. 2014, 37, 115–126. [Google Scholar] [CrossRef]
- Singh, P.; Mustapha, A. Multiplex real-time PCR assays for detection of eight Shiga toxin-producing Escherichia coli in food samples by melting curve analysis. Int. J. Food Microbiol. 2015, 215, 101–108. [Google Scholar] [CrossRef]
- Sah, S.; Bordoloi, P.; Vijaya, D.; Amarnath, S.K.; Sheela Devi, C.; Indumathi, V.A.; Prashanth, K. Simple and economical method for identification and speciation of Staphylococcus epidermidis and other coagulase negative Staphylococci and its validation by molecular methods. J. Microbiol. Methods 2018, 149, 106–119. [Google Scholar] [CrossRef] [PubMed]
- Marino, M.; Frigo, F.; Bartolomeoli, I.; Maifreni, M. Safety-related properties of staphylococci isolated from food and food environments. J. Appl. Microbiol. 2011, 110, 550–561. [Google Scholar] [CrossRef] [PubMed]
- Ghebremedhin, B.; Layer, F.; König, W.; König, B. Genetic classification and distinguishing of Staphylococcus species based on different partial gap, 16S rRNA, hsp60, rpoB, sodA, and tuf gene sequences. J. Clin. Microbiol. 2008, 46, 1019–1025. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, F.; Ye, Q.; Chen, M.; Zhang, J.; Xue, L.; Wang, J.; Wu, S.; Zeng, H.; Gu, Q.; Zhang, Y.; et al. Multiplex PCR for the Identification of Pathogenic Listeria in Flammulina velutipes Plant Based on Novel Specific Targets Revealed by Pan-Genome Analysis. Front. Microbiol. 2021, 11, 634225. [Google Scholar] [CrossRef]
- Li, F.; Ye, Q.; Chen, M.; Zhou, B.; Xiang, X.; Wang, C.; Shang, Y.; Zhang, J.; Pang, R.; Wang, J.; et al. Mining of novel target genes through pan-genome analysis for multiplex PCR differentiation of the major Listeria monocytogenes serotypes. Int. J. Food Microbiol. 2021, 339, 109026. [Google Scholar] [CrossRef] [PubMed]
- Xiong, D.; Song, L.; Pan, Z.; Jiao, X. Identification and Discrimination of Salmonella enterica Serovar Gallinarum Biovars Pullorum and Gallinarum Based on a One-Step Multiplex PCR Assay. Front. Microbiol. 2018, 9, 1718. [Google Scholar] [CrossRef] [Green Version]
- Ma, B.; Li, J.; Chen, K.; Yu, X.; Sun, C.; Zhang, M. Multiplex recombinase polymerase amplification assay for the simultaneous detection of three foodborne pathogens in seafood. Foods 2020, 9, 278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kilic, A.; Muldrew, K.L.; Tang, Y.W.; Basustaoglu, A.C. Triplex real-time polymerase chain reaction assay for simultaneous detection of Staphylococcus aureus and coagulase-negative staphylococci and determination of methicillin resistance directly from positive blood culture bottles. Diagn. Microbiol. Infect. Dis. 2010, 66, 349–355. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Pan-genome analysis generated with Anvi’o software (version 7.0) for 155 staphylococci genomes. The layers represent individual staphylococci genomes organized by their phylogenomic relationship. In the layers, the dark and bright areas within the bars indicate the presence and absence of genes, respectively. The ANI values are represented by a heatmap determined at high (black) and low (gray) similarities.
Figure 1.
Pan-genome analysis generated with Anvi’o software (version 7.0) for 155 staphylococci genomes. The layers represent individual staphylococci genomes organized by their phylogenomic relationship. In the layers, the dark and bright areas within the bars indicate the presence and absence of genes, respectively. The ANI values are represented by a heatmap determined at high (black) and low (gray) similarities.
Figure 2.
Specificity of the primer pairs for real-time PCR amplification. (A) S. aureus ATCC 29213, KCTC 1928, NCCP 14560, ATCC25923, ATCC 29737, and ATCC 6538 amplified using the S. aureus primer pair; (B) S. capitis NCCP 14663 and KACC 13242 amplified using the S. capitis primer pair; (C) S. caprae KCTC 3583 and NCCP 15629 amplified using the S. caprae primer pair; (D) S. epidermidis NCCP 14723 and KACC 13234 amplified using the S. epidermidis primer pair.
Figure 2.
Specificity of the primer pairs for real-time PCR amplification. (A) S. aureus ATCC 29213, KCTC 1928, NCCP 14560, ATCC25923, ATCC 29737, and ATCC 6538 amplified using the S. aureus primer pair; (B) S. capitis NCCP 14663 and KACC 13242 amplified using the S. capitis primer pair; (C) S. caprae KCTC 3583 and NCCP 15629 amplified using the S. caprae primer pair; (D) S. epidermidis NCCP 14723 and KACC 13234 amplified using the S. epidermidis primer pair.
Figure 3.
Standard curves by plotting cycle threshold (Ct) values against the logarithm of the number of cells of (A) S. aureus, (B) S. capitis, (C) S. caprae, and (D) S. epidermidis in pure culture.
Figure 3.
Standard curves by plotting cycle threshold (Ct) values against the logarithm of the number of cells of (A) S. aureus, (B) S. capitis, (C) S. caprae, and (D) S. epidermidis in pure culture.
Figure 4.
Limit of detection for (A) S. aureus, (B) S. capitis, (C) S. caprae, and (D) S. epidermidis in spiked milk samples.
Figure 4.
Limit of detection for (A) S. aureus, (B) S. capitis, (C) S. caprae, and (D) S. epidermidis in spiked milk samples.
Table 1.
List of reference strains used in this study.
| Classification | Species | Strain Number |
|---|---|---|
| Target Staphylococcus | Staphylococcus aureus | ATCC 29213 |
| Staphylococcus aureus | KCTC 1928 | |
| Staphylococcus aureus | NCCP 14560 | |
| Staphylococcus aureus | ATCC 25923 | |
| Staphylococcus aureus | ATCC 29737 | |
| Staphylococcus aureus subsp. aureus | ATCC 6538 | |
| Staphylococcus capitis | NCCP 14663 | |
| Staphylococcus capitis subsp. capitis | KACC 13242 | |
| Staphylococcus caprae | KCTC 3583 | |
| Staphylococcus caprae | NCCP 15629 | |
| Staphylococcus epidermidis | NCCP 14723 | |
| Staphylococcus epidermidis | KACC 13234 | |
| Non-target Staphylococcus | Staphylococcus auricularis | KACC 13252 |
| Staphylococcus carnosus subsp. utilis | KACC 13190 | |
| Staphylococcus cohnii subsp. cohnii | KACC 13237 | |
| Staphylococcus cohnii subsp. urealyticus | KCTC 3574 | |
| Staphylococcus delphini | KACC 13258 | |
| Staphylococcus equorum subsp. equorum | KACC 13255 | |
| Staphylococcus fleurettii | KACC 13199 | |
| Staphylococcus gallinarum | KACC 13253 | |
| Staphylococcus haemolyticus | NCCP 14691 | |
| Staphylococcus hominis | NCCP 10748 | |
| Staphylococcus hominis | KACC 13409 | |
| Staphylococcus kloosii | KACC 13256 | |
| Staphylococcus lentus | KCCM 41469 | |
| Staphylococcus lugdunensis | NCCP 15630 | |
| Staphylococcus lugdunensis | KACC 11270 | |
| Staphylococcus pasteuri | KCTC 13167 | |
| Staphylococcus pettenkoferi | DSM 19554 | |
| Staphylococcus saprophyticus | NCCP 14670 | |
| Staphylococcus saprophyticus | KCTC 3345 | |
| Staphylococcus saprophyticus | KACC 15799 | |
| Staphylococcus schleiferi subsp. coagulans | KCCM 41634 | |
| Staphylococcus sciuri subsp. rodentium | KACC 13217 | |
| Staphylococcus sciuri subsp. sciuri | KCCM 41468 | |
| Staphylococcus warneri | KCTC 3340 | |
| Staphylococcus warneri | KACC 10785 | |
| Staphylococcus xylosus | NCCP 10937 | |
| Staphylococcus xylosus | KACC 13239 | |
| Non-Staphylococcus | Bacillus cereus | KCTC 3624 |
| Bacillus cereus | KCTC 1661 | |
| Bacillus cereus | KCCM 1173 | |
| Bacillus cereus | KCCM 1174 | |
| Bacillus cereus | KCCM 40133 | |
| Bacillus cereus | ATCC 11778 | |
| Bacillus cereus | ATCC 10876 | |
| Bacillus cereus | ATCC 14579 | |
| Bacillus circulans | KCTC 3347 | |
| Bacillus licheniformis | KCTC 1026 | |
| Bacillus megaterium | KCTC 3007 | |
| Bacillus subtilis | KCTC 3725 | |
| Clostridium perfringens | ATCC 14810 | |
| Enterococcus avium | KACC 10788 | |
| Enterococcus casseliflavus | KCTC 3552 | |
| Enterococcus cecorum | KACC 13884 | |
| Enterococcus durans | KCTC 13289 | |
| Enterococcus faecalis | KCTC 5290 | |
| Enterococcus faecalis | KACC 11859 | |
| Enterococcus faecalis | KCTC 3206 | |
| Enterococcus faecium | KACC 15681 | |
| Enterococcus faecium | KACC 11954 | |
| Enterococcus faecium | KCTC 13225 | |
| Enterococcus faecium | KACC 14552 | |
| Enterococcus gallinarum | NCCP 11518 | |
| Enterococcus gilvus | KACC 13847 | |
| Enterococcus hirae | KACC 16328 | |
| Enterococcus hirae | KACC 10782 | |
| Enterococcus hirae | KACC 10779 | |
| Enterococcus malodoratus | KACC 13883 | |
| Enterococcus mundtii | KCTC 3630 | |
| Enterococcus raffinosus | KACC 13782 | |
| Enterococcus saccharolyticus | KACC 10783 | |
| Enterococcus thailandicus | KCTC 13134 | |
| Escherichia coli | KCTC 1682 | |
| Escherichia coli | ATCC 25922 | |
| Escherichia coli | ATCC 23763 | |
| Escherichia coli | ATCC 35150 | |
| Escherichia coli | ATCC 43890 | |
| Enteroaggregative Escherichia coli | NCCP 14039 | |
| Enterohemorrhagic Escherichia coli | NCCP 11076 | |
| Enteroinvasive Escherichia coli | NCCP 15663 | |
| Enteropathogenic Escherichia coli | NCCP 13715 | |
| Enterotoxigenic Escherichia coli | NCCP 15732 | |
| Listeria ivanovii | ATCC 19119 | |
| Listeria monocytogenes | ATCC 19115 | |
| Listeria monocytogenes | KCTC 3569 | |
| Proteus mirabilis | KCTC 2566 | |
| Proteus vulgaris | KCTC 2579 | |
| Pseudomonas aeruginosa | KCTC 1636 | |
| Pseudomonas chlororaphis | KCCM 41854 | |
| Pseudomonas oryzihabitans | KCCM 42984 | |
| Salmonella bongori | ATCC 43975 | |
| Salmonella enterica subsp. arizonae | ATCC 13314 | |
| Salmonella enterica subsp. diarizonae | ATCC 43973 | |
| Salmonella enterica subsp. enterica | ATCC 19585 | |
| Salmonella Choleraesuis | ATCC 13312 | |
| Salmonella Gallinarum | ATCC 9120 | |
| Salmonella Paratyphi B | ATCC 10719 | |
| Salmonella Paratyphi C | ATCC 13428 | |
| Salmonella Typhimurium | ATCC 14028 | |
| Salmonella enterica subsp. houtenae | ATCC 43974 | |
| Salmonella enterica subsp. indica | ATCC 43976 | |
| Salmonella enterica subsp. salamae | ATCC 15793 | |
| Shigella dysenteriae | ATCC 13313 | |
| Shigella sonnei | KCTC 2518 | |
| Vibrio cholerae | NCCP 13589 | |
| Vibrio cholerae | ATCC 14033 | |
| Vibrio cholerae | ATCC 14035 | |
| Vibrio parahaemolyticus | ATCC 17802 | |
| Vibrio parahaemolyticus | KCCM 41664 | |
| Vibrio parahaemolyticus | ATCC 27969 | |
| Vibrio vulnificus | ATCC 33814 |
Table 2.
Specific primer information.
| Species | Primer | Sequence | Size (bp) |
|---|---|---|---|
| S. aureus | Aureus_F | CAA GCA CAA GGC AGT GGT AT | 145 |
| Aureus_R | GTG GCG TTG CAA TCT CCT TA | ||
| S. capitis | Capitis_F | CGC AAG GTG GTC AAC TTG AT | 150 |
| Capitis_R | GCG CAT CGT GAA GTA ATT CC | ||
| S. caprae | Caprae_F | TCG TCG CAA CGA AGT TCA TC | 146 |
| Caprae_R | CCT GGC GCA TAT GTA TGC TT | ||
| S. epidermidis | Epidermidis_F | TGG CAC GGC TGG TAT TAG AG | 121 |
| Epidermidis_R | GAC AGG ATG CGC GAT ACT TG |
Table 3.
Identification of strains isolated from food samples.
| Sample Type | No. of Isolates | No. of Positive Results by Real-Time PCR | |||
|---|---|---|---|---|---|
| S. Aureus | S. Capitis | S. Caprae | S. Epidermidis | ||
| chicken (n = 4) | 12 | 12 | 0 | 0 | 0 |
| beef (n = 3) | 11 | 11 | 0 | 0 | 0 |
| pork (n = 5) | 38 | 7 | 0 | 0 | 31 |
| fish (n = 3) | 6 | 6 | 0 | 0 | 0 |
| salted fish (n = 5) | 22 | 0 | 4 | 0 | 18 |
| raw milk (n = 2) | 14 | 0 | 0 | 0 | 14 |
| Total (n = 22) | 103 | 36 | 4 | 0 | 63 |
Table 4.
Identification of Staphylococcus contamination in food samples using the real-time PCR method developed in this study.
Table 4.
Identification of Staphylococcus contamination in food samples using the real-time PCR method developed in this study.
| Sample Type | No. of Samples | No. of Positive Results by Real-Time PCR | |||
|---|---|---|---|---|---|
| S. Aureus | S. Capitis | S. Caprae | S. Epidermidis | ||
| beef | 2 | 0 | 0 | 0 | 0 |
| pork | 8 | 1 | 0 | 0 | 0 |
| lettuce | 1 | 0 | 0 | 0 | 0 |
| cucumber | 1 | 0 | 0 | 0 | 0 |
| raw milk | 34 | 10 | 2 | 0 | 9 |
| fermented fish | 4 | 0 | 1 | 0 | 0 |
| Total | 50 | 11 | 3 | 0 | 9 |
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Kim, E.; Yang, S.-M.; Won, J.-E.; Kim, D.-Y.; Kim, D.-S.; Kim, H.-Y. Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes. Foods 2021, 10, 2839. https://doi.org/10.3390/foods10112839
AMA Style
Kim E, Yang S-M, Won J-E, Kim D-Y, Kim D-S, Kim H-Y. Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes. Foods. 2021; 10(11):2839. https://doi.org/10.3390/foods10112839
Chicago/Turabian StyleKim, Eiseul, Seung-Min Yang, Ji-Eun Won, Da-Young Kim, Da-Som Kim, and Hae-Yeong Kim. 2021. "Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes" Foods 10, no. 11: 2839. https://doi.org/10.3390/foods10112839
APA StyleKim, E., Yang, S. -M., Won, J. -E., Kim, D. -Y., Kim, D. -S., & Kim, H. -Y. (2021). Real-Time PCR Method for the Rapid Detection and Quantification of Pathogenic Staphylococcus Species Based on Novel Molecular Target Genes. Foods, 10(11), 2839. https://doi.org/10.3390/foods10112839
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