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Article

Genome-Wide Searching Single Nucleotide-Polymorphisms (SNPs) and SNPs-Targeting a Multiplex Primer for Identification of Common Salmonella Serotypes

by
Md-Mafizur Rahman
1,2,
Sang-Jin Lim
3,* and
Yung-Chul Park
1,*
1
Division of Forest Science, Kangwon National University, Chuncheon 24341, Korea
2
Department of Biotechnology and Genetic Engineering, Faculty of Biological Science, Islamic University, Kushtia 7001, Bangladesh
3
Institute of Forest Science, Kangwon National University, Chuncheon 24341, Korea
*
Authors to whom correspondence should be addressed.
Pathogens 2022, 11(10), 1075; https://doi.org/10.3390/pathogens11101075
Submission received: 16 July 2022 / Revised: 15 September 2022 / Accepted: 16 September 2022 / Published: 21 September 2022
(This article belongs to the Special Issue Bacterial, Fungal and Parasitic Zoonoses)
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Abstract

:
A rapid and high-quality single-nucleotide polymorphisms (SNPs)-based method was developed to improve detection and reduce salmonellosis burden. In this study, whole-genome sequence (WGS) was used to investigate SNPs, the most common genetic marker for identifying bacteria. SNP-sites encompassing 15 sets of primers (666–863 bp) were selected and used to amplify the target Salmonella serovar strains, and the amplified products were sequenced. The prevalent Salmonella enterica subspecies enterica serovars, including Typhimurium; Enteritidis, Agona, enterica, Typhi, and Abony, were amplified and sequenced. The amplified sequences of six Salmonella serovars with 15 sets of SNP-sites encompassing primers were aligned, explored SNPs, and SNPs-carrying primers (23 sets) were designed to develop a multiplex PCR marker (m-PCR). Each primer exists in at least two SNPs bases at the 3′ end of each primer, such as one was wild, and another was a mismatched base by transition or transversion mutation. Thus, twenty-three sets of SNP primers (242–670 bp), including 13 genes (SBG, dedA, yacG, mrcB, mesJ, metN, rihA/B, modA, hutG, yehX, ybiY, moeB, and sopA), were developed for PCR confirmation of target Salmonella serovar strains. Finally, the SNPs in four genes, including fliA gene (S. Enteritidis), modA (S. Agona and S. enterica), sopA (S. Abony), and mrcB (S. Typhimurium and S. Typhi), were used for detection markers of six target Salmonella serotypes. We developed an m-PCR primer set in which Salmonella serovars were detected in a single reaction. Nevertheless, m-PCR was validated with 21 Salmonella isolates (at least one isolate was taken from one positive animal fecal, and n = 6 reference Salmonella strains) and non-Salmonella bacteria isolates. The SNP-based m-PCR method would identify prevalent Salmonella serotypes, minimize the infection, and control outbreaks.

1. Introduction

Single nucleotide polymorphisms (SNPs), a common allelic variation in all organisms, exists in whole-genome sequences of Salmonella. Identification of SNPs by comparing sequence data from target Salmonella serovars with a reference genome sequence and varying nucleotides are created an SNP matrix [1,2]. SNPs are highly informative and stable markers that can be used to efficiently detect, investigate outbreaks, and reveal evolutionary analysis of similar bacterial groups [1,2]. The advent of whole-genome sequence (WGS) has improved the ability to investigate outbreaks by exploiting SNPs that may vary among isolates [3].
Salmonella enterica serovars Typhi (typhoidal) are restricted to humans, whereas nontyphoidal Salmonella (NTS) serovars are generalist pathogens with different hosts (wild and domestic animals), which express as asymptomatic carriers [4]. NTS Salmonella enterica, i.e., Enteritidis, and Typhimurium are the two major zoonotic serovars that cause illness, including diarrhea, gastroenteritis, septicemia, and other clinical syndromes [5,6]. S. Typhimurium is a broad host range of serovar, including wild and domestic animals, which are recognized as a leading NTS infectious agent (approximately a quarter of infections out of total NTS infections), and S. Enteritidis is recognized as the second infectious agent [7]. S. Agona infections commonly occur in humans by consuming contaminated animal food [8,9]. The ultimate consequence is that two billion humans are annually suffering from Salmonella gastroenteritis, leading to over 3 million deaths globally [10]. The prevalent Salmonella is widely found in diverse sources, including environment, animal-originated foods, water, and animals, specifically wild animals, including wild birds [11,12,13], wild pigs [14], poultry and their eggs [15], and cattle [16]. It can be disseminated to humans via ingestion of contaminated food of animal origins, including pork, chicken, eggs, beef, and milk [17,18,19].
Serotyping methods can be used for the characterization of over 2800 distinct serovars of Salmonella enterica, which are costly, time-consuming, labor-intensive, and insensitive [20]. So, a rapid and accurate detection method is essential for identifying prevalent and high-risk Salmonella serovars. In addition, serotype information is needed for animal-originated foods safety and the public health burden of salmonellosis. Nevertheless, serotypes may provide important epidemiological data, as well as specific virulence characteristics with specific contamination sources [20,21]. Therefore, public health and regulatory agencies need a rapid, highly accurate, and discriminatory SNP-based method to detect serotypes and outbreaks, and to link illness cases to the incidence investigations [21,22,23]. Thus, to achieve serovar-specific detection of six Salmonella enterica serovar strains in a single multiplex PCR amplification, we implement the SNP-existing genes of WGS Salmonella. Nevertheless, several researchers have utilized mutation sites in different genes to provide a molecular target to establish a method due to its excellent specificity [24,25,26,27].
The current research aims to identify novel sensitive, and reliable serovar-specific targets and develop an m-PCR method for Salmonella serovars to facilitate timely prevention and treatment. Several developed detection methods are conducted for Salmonella serovars Enteritidis [28], and Typhimurium [29], since they top the list of the most prevalent serotypes [30,31,32]. S. Enteritidis is the largest (between 40% and 60% of human illness) pathogens of Salmonella infection and disease outbreaks in humans globally [30,33,34]. However, a few SNP-based molecular identification studies are conducted on multiple serovars of Salmonella in a single PCR reaction [35]. In research, a Salmonella-serovar-specific multiplex marker was developed using SNPs in gene fragments (flagellin gene, fljB, DNA gyrase, gyrB, and putative stress regulatory gene, ycfQ) and evaluated for serotype-specific subtyping of Salmonella enterica isolates. So far, to our knowledge, a few studies with SNP-based multiplex PCR markers were developed, and detection in which widely prevalent S. enterica serovars (typhoidal and non-typhoidal) were detected in a single reaction. Therefore, we conducted a molecular study of six Salmonella enterica subsp. enterica serovars (Typhimurium, Typhi, Enteritidis, S. Abony, Agona, and S. enterica) identification with a developed m-PCR marker.

2. Results

2.1. Acquired Salmonella WGS from GenBank

Three Salmonella bongori WGS sequences were downloaded and aligned for investigating SNP sites. Among the three, we regarded one reference (NC-015761) and two comparing strains (NC_021870, NZ_CP006692). The accession number, SNP positions, length of reference WGS, and compared S. bongori strains are provided in Table 1 and Table S1.

2.2. Searching SNP Sites from NGS of Salmonella Genome Alignment and Design Primers Based on SNP Sites

The Salmonella genome sequences were obtained from NCBI and compared to the reference sequences. We found 140 SNPs on the alignment of S. bongori serovar genomes (Table S1). Within the SNPs, we detected a number of SNPs (functional, high-quality SNPs based on nonsynonymous and synonymous mutation) on the aligned WGS of Salmonella using Bioinformatics software. We thus selected 15 sets of SNP sites surrounding primers based on 13 genes of the Salmonella genome (Table 1). Detailed information on SNPs with the position of ambiguous codes and amino acids in the respective genes of a reference Salmonella strain (NCTC 12419) is provided in Table 1.

2.3. Amplified Target Salmonella Serovars with Newly Designed Encompassing-Primer Sets

We developed 15 sets of SNP markers (666–863 bp) from the NGS data analysis of Salmonella genomes (Table 1). The amplified PCR products with PCR results (amplicon length) are marked in Figure S1, which describes the amplification of PCR with 15 primer pairs. Among 15 primers, the six primers (09-, 12-, 15-, 16-, 18-, and 24-Sbon) were amplified with desire genes of target Salmonella, whereas six primers (04-, 06-, 11-, 14-, 19-, 21-, 22-Sbon) were not amplified with desire genes of Salmonella (Figure S1 and Table S2). Five primer pairs (1-, 9-, 13-, 14- and 24-Sbon) were amplified in the first PCR, and seven primer sets (11-, 12-, 15-, 16-, 18-, 19-, 21-Sbon) were amplified in the second PCR amplification. However, the three (4-, 6, and 22-Sbon) primer sets have not produced any band in both amplification (first and second PCR) (Figure S1 and Table S3).

2.4. Justification of SNP Sites on Target Salmonella Gene Sequences and Design with Serotype-Specific SNP Primers

Among the 15 primers, 12 primers were amplified at the target band properly during the first (five primers) and second PCR (seven primers), and the rest of the three primers failed to produce any band (Table S2). For instance, the forward primer (12-Sbon-F): 5′-ATTGGCACGCTGTCAGCT-3′ and the reverse primer (12-Sbn-R): 5′-TGCCGGTAAAAGCACGCT-3′ were used to amplify the target band (681 bp) and desired SNP positions of reference Salmonella strain (270836: G of S. bongori, NC-015761, red color “G’ indicates a SNP) of the methionine import ATP-binding protein (metN) gene (Table 1). First, the amplified metN gene products of desired six Salmonella serotypes were sequenced, and the amplified sequences were aligned. Then we checked SNP positions on the aligned gene sequences. Based on SNPs on the aligned gene sequences, 23 Salmonella serotype-specific SNP primers were designed from 13 genes where at least one wild SNP (Tables S3 and S4).

2.5. Salmonella Serotype-Specific-SNP Primers Design Based on the Appropriate SNP Sites on the Aligned Gene Sequences

The designed 23 sets of SNP-based primers (242–670 bp) were created for confirmation by amplifying the desired Salmonella serovars (Figure 1 and Table S4). One example of the design of the SNP-based primers is shown in Figure S2. SNP-encompassing primer pair ‘16-Sbon’; the forward primer was 5′-ACGGTCTGGGTGAGGTGT-3′ and reverse primer 5′-CCACCGCATCAGAACCGT C-3′. The amplified products with a marker ‘16-Sbon’-amplified modA gene of target Salmonella were aligned. We observed a few SNP nucleotides on the aligned gene sequences. Based on the SNP sites, five primer sets were developed on the amplified gene (modA) sequences (Figure S2 and Table S4). We thus developed the SNP-based marker ‘ModA-1-F/R’, 24-mer forward 5′-ACCCCTGAGATTATCGTTATACTG-3′ and 19-mer reverse primer 5′-ATCGCCCACTGCCAGATGT-3′. In the designed primer, we considered at least two SNPs e.g., forward primer ranges from 130 to 153 (the position of wild SNP ‘G = 153′ and transition mutated SNP site “T = 151; C > T”), and reverse primer ranges from 598 to 616 (the position of wild SNP ‘T = 598’ and the transition mutated SNP site was “C = 600; T > C”). The target amplified product size was 490 bp (Table S4). A square shape marks the wild SNP and a square shape with black shaded marks the incorporated SNP (Figure S2). Thus, 23 SNP-based markers of Salmonella serotype-specific primers were used to amplify the target six Salmonella serotype strains. Finally, we confirm the performance of the developed SNP marker with the desired band of target Salmonella. The detailed information of six target Salmonella gene sequences, their alignment pattern, and designed SNP-based 23 primers were provided in Supplementary Tables S3 and S4.

2.6. SNP-Based Multiplex PCR

The amplification with 23 SNP-based markers with target 6 Salmonella serovars is time-consuming. Each marker was amplified with all target Salmonella strains, a limitation of the developed assay. However, it overcame the expenditure and time for repeated PCR amplification of the SNP-based triplex-marker assay (S1 and S2) (Figure 2 and Table 2). Therefore, we developed a Salmonella serotype-specific detection primer set (m-PCR) in a single reaction. In addition, the three primer pairs (SBG-2, ModA-3, SBF-(2)-6) amplified fragment sizes were approximately 498, 373, and 300 bp against S. Enteritidis, S. Agona, and S. Abony, respectively (S1). On the contrary, the three primer pairs (mrcB-1-4, ModA-4, and mrcB-5) amplified fragment sizes were approximately 363, 242, and 637 bp for S. Typhimurium, S. enterica, and S. Typhi, respectively (Figure 2 and Table 2).

2.7. Validation of SNP-Based Multiplex Marker with Isolated Salmonella Strains from Wild Animal

For the efficiency test, 21 Salmonella were tested with m-PCR, but only 8 Salmonella isolates from wild animal feces were identified and evaluated with SNP-m-PCR. The remaining Salmonella strains were not identified with m-PCR because these Salmonellae were not included with the target six Salmonella serotypes. Figure 3 depicts the multiplex PCR marker (S1) generating band drawn to the isolate’s lane no. from 1 to 9, whereas the m-PCR (S2) generating band drawn to the isolate’s lane no. from 10 to 16. The 5 Salmonella isolates from leopard cat (Prionailurus bengalensis) fecal samples were observed in lanes 5 to 9. These five isolates were well-matched (target band 300 bp) to reference Salmonella Abony (BA1800061). Moreover, the Salmonella isolates in lanes no.14 and 15 were detected from P. bengalensis. The only isolate in lane no.16 was detected from magpie bird (Pica sericea) which was well-matched (242 bp) to Salmonella enterica subsp. enterica NCCP-15756 strain (Figure 3).

3. Discussion

Salmonella, especially NTS Salmonella Typhimurium, Enteritidis are the most prevalent and common serovars [36] for the gastrointestinal disorders resulting from cross-contamination of wild and domestic animal feces [4,37], consumption of animal-originated foods, fresh agricultural produces, i.e., raw fruits and vegetables [38]. In the context of public health, detection of a foodborne outbreak source is essential to remove carrier food items from the market. Molecular investigation and typing of Salmonella, the SNP-based typing techniques are essential for identifying the source of a foodborne outbreak. However, Salmonella serovars identification methods are based on mainly three basic mechanisms, including restriction fragment analysis of Salmonella DNA, PCR amplification of target genes, and SNP-based identification at specific loci in the whole genome sequence [6,20,28]. To date, several molecular approaches have been applied to detect Salmonellae, such as PFGE [39], phage typing [40], and multilocus sequence typing (MLST) [41], but they have some limitations. However, SNP-based molecular techniques have recently been proposed as a cost-effective identification method of various bacterial species, including, Salmonella [42], E. coli [43]; Mycobacterium [44]. The SNPs have discrimination power for comparing of bacterial subspecies and at the serovar level using different bioinformatics software [45]. Thus, SNPs could be used as an alternative method to detect outbreaks [2], surveillance of foodborne pathogenic Salmonella [46,47], determine models for future outbreaks, and even build an evolutionary and phylogenetic relationship within similar bacterial strains [48,49].
In this study, we searched high-quality SNPs in the coding regions of a respective WGS by comparing each other (reference and compared strains). The quality filtered nucleotide matrix is generated (Table S1). Furthermore, in this study, 15 primer sets were selected from 13 genes of the Salmonella genome-wide searching based on the encompassing SNP sites (Table 1). Primers were designed on the aligned WGS of 13 genes mentioned above (appropriate SNP sites on the aligned genes), approximately 666–863 bp (Table 1). However, only 12 primer sets were amplified with the first and second single-plex PCR, and the rest of the three (4-, 6, and 22-Sbon) primer sets could not produce any band during both PCR cycles (Figure S1). The amplified PCR products were sequenced and aligned for searching suitable SNPs to make serotype-specific primer sets (wild and altered bases of 3′ end of each primer, thus, 23 sets were selected based on aligned sequences). Finally, SNP-containing 6 primers sets from four genes (fliA, modA, sopA, and mrcB) were selected for the widely found prevalent six Salmonella serotypes detection in an SNP-based m-PCR marker (Table 2). A study used serotype-specific SNPs to identify five Salmonella serotypes [42]. Guard et al. [50] postulated that the allele-specific primer was developed based on > 80 SNPs in an adenylate cyclase gene (cyaA) of Salmonella for the detection of S. enterica serovar strains [50]. Roumagnac et al., 2006 conducted a study of approximately 82 SNPs were detected in the partial gene sequences (n = 99) of worldwide (n = 105) Salmonella Typhi isolate, and these SNPs data were used for resolving clear identification of Typhi isolates [51].
This study developed SNP-based primers based on SNPs (wild or mutated transition or transversion) at the 3′ end aligned sequence sites. The introduction of altered bases (transition or transversion) at the end (generally 3′ end) of each primer (except reverse primer SBG-2R) might have changed the codon, which was ultimately used as a target for PCR primer [52,53,54]. This introduction of a mismatched transversion (A-T), or transition (A-G) base pair at the 3′ end sequence could enhance the specific amplification during PCR [55,56]. Thus, transversions (G-C, G-T, A-T, A-C) and transition (A-G, T-C) mutations are required to improve the allele-specific amplification. Based on altered bases (transition and transversions), we developed SNP primers (23 sets, 242–670 bp) for evaluation by amplifying the desired 6 Salmonella serovar strains (Figure 1 and Table S4). In addition, the melting temperature (Tm) generally depends on the GC content of the primer sequences, which is required for PCR conditions adjustment. By incorporating altered bases, the melting temperature of allele-specific SNP-based primers can be fixed to PCR conditions [57,58]. Similarly, the Salmonella detected primers were designed based on wild and an altered nucleotide at the 3′ end SNP sites (generally within the three bases) (Table 2 and Figure S2). SNP-based PCR marker was developed for the identification of desire Salmonella in a single reaction. An efficient test was conducted with the desired Salmonella serovars by PCR amplification of multiplex PCR marker and adjusted to PCR conditions (Table S5). In a study, the SNP-based phylogenetic analysis of S. Enteritidis whole-genome proved that these most prevalent serotypes were clustered in the same lineage, which evolved from the poultry flocs in Brazil [59].
Recently, software algorithms have been used to explore SNP positioning from assembled or raw genome sequences [60]. These new techniques have become increasingly popular for the detection of Salmonella compared to other methods. In several investigations, the new technique (SNP-based) has already been applicable in retrospective research studies [52,61,62]. In a study, ten target genes were used to analyze SNPs with common Salmonella serovars (Enteritidis, Typhimurium, and Heidelberg). They observed the forty-five nonsynonymous mutations and two most common transition mutations (T ↔ C and A ↔ G), which existed in all Salmonella isolates [63]. Similarly, we used 13 genes with two nonsynonymous mutations encompassing primers to sequence all six targets of Salmonella serovars for searching SNPs and develop an SNP-based m-PCR marker. Moreover, the most common transition mutations (T ↔ C and A ↔ G) were observed in this study (Table 1 and Figure S2).
Den et al. showed that SNP in WGS is a robust technique compared to multiple-locus variable-number tandem-repeat (VNTR) analysis (MLVA) and pulsed-field gel electrophoresis (PFGE) [64]. In a research, the serovar of Salmonella Pullorum was detected by serotype-specific PCR of target gene rfbS (paratose synthetase), where a polymorphic site exists at the position of 237, and this SNP-based gene was used to detect and discriminate efficiently between the Pullorum and non-Pullorum [56,58]. Moreover, an allele-specific detection of S. Enteritidis was possible based on the SNP site (at position 272 in the plasmid virulence spvA gene of Salmonella) [65]. Similarly, serotype-specific PCR amplification of four genes (fliA; modA, sopA, and mrcB) was used to identify of target six Salmonella serotypes, including Enteritidis, Agona, S. enetrica, Abony, Typhi, and Typhimurium, respectively, in our study (Table 2). In addition, we observed 870 and 140 SNPs on the whole genome of aligned S. enterica and S. bogori, respectively (Table S1). SNP encompassing regions of aligned 13 gene sequences were selected for further sequencing with target six Salmonella, and SNPs-containing four genes (fliA; modA gene, sopA, and mrcB) were validated for the SNP-based multiplex PCR marker. However, PCR enzymes are capable of proofreading activity to correct the mismatch bases [66], but DNA polymerase can increase primers less efficiently (100 to 10,000 folds) compared to matched and mismatched bases [67]. Furthermore, simultaneous incorporation of mismatched bases at the 3′end of each primer (except reverse primer SBG-2R) of third position bases prevented the accurate amplification of targeted Salmonella (Table 2). Thus, the detected SNPs could be applied to develop a proper, rapid, alternative, and cost-effective identification method that may contribute to significant improvements in the diagnosis of Salmonella.
Species identification with sequence matched to the NCBI, while only sequence cannot identify the Salmonella serovar strains due to their high similarity. For serovar identification, we need multiplex PCR. However, our limitation was the developed SNP-based m-PCR, which could not detect all identified Salmonella spp. from wild-animal fecal samples beyond the limited serotypes (only six S. enterica subsp. enterica serovars). The study’s developed m-PCR (S1 and S2) only detected the six serovars. Furthermore, the proportion of primers mixture was varied owing to possible interference between primers. The S1 and S2 primer sets were separated in this study because there was a difference in the ratio of primers in the primer mixture.
With the developed m-PCR, we should need to verify a number of Salmonella serovars from different sources, and further studies should be conducted with accurate serovar determination in the future.

4. Materials and Methods

4.1. Acquired Salmonella Whole-Genome Sequences (WGS) from GenBank, Searched SNPs, and Designed SNPs-Encompassing Primers

A total of 13 Salmonella WGS sequence data, 10 S. enterica, and 3 S. bongori strains, were obtained from GenBank (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/; accessed on 1 August 2022), including reference strain, S. bongori NCTC 12419 (NC-015761) and S. enterica. serovar Typhimurium str. LT2 (NC-003197) (Table S1). According to our published article, SNP-based primers and protocols were developed [43]. We used the MUMer package (v3.19, NUCmer algorithm, National Institutes of Health, Bethesda, MD, USA) to investigate the SNP sites on the WGS [61]. SNPs-surrounded primers were developed based on the reference and comparing Salmonella strains (Table 1). These primer sets (n = 15, SNP encompassing forward and reverse primer sets) were selected based on coding gene sequences, mutation pattern, primer length, G: C content, annealing temperature, the position of the SNPs sites, and so on. In addition, each primer (15 primer sets) was amplified with single plex PCR with the reference target of six S. enterica serovars.

4.2. Selection and Isolation of Genomic DNA from Serotype-Specific Target Salmonella Serovar Strains

The target six Salmonella enterica serovar strains were sequenced by using designed encompassing-primers. For sequencing of target S. enterica serovars (n = 6), a single plex reaction (20-μL volume) was conducted with the respective primers sets (10 pmol/μL), 10 ng of genomic DNA, 2 μL 10 × buffer, 2 μL dNTP, 0.5 μL (5 unit/μL) Taq DNA polymerase (Qiagen, Hilden, Germany). The amplification reaction was completed in a Bio-Rad T100 thermal cycler (Hercules, CA, USA) programmed with first and second-time PCR cycles consisting of amplification at 95 °C for 5 min, followed by 30–35 cycles of denaturation for 30 s, annealing at 55 °C for 30 s (first PCR), and annealing at 50 °C for 30 s (second PCR), polymerization at 72 °C for 1 min 30 s, and final elongation at 72 °C for 10 min. After amplification, 5 μL of each PCR product was analyzed on a 1.5% (w/v) agarose gel. The amplified PCR products were purified (Gel & PCR Purification Kit; Biomedic Co., Ltd., Seoul, Korea) and sequenced using a BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA) and ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA, USA).
We carefully checked one or more SNP positions on the multiple aligned gene sequences of each target reference Salmonella (Table S3). The designed primers were amplified efficiently at the target band with first and second PCR cycles. Furthermore, if SNP-based encompassing primers were produced ambiguous, overlapping/non-target peaks, then they were removed for further analysis. Salmonella serovar-specific-SNP markers were designed to discriminate single base changes through experimental optimization [30,32,33]. However, Salmonella serotype-specific SNP primers were designed on the aligned gene sequences where at least one wild-type and one altered SNP were present in each primer set. Furthermore, if SNP-based encompassing primers produced ambiguous, overlap-ping/non-target peaks, they were removed for further analysis. Salmonella serovar-specific-SNP markers were designed to distinguish any base changes (SNP) by the experimental optimization [30,32,33].
All the 13 different genes in a Salmonella genome such as conserved hypothetical protein (SBG); dedA family integral membrane (dedA); conserved hypothetical protein (yacG); penicillin-binding protein (mrcB); tRNA(Ile)-lysidine synthase (mesJ); methionine import ATP-binding protein (metN); pyrimidine-specific ribonucleoside hydrolase (rihA, rihB); molybdate-binding periplasmic protein (modA); formimidoylglutamase (hutG); hypothetical ABC transporter ATP-binding (yehX); pyruvate formate-lyase 3-activating enzyme (ybiY); molybdopterin biosynthesis MoeB protein (moeB); candidate type three secretion system effector protein (sopA) including SNP sites, were amplified using the target six Salmonella serovars. The amplified PCR products of the target Salmonella were re-sequenced and aligned using BioEdit sequence alignment editor, version 7.0.0 (Tom Hall, North Carolina State University, United States). The Salmonella detected primers were designed based on a wild and an altered nucleotide at the 3′ end SNP sites (transition/transversion mutation). Thus, all the designed primers were further analyzed using NetPrimer (https://www.premierbiosoft.com/netprimer/ accessed on 1 August 2022) to choose the best primer pairs.

4.3. Salmonella Serotype-Specific SNP-Based Multiplex-PCR Marker

The SNP-based serotype-specific primers were designed to identify the target Salmonella. However, the amplification of all six target Salmonella serotypes with all 23 SNP-based primers is time-consuming. To overcome the limitation of repeated PCR amplification, we developed an SNP-based multiplex PCR kit to identify target six Salmonella serovars in a single reaction.
Multiplex (S1 and S2) PCR indicates the difference in PCR mixture ratio (mixture of three primers sets), which were conducted with similar PCR conditions (annealing reaction at 60 °C for 30 s, 30 PCR cycles, see below). The adjusted concentration of the primer mixture of S1 (three primer sets, SBG-2F/R, ModA-3-F/R, and SBG(2)-6F/R) was 1:1:1 while the primer mixture of S2 (mrcB-1-F/R, modA-4-F/R, and mrcB-5-F/R) was 1:3:3, respectively. From a mixture of each forward and reverse primer, only 3 μL PCR mixture (10 pmol/μL) was used in a final volume of 20 μL. A multiplex reaction (20-μL volume) was conducted with the respective primer’s mixture 3 μL (10 pmol/μL), 5 ng of genomic DNA (5 ng/ul), 10 μL Hot Start Taq master mix including Hot Start Taq DNA Polymerase, dNTPs, MgCl2, KCl, and stabilizers (Takara Bio Inc., United States), and PCR grade water 6 μL. The multiplex PCR reaction was conducted for 5 min at 95 °C, followed by denaturation 95 °C for 30 s, annealing reaction at 60 °C for 30 s, extension at 72 °C for 30 s, and final extension at 72 °C for 5 min and holding temperature at 4 °C for an unlimited period. The amplification reaction was completed in a Bio-Rad T100 (Hercules, CA, USA).

4.4. Validation of SNP-Based Multiplex Marker with Reference Strains and Laboratory Isolated Salmonella Strains

The isolated laboratory Salmonella from wild-animal fecal samples and reference bacteria strains were tested with multiplex PCR marker for efficiency tests. The cross-reaction was observed with the reference Salmonella strains (n = 6) and the laboratory Salmonella isolates (n = 21) from wild-animal and bird feces. Wild animal and bird fecal (N = 699) samples were collected from various agricultural regions and mountainous areas over three years (2015–2017) across South Korea (unpublished data). From these wild-animal fecal samples, 21 Salmonella-positive fecal based on traditional cultural, biochemical, serological, and molecular approaches according to the methods described earlier [68,69,70]. In a cultural process, a non-selective buffer peptone water (BPW) was used for primary enrichment of Salmonella at 37 °C for 18–24 h. One milliliter (1000 µL) of primary enrichment broth was added to 9 mL of Muller Kauffmann Tetrathionate enrichment broth (Difco, Becton Dickinson, NJ, USA) at 40–44 °C for 18–24 h, while 100 µL of primary enrichment broth culture was incubated in 10 mL Rappaport-Vassiliadis (RV) enrichment broth (Oxoid, UK) at 37 °C for 18–24 h. After primary and secondary enrichment, 10 µL of each sample was streaked on Salmonella-selective, Salmonella-Shigella (SS) and Hektoen enteric (HE) agar media (Difco, Becton Dickinson, United States), and incubated at 37 °C for 24–48 h. A presumptive Salmonella colony from SS and HE agar media was identified by amplifying the invA and iroB primer sets, the genes (invasion gene, invA, and iron-regulated virulence gene, iroB), which are shared by all Salmonella species (35, 36). The identified Salmonella isolates from wild animal feces were tested and evaluated by the developed SNP-based m-PCR primers in this study. In addition, sensitivity tests were also conducted with Gram-negative and Gram-positive bacteria (data not shown). We provided the image of gram negative six Salmonella serovar strains.
Nevertheless, the sample collections were conducted under permission and the guideline of the local government. In addition, the protocol of this experiment was permitted by the Institutional Animal Care and Use Committee of Kangwon National University, Chuncheon, Korea. The approval number was KW210701-1. Moreover, the obtained Salmonella spp. sequences were compared with similar deposited sequences in NCBI, BLASTN2.2.31+ [71] analysis. The genetic sequence data (21 isolate-sequences and 6 reference sequences) were deposited into the NCBI with the accession numbers (OM793284-OM793310).

5. Conclusions

So far, we know few SNP-based multiplex PCR detection methods in which widely prevalent six S. enterica serovars were detected in a single reaction. In this stud, the developed m-PCR test could be applied to investigate and distinguish serovars in a single PCR tube. The newly developed m-PCR marker is a novel, simple and reliable method for the identification of widely found six S. enterica subsp. enterica serovar strains (typhoidal and nontyphoidal Salmonella). However, to ensure the high specificity of developed mPCR, further analysis should be conducted with a number of sample sources Salmonella serovars and other bacterial strains (wild animals, foods, food animals, environmental, and clinical samples).

6. Patents

Yung Chul Park, M.M. Rahman, and S.J. Lim. Development of multiplex PCR kit and detection of Salmonella. Kangwon National University, Korea. Patent No: 10-182857.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens11101075/s1, Figure S1: Desire single nucleotide polymorphisms (SNPs) encompassing PCR amplification of six target Salmonella with 15 primer sets; Figure S2: The alignment of “16-Sbon” primer amplified gene sequences of (Molybdate-binding periplasmic protein, modA) of target six Salmonella serotypes; Table S1: A. Information of single nucleotide polymorphism (SNPs) sites on the alignment of the reference and comparing Salmonella bogori strains. B. The position of single nucleotide polymorphism (SNPs) sites on the alignment of reference and comparing Salmonella enterica subspecies enterica serovar strains; Table S2: Information of success rate with SNP-encompassing primers amplification. Table S3: Information of target six Salmonella enetrica subspecies enterica serovars, their amplified sequences with encompassing primers, alignments of amplified sequences, and searching SNPs on the alignment, and design SNP-based primers on the respective align genes. Table S4: List of Salmonella serovar-specific single nucleotide polymorphisms (SNP)-based primers with respective genes used in this study. Table S5: A. specificity test of newly designed SNP-based primers with reference six serovars. B. Final selection of Salmonella specific SNP-based primers.

Author Contributions

Conceptualization, Y.-C.P. and M.-M.R.; methodology, Y.-C.P. and M.-M.R.; software, M.-M.R.; validation, M.-M.R., Y.-C.P. and S.-J.L.; formal analysis, M.-M.R.; investigation, M.-M.R.; resources, S.-J.L.; data curation, Y.-C.P. and M.-M.R.; writing—original draft preparation, M.-M.R. and Y.-C.P.; writing—review and editing, Y.-C.P., S.-J.L. and M.-M.R.; visualization, Y.-C.P. and M.-M.R.; supervision, Y.-C.P.; project administration, supervision, Y.-C.P.; funding acquisition, Y.-C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Cooperative Research Program for Agricultural Science & Technology Development (grant number PJ010859012016). https://www.rda.go.kr/main/mainPage.do/ (accessed on 10 September 2022). This work was also supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Animal Disease Management Technology Advancement Support Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. 122013-2). The funders had no role in study data collection, analysis, and publication decision, or manuscript preparation.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The genetic sequence data used were deposited into the following GenBank accession numbers (OM793284-OM793310). The data will be available on request to the corresponding author.

Acknowledgments

We acknowledge the Korea Veterinary Culture Collection (KVCC) and National Culture Collection of Pathogens (NCCP) institute for providing and depositing our laboratory-isolated Salmonella. Special thanks to our laboratory members of wildlife genomics at Kangwon National University, South Korea.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. den Bakker, H.C.; Moreno Switt, A.I.; Cummings, C.A.; Hoelzer, K.; Degoricija, L.; Rodriguez-Rivera, L.D.; Wright, E.M.; Fang, R.; Davis, M.; Root, T. A Whole-Genome Single Nucleotide Polymorphism-Based Approach to Trace and Identify Outbreaks Linked to a Common Salmonella Enterica Subsp. Enterica Serovar Montevideo Pulsed-Field Gel Electrophoresis Type. Appl. Environ. Microbiol. 2011, 77, 8648–8655. [Google Scholar] [CrossRef] [PubMed]
  2. Taylor, A.J.; Lappi, V.; Wolfgang, W.J.; Lapierre, P.; Palumbo, M.J.; Medus, C.; Boxrud, D. Characterization of Foodborne Outbreaks of Salmonella Enterica Serovar Enteritidis with Whole-Genome Sequencing Single Nucleotide Polymorphism-Based Analysis for Surveillance and Outbreak Detection. J. Clin. Microbiol. 2015, 53, 3334–3340. [Google Scholar] [CrossRef] [PubMed]
  3. Pightling, A.W.; Pettengill, J.B.; Luo, Y.; Baugher, J.D.; Rand, H.; Strain, E. Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations. Front. Microbiol. 2018, 9, 1482. [Google Scholar] [CrossRef] [PubMed]
  4. Dróżdż, M.; Małaszczuk, M.; Paluch, E.; Pawlak, A. Zoonotic Potential and Prevalence of Salmonella Serovars Isolated from Pets. Infect. Ecol. Epidemiol. 2021, 11, 1975530. [Google Scholar] [CrossRef]
  5. Dekker, J.P.; Frank, K.M. Salmonella, Shigella, and Yersinia. Clin. Lab. Med. 2015, 35, 225–246. [Google Scholar] [CrossRef]
  6. Ye, Q.; Shang, Y.; Chen, M.; Pang, R.; Li, F.; Xiang, X.; Wang, C.; Zhou, B.; Zhang, S.; Zhang, J. Identification of Novel Sensitive and Reliable Serovar-Specific Targets for PCR Detection of Salmonella Serovars Hadar and Albany by Pan-Genome Analysis. Front. Microbiol. 2021, 12, 540. [Google Scholar] [CrossRef]
  7. Branchu, P.; Bawn, M.; Kingsley, R.A. Genome Variation and Molecular Epidemiology of Salmonella Enterica Serovar Typhimurium Pathovariants. Infect. Immun. 2018, 86, e00079-18. [Google Scholar] [CrossRef]
  8. European Food Safety Authority; European Centre for Disease Prevention and Control. Multi-Country Outbreak of Salmonella Agona Infections Possibly Linked to Ready-to-Eat Food; Wiley Online Library: Hoboken, NJ, USA, 2018. [Google Scholar]
  9. Jourdan-da Silva, N.; Fabre, L.; Robinson, E.; Fournet, N.; Nisavanh, A.; Bruyand, M.; Mailles, A.; Serre, E.; Ravel, M.; Guibert, V. Ongoing Nationwide Outbreak of Salmonella Agona Associated with Internationally Distributed Infant Milk Products, France, December 2017. Eurosurveillance 2018, 23, 17–852. [Google Scholar] [CrossRef]
  10. Marchello, C.S.; Birkhold, M.; Crump, J.A.; Martin, L.B.; Ansah, M.O.; Breghi, G.; Canals, R.; Fiorino, F.; Gordon, M.A.; Kim, J.-H. Complications and Mortality of Non-Typhoidal Salmonella Invasive Disease: A Global Systematic Review and Meta-Analysis. Lancet Infect. Dis. 2022, 22, 692–705. [Google Scholar] [CrossRef]
  11. Allgayer, M.C.; Lima-Rosa, C.A.V.; Weimer, T.A.; Rodenbusch, C.R.; Pereira, R.A.; Streck, A.F.; Oliveira, S.D.; Canal, C.W. Molecular Diagnosis of Salmonella Species in Captive Psittacine Birds. Vet. Rec. 2008, 162, 816–819. [Google Scholar] [CrossRef]
  12. de Souza, M.L.; Coelho, M.L.; da Silva, A.O.; da Silva Azuaga, L.B.; Macedo Coutinho Netto, C.R.; Galhardo, J.A.; Brito Leal, C.R.; do Nascimento Ramos, C.A. Salmonella Spp. Infection in Psittacidae at a Wildlife Rehabilitation Center in the State of Mato Grosso Do Sul, Brazil. J. Wildl. Dis. 2020, 56, 288–293. [Google Scholar] [CrossRef] [PubMed]
  13. dos Santos, E.J.E.; Lopes, A.T.S.; Maciel, B.M. Salmonella in Wild Animals: A Public Health Concern. In Enterobacteria [Working Title]; IntechOpen: London, UK, 2022. [Google Scholar]
  14. Piras, F.; Spanu, V.; Siddi, G.; Gymoese, P.; Spanu, C.; Cibin, V.; Schjørring, S.; De Santis, E.P.L.; Scarano, C. Whole-Genome Sequencing Analysis of Highly Prevalent Salmonella Serovars in Wild Boars from a National Park in Sardinia. Food Control 2021, 130, 108247. [Google Scholar] [CrossRef]
  15. Siddiky, N.A.; Sarker, S.; Khan, S.R.; Rahman, T.; Kafi, A.; Samad, M.A. Virulence and Antimicrobial Resistance Profile of Non-Typhoidal Salmonella Enterica Serovars Recovered from Poultry Processing Environments at Wet Markets in Dhaka, Bangladesh. PLoS ONE 2022, 17, e0254465. [Google Scholar] [CrossRef] [PubMed]
  16. Gutema, F.D.; Agga, G.E.; Abdi, R.D.; De Zutter, L.; Duchateau, L.; Gabriël, S. Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and Meta-Analysis of Published Studies, 2000–2017. Front. Vet. Sci. 2019, 6, 102. [Google Scholar] [CrossRef] [PubMed]
  17. Kim, J.H.; Hur, S.J.; Yim, D.G. Monitoring of Microbial Contaminants of Beef, Pork, and Chicken in HACCP Implemented Meat Processing Plants of Korea. Korean J. food Sci. Anim. Resour. 2018, 38, 282. [Google Scholar]
  18. Kim, E.; Park, S.; Cho, S.; Hahn, T.-W.; Yoon, H. Comparative Genomics Approaches to Understanding Virulence and Antimicrobial Resistance of Salmonella Typhimurium ST1539 Isolated from a Poultry Slaughterhouse in Korea. J. Microbiol. Biotechnol. 2019, 29, 962–972. [Google Scholar] [CrossRef] [PubMed]
  19. Sun, T.; Liu, Y.; Qin, X.; Aspridou, Z.; Zheng, J.; Wang, X.; Li, Z.; Dong, Q. The Prevalence and Epidemiology of Salmonella in Retail Raw Poultry Meat in China: A Systematic Review and Meta-Analysis. Foods 2021, 10, 2757. [Google Scholar] [CrossRef]
  20. Tang, S.; Orsi, R.H.; Luo, H.; Ge, C.; Zhang, G.; Baker, R.C.; Stevenson, A.; Wiedmann, M. Assessment and Comparison of Molecular Subtyping and Characterization Methods for Salmonella. Front. Microbiol. 2019, 10, 1591. [Google Scholar] [CrossRef]
  21. Ricke, S.C.; Kim, S.A.; Shi, Z.; Park, S.H. Molecular-based Identification and Detection of Salmonella in Food Production Systems: Current Perspectives. J. Appl. Microbiol. 2018, 125, 313–327. [Google Scholar] [CrossRef]
  22. EFSA Panel on Biological Hazards; Koutsoumanis, K.; Allende, A.; Alvarez-Ordóñez, A.; Bolton, D.; Bover-Cid, S.; Chemaly, M.; Davies, R.; De Cesare, A.; Hilbert, F. Whole Genome Sequencing and Metagenomics for Outbreak Investigation, Source Attribution and Risk Assessment of Food-borne Microorganisms. EFSA J. 2019, 17, e05898. [Google Scholar]
  23. Ibrahim, G.M.; Morin, P.M. Salmonella Serotyping Using Whole Genome Sequencing. Front Microbiol. 2018, 9, 2993. [Google Scholar] [CrossRef] [PubMed]
  24. Kang, M.-S.; Besser, T.E.; Hancock, D.D.; Porwollik, S.; McClelland, M.; Call, D.R. Identification of Specific Gene Sequences Conserved in Contemporary Epidemic Strains of Salmonella Enterica. Appl. Environ. Microbiol. 2006, 72, 6938–6947. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Schut, C.H.; Farzan, A.; Fraser, R.S.; Ainslie-Garcia, M.H.; Friendship, R.M.; Lillie, B.N. Identification of Single-Nucleotide Variants Associated with Susceptibility to Salmonella in Pigs Using a Genome-Wide Association Approach. BMC Vet. Res. 2020, 16, 138. [Google Scholar] [CrossRef] [PubMed]
  26. Tang, Y.; Davies, R.; Petrovska, L. Identification of Genetic Features for Attenuation of Two Salmonella Enteritidis Vaccine Strains and Differentiation of These from Wildtype Isolates Using Whole Genome Sequencing. Front. Vet. Sci. 2019, 447. [Google Scholar] [CrossRef]
  27. Zou, Q.-H.; Li, R.-Q.; Wang, Y.-J.; Liu, S.-L. Identification of Genes to Differentiate Closely Related Salmonella Lineages. PLoS ONE 2013, 8, e55988. [Google Scholar] [CrossRef]
  28. Xin, S.; Zhu, H.; Tao, C.; Zhang, B.; Yao, L.; Zhang, Y.; Afayibo, D.J.A.; Li, T.; Tian, M.; Qi, J. Rapid Detection and Differentiating of the Predominant Salmonella Serovars in Chicken Farm by TaqMan Multiplex Real-Time PCR Assay. Front. Cell. Infect. Microbiol. 2021, 925. [Google Scholar] [CrossRef]
  29. Park, S.H.; Kim, H.J.; Cho, W.H.; Kim, J.H.; Oh, M.H.; Kim, S.H.; Lee, B.K.; Ricke, S.C.; Kim, H.Y. Identification of Salmonella Enterica Subspecies I, Salmonella Enterica Serovars Typhimurium, Enteritidis and Typhi Using Multiplex PCR. FEMS Microbiol. Lett. 2009, 301, 137–146. [Google Scholar] [CrossRef]
  30. Ferrari, R.G.; Rosario, D.K.A.; Cunha-Neto, A.; Mano, S.B.; Figueiredo, E.E.S.; Conte-Junior, C.A. Worldwide Epidemiology of Salmonella Serovars in Animal-Based Foods: A Meta-Analysis. Appl. Environ. Microbiol. 2019, 85, e00591-19. [Google Scholar] [CrossRef]
  31. Centers for Disease Control and Prevention (CDC). National Enteric Disease Surveillance: Salmonella Annual Report, 2016; Centers for Disease Control and Prevention: Atlanta, GA, USA, 28 February 2018.
  32. Centers for Disease Control and Prevention (CDC). National Enteric Disease Surveillance: Salmonella Annual Summary, 2009; Centers for Disease Control and Prevention: Atlanta, GA, USA, February 2011.
  33. Hendriksen, R.S.; Vieira, A.R.; Karlsmose, S.; Lo Fo Wong, D.M.A.; Jensen, A.B.; Wegener, H.C.; Aarestrup, F.M. Global Monitoring of Salmonella Serovar Distribution from the World Health Organization Global Foodborne Infections Network Country Data Bank: Results of Quality Assured Laboratories from 2001 to 2007. Foodborne Pathog. Dis. 2011, 8, 887–900. [Google Scholar] [CrossRef]
  34. Popa, G.L.; Papa, M.I. Salmonella Spp. Infection-a Continuous Threat Worldwide. Germs 2021, 11, 88. [Google Scholar] [CrossRef]
  35. Zeinzinger, J.; Pietzka, A.T.; Stöger, A.; Kornschober, C.; Kunert, R.; Allerberger, F.; Mach, R.; Ruppitsch, W. One-Step Triplex High-Resolution Melting Analysis for Rapid Identification and Simultaneous Subtyping of Frequently Isolated Salmonella Serovars. Appl. Environ. Microbiol. 2012, 78, 3352–3360. [Google Scholar] [CrossRef] [PubMed]
  36. Afshari, A.; Baratpour, A.; Khanzade, S.; Jamshidi, A. Salmonella Enteritidis and Salmonella Typhimorium Identification in Poultry Carcasses. Iran. J. Microbiol. 2018, 10, 45–50. [Google Scholar] [PubMed]
  37. Skov, M.N.; Madsen, J.J.; Rahbek, C.; Lodal, J.; Jespersen, J.B.; Jørgensen, J.C.; Dietz, H.H.; Chriél, M.; Baggesen, D.L. Transmission of Salmonella between Wildlife and Meat-production Animals in Denmark. J. Appl. Microbiol. 2008, 105, 1558–1568. [Google Scholar] [CrossRef] [PubMed]
  38. Rahman, M.; Azad, M.; Kalam, O.; Uddain, J.; Adnan, M.; Ali, M.; Al-Mujahidy, S.K.; Roni, M.; Kadir, Z.; Rahman, M.S. Microbial Quality Assessment and Efficacy of Low-Cost Disinfectants on Fresh Fruits and Vegetables Collected from Urban Areas of Dhaka, Bangladesh. Foods 2021, 10, 1325. [Google Scholar] [CrossRef] [PubMed]
  39. Ha, A.J.; Perez, L.G.S.; Kim, T.-J.; Mizan, M.F.R.; Nahar, S.; Park, S.-H.; Chun, H.-S.; Ha, S.-D. Research Note: Identification and Characterization of Salmonella Spp. in Mechanically Deboned Chickens Using Pulsed-Field Gel Electrophoresis. Poult. Sci. 2021, 100, 100961. [Google Scholar] [CrossRef] [PubMed]
  40. Rabsch, W. Salmonella Typhimurium Phage Typing for Pathogens. In Salmonella; Springer: Berlin, Germany, 2007; pp. 177–211. [Google Scholar]
  41. Alcaine, S.D.; Soyer, Y.; Warnick, L.D.; Su, W.-L.; Sukhnanand, S.; Richards, J.; Fortes, E.D.; McDonough, P.; Root, T.P.; Dumas, N.B. Multilocus Sequence Typing Supports the Hypothesis That Cow-and Human-Associated Salmonella Isolates Represent Distinct and Overlapping Populations. Appl. Environ. Microbiol. 2006, 72, 7575–7585. [Google Scholar] [CrossRef]
  42. Ben-Darif, E.; Jury, F.; De Pinna, E.; Threlfall, E.J.; Bolton, F.J.; Fox, A.J.; Upton, M. Development of a Multiplex Primer Extension Assay for Rapid Detection of Salmonella Isolates of Diverse Serotypes. J. Clin. Microbiol. 2010, 48, 1055–1060. [Google Scholar] [CrossRef]
  43. Rahman, M.-M.; Lim, S.; Park, Y.-C. Development of Single Nucleotide Polymorphism (SNP)-Based Triplex PCR Marker for Serotype-Specific Escherichia Coli Detection. Pathogens 2022, 11, 115. [Google Scholar] [CrossRef]
  44. Kim, T.-W.; Jang, Y.-H.; Jeong, M.K.; Seo, Y.; Park, C.H.; Kang, S.; Lee, Y.J.; Choi, J.-S.; Yoon, S.-S.; Kim, J.M. Single-Nucleotide Polymorphism-Based Epidemiological Analysis of Korean Mycobacterium Bovis Isolates. J. Vet. Sci. 2021, 22, e24. [Google Scholar] [CrossRef]
  45. Uelze, L.; Grützke, J.; Borowiak, M.; Hammerl, J.A.; Juraschek, K.; Deneke, C.; Tausch, S.H.; Malorny, B. Typing Methods Based on Whole Genome Sequencing Data. One Heal. Outlook 2020, 2, 1–19. [Google Scholar] [CrossRef]
  46. Sun, L.; Zhang, H.; Chen, J.; Chen, L.; Qi, X.; Zhang, R. Epidemiology of Foodborne Disease Outbreaks Caused by Nontyphoidal Salmonella in Zhejiang Province, China, 2010–2019. Foodborne Pathog. Dis. 2021, 18, 880–886. [Google Scholar] [CrossRef] [PubMed]
  47. Uelze, L.; Becker, N.; Borowiak, M.; Busch, U.; Dangel, A.; Deneke, C.; Fischer, J.; Flieger, A.; Hepner, S.; Huber, I. Toward an Integrated Genome-Based Surveillance of Salmonella Enterica in Germany. Front. Microbiol. 2021, 12, 200. [Google Scholar] [CrossRef] [PubMed]
  48. Behl, A.; Nair, A.; Mohagaonkar, S.; Yadav, P.; Gambhir, K.; Tyagi, N.; Sharma, R.K.; Butola, B.S.; Sharma, N. Threat, Challenges, and Preparedness for Future Pandemics: A Descriptive Review of Phylogenetic Analysis Based Predictions. Infect. Genet. Evol. 2022, 98, 105217. [Google Scholar] [CrossRef] [PubMed]
  49. Criscuolo, A.; Issenhuth-Jeanjean, S.; Didelot, X.; Thorell, K.; Hale, J.; Parkhill, J.; Thomson, N.R.; Weill, F.-X.; Falush, D.; Brisse, S. The Speciation and Hybridization History of the Genus Salmonella. Microb. Genom. 2019, 5, e000284. [Google Scholar] [CrossRef]
  50. Guard, J.; Abdo, Z.; Byers, S.O.; Kriebel, P.; Rothrock Jr, M.J. Subtyping of Salmonella Enterica Subspecies I Using Single-Nucleotide Polymorphisms in Adenylate Cyclase. Foodborne Pathog. Dis. 2016, 13, 350–362. [Google Scholar] [CrossRef]
  51. Roumagnac, P.; Weill, F.-X.; Dolecek, C.; Baker, S.; Brisse, S.; Chinh, N.T.; Le, T.A.H.; Acosta, C.J.; Farrar, J.; Dougan, G. Evolutionary History of Salmonella Typhi. Science 2006, 314, 1301–1304. [Google Scholar] [CrossRef]
  52. Octavia, S.; Lan, R. Single Nucleotide Polymorphism Typing of Global Salmonella Enterica Serovar Typhi Isolates by Use of a Hairpin Primer Real-Time PCR Assay. J. Clin. Microbiol. 2010, 48, 3504–3509. [Google Scholar] [CrossRef]
  53. Ogunremi, D.; Kelly, H.; Dupras, A.A.; Belanger, S.; Devenish, J. Development of a New Molecular Subtyping Tool for Salmonella Enterica Serovar Enteritidis Based on Single Nucleotide Polymorphism Genotyping Using PCR. J. Clin. Microbiol. 2014, 52, 4275–4285. [Google Scholar] [CrossRef]
  54. Gaudet, M.; Fara, A.-G.; Beritognolo, I.; Sabatti, M. Allele-Specific PCR in SNP Genotyping. In Single Nucleotide Polymorphisms; Springer: Berlin, Germany, 2009; pp. 415–424. [Google Scholar]
  55. Shen, H.; Wen, J.; Liao, X.; Lin, Q.; Zhang, J.; Chen, K.; Wang, S.; Zhang, J. A Sensitive, Highly Specific Novel Isothermal Amplification Method Based on Single-Nucleotide Polymorphism for the Rapid Detection of Salmonella Pullorum. Front. Microbiol. 2020, 11, 560791. [Google Scholar] [CrossRef]
  56. Shah, D.H.; Park, J.-H.; Cho, M.-R.; Kim, M.-C.; Chae, J.-S. Allele-Specific PCR Method Based on RfbS Sequence for Distinguishing Salmonella Gallinarum from Salmonella Pullorum: Serotype-Specific RfbS Sequence Polymorphism. J. Microbiol. Methods 2005, 60, 169–177. [Google Scholar] [CrossRef]
  57. Shang, Y.; Ye, Q.; Wu, Q.; Xiang, X.; Zha, F.; Du, M.; Zhang, J. Novel Multiplex PCR Assays for Rapid Identification of Salmonella Serogroups B, C1, C2, D, E, S. Enteritidis, and S. Typhimurium. Anal. Methods 2022, 14, 1445–1453. [Google Scholar] [CrossRef] [PubMed]
  58. Desai, A.R.; Shah, D.H.; Shringi, S.; Lee, M.-J.; Li, Y.-H.; Cho, M.-R.; Park, J.-H.; Eo, S.-K.; Lee, J.-H.; Chae, J.-S. An Allele-Specific PCR Assay for the Rapid and Serotype-Specific Detection of Salmonella Pullorum. Avian Dis. 2005, 49, 558–561. [Google Scholar] [CrossRef] [PubMed]
  59. Campioni, F.; Cao, G.; Kastanis, G.; Janies, D.A.; Bergamini, A.M.M.; dos Prazeres Rodrigues, D.; Stones, R.; Brown, E.; Allard, M.W.; Falcão, J.P. Changing of the Genomic Pattern of Salmonella Enteritidis Strains Isolated in Brazil over a 48 Year-Period Revealed by Whole Genome SNP Analyses. Sci. Rep. 2018, 8, 10478. [Google Scholar] [CrossRef] [PubMed]
  60. Kisand, V.; Lettieri, T. Genome Sequencing of Bacteria: Sequencing, de Novo Assembly and Rapid Analysis Using Open Source Tools. BMC Genom. 2013, 14, 211. [Google Scholar] [CrossRef] [PubMed]
  61. Saltykova, A.; Wuyts, V.; Mattheus, W.; Bertrand, S.; Roosens, N.H.C.; Marchal, K.; De Keersmaecker, S.C.J. Comparison of SNP-Based Subtyping Workflows for Bacterial Isolates Using WGS Data, Applied to Salmonella Enterica Serotype Typhimurium and Serotype 1,4,[5],12:I:-. PLoS One 2018, 13, e0192504. [Google Scholar] [CrossRef] [PubMed]
  62. Gymoese, P.; Kiil, K.; Torpdahl, M.; Østerlund, M.T.; Sørensen, G.; Olsen, J.E.; Nielsen, E.M.; Litrup, E. WGS Based Study of the Population Structure of Salmonella Enterica Serovar Infantis. BMC Genomics 2019, 20, 870. [Google Scholar] [CrossRef]
  63. Hu, L.; Cao, G.; Brown, E.W.; Allard, M.W.; Ma, L.M.; Zhang, G. Whole Genome Sequencing and Protein Structure Analyses of Target Genes for the Detection of Salmonella. Sci. Rep. 2021, 11, 20887. [Google Scholar] [CrossRef]
  64. Den Bakker, H.C.; Allard, M.W.; Bopp, D.; Brown, E.W.; Fontana, J.; Iqbal, Z.; Kinney, A.; Limberger, R.; Musser, K.A.; Shudt, M. Rapid Whole-Genome Sequencing for Surveillance of Salmonella Enterica Serovar Enteritidis. Emerg. Infect. Dis. 2014, 20, 1306. [Google Scholar] [CrossRef]
  65. Lampel, K.A.; Keasler, S.P.; Hanes, D.E. Specific Detection of Salmonella Enterica Serotype Enteritidis Using the Polymerase Chain Reaction. Epidemiol. Infect. 1996, 116, 137–145. [Google Scholar] [CrossRef]
  66. Moorhead, S.M.; Dykes, G.A.; Cursons, R.T. An SNP-Based PCR Assay to Differentiate between Listeria Monocytogenes Lineages Derived from Phylogenetic Analysis of the SigB Gene. J. Microbiol. Methods 2003, 55, 425–432. [Google Scholar] [CrossRef]
  67. Chen, X.; Sullivan, P.F. Single Nucleotide Polymorphism Genotyping: Biochemistry, Protocol, Cost and Throughput. Pharmacogenom. J. 2003, 3, 77–96. [Google Scholar] [CrossRef] [PubMed]
  68. Andrews, W.H.; Jacobson, A.; Hammack, T. Bacteriological Analytical Manual (BAM) Chapter 5: Salmonella; U.S. Food and Drug Administration: Silver Spring, MD, USA, 2018.
  69. Bäumler, A.J.; Heffron, F.; Reissbrodt, R. Rapid Detection of Salmonella Enterica with Primers Specific for IroB. J. Clin. Microbiol. 1997, 35, 1224–1230. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Ferretti, R.; Mannazzu, I.; Cocolin, L.; Comi, G.; Clementi, F. Twelve-Hour PCR-Based Method for Detection of Salmonella Spp. in Food. Appl. Environ. Microbiol. 2001, 67, 977–978. [Google Scholar] [CrossRef] [PubMed]
  71. Zhang, Z.; Schwartz, S.; Wagner, L.; Miller, W. A Greedy Algorithm for Aligning DNA Sequences. J. Comput. Biol. 2000, 7, 203–214. [Google Scholar] [CrossRef]
Pathogens 11 01075 g001 550
Figure 1. A Figure depicts the PCR amplification of six target Salmonella serovars with 23 SNP-based primers. ‘M’ denotes DNA 100 bp marker. The gel lane numbers were presented in each section (1–6): lane No.1 = Salmonella enterica subspecies enterica serovar Typhimurium, S. Typhimurium, (NCCP-14760); No.2 = S. Enteritidis (NCCP-14545); No.3 = S. Agona (NCCP-12231); No.4 = S. enterica (NCCP-15756); No.5 = S. Typhi (NCCP-14641); No.6 = S. Abony (BA1800061). Detailed primer sequence information is provided in Table S4. Here, all the primers were presented on top of the gel image, the target length of each primer was provided in a white shaded box, and the desired band of each primer of target Salmonella was marked by a black circle. We selected only the single intense band (primer, mrcB-1) with the SNP-based single-plex multiplication for further analysis. The primers produce a non-target light band (SBG-4F/R), a double band (SBG (2)-4); and multiple bands [(SBG-2(2); SBG (2)-2; SBG (2)-1)]; which were not considered for the identification of target Salmonella serovars.
Figure 1. A Figure depicts the PCR amplification of six target Salmonella serovars with 23 SNP-based primers. ‘M’ denotes DNA 100 bp marker. The gel lane numbers were presented in each section (1–6): lane No.1 = Salmonella enterica subspecies enterica serovar Typhimurium, S. Typhimurium, (NCCP-14760); No.2 = S. Enteritidis (NCCP-14545); No.3 = S. Agona (NCCP-12231); No.4 = S. enterica (NCCP-15756); No.5 = S. Typhi (NCCP-14641); No.6 = S. Abony (BA1800061). Detailed primer sequence information is provided in Table S4. Here, all the primers were presented on top of the gel image, the target length of each primer was provided in a white shaded box, and the desired band of each primer of target Salmonella was marked by a black circle. We selected only the single intense band (primer, mrcB-1) with the SNP-based single-plex multiplication for further analysis. The primers produce a non-target light band (SBG-4F/R), a double band (SBG (2)-4); and multiple bands [(SBG-2(2); SBG (2)-2; SBG (2)-1)]; which were not considered for the identification of target Salmonella serovars.
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Figure 2. The PCR amplification of 6 target Salmonella serovars with SNP-based multiplex serotype-specific Salmonella primer set (S1 indicate the three distinguish band of 2, 3, and 6 no lane; and S2 indicate the rest of three distinguish band of 1, 4, 5 no lane for clear visualization with the necked eye). The band ‘M’ denotes the DNA 100 bp marker. The lane numbers were presented in each section (1–6): the gel lane no = Salmonella enterica subspecies enterica serovar (Typhimurium) name (reference no, target length) of S1: lane No.2 = S. Enteritidis (NCCP-14545; 498 bp); No.3 = S. Agona (NCCP-12231, 373 bp); No.6 = S. Abony (BA1800061, 300 bp) and S2: lane No.1 = S. Typhi (NCCP-14641, 363 bp); No.4 = S. enterica (NCCP-15756, 242 bp); No.5 = S. Typhimurium (NCCP-14760, 637 bp).
Figure 2. The PCR amplification of 6 target Salmonella serovars with SNP-based multiplex serotype-specific Salmonella primer set (S1 indicate the three distinguish band of 2, 3, and 6 no lane; and S2 indicate the rest of three distinguish band of 1, 4, 5 no lane for clear visualization with the necked eye). The band ‘M’ denotes the DNA 100 bp marker. The lane numbers were presented in each section (1–6): the gel lane no = Salmonella enterica subspecies enterica serovar (Typhimurium) name (reference no, target length) of S1: lane No.2 = S. Enteritidis (NCCP-14545; 498 bp); No.3 = S. Agona (NCCP-12231, 373 bp); No.6 = S. Abony (BA1800061, 300 bp) and S2: lane No.1 = S. Typhi (NCCP-14641, 363 bp); No.4 = S. enterica (NCCP-15756, 242 bp); No.5 = S. Typhimurium (NCCP-14760, 637 bp).
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Figure 3. PCR amplification of Salmonella serovar-specific multiplex primer set (m-PCR) with our lab isolated Salmonella and reference Salmonella. The ‘M’ denotes DNA 100 bp marker. Salmonella enterica subspecies enterica serovars (S. serovar); the gel lane no. (1–9) for m-PCR primer sets, S1: three reference Salmonella (lane, 1–3) lane No.1 = S. Enteritidis (NCCP-14545); No.2 = S. Agona (NCCP-12231); No.3 = S. Abony (BA1800061) and No.4 = negative control (only PCR mixture without DNA); No.5 = Prionailurus bengalensis; No.6 = P. bengalensis; No.7 = P. bengalensis; No.8 = P. bengalensis; No.9 = P. bengalensis and the gel lane no. (10–16) for m-PCR primer sets, S2: three reference Salmonella (lane, 10–12); lane No.10 = S. Typhimurium (NCCP-14760); No.11 = S. Typhi (NCCP-14641); No.12 = Salmonella enterica subsp. enterica (NCCP-15756); No.13 = reference non-Salmonella, Enterobacter cloacae (NCCP-14621); No.14 = P. bengalensis; No.15 = P. bengalensis; and No.16 = Pica sericea.
Figure 3. PCR amplification of Salmonella serovar-specific multiplex primer set (m-PCR) with our lab isolated Salmonella and reference Salmonella. The ‘M’ denotes DNA 100 bp marker. Salmonella enterica subspecies enterica serovars (S. serovar); the gel lane no. (1–9) for m-PCR primer sets, S1: three reference Salmonella (lane, 1–3) lane No.1 = S. Enteritidis (NCCP-14545); No.2 = S. Agona (NCCP-12231); No.3 = S. Abony (BA1800061) and No.4 = negative control (only PCR mixture without DNA); No.5 = Prionailurus bengalensis; No.6 = P. bengalensis; No.7 = P. bengalensis; No.8 = P. bengalensis; No.9 = P. bengalensis and the gel lane no. (10–16) for m-PCR primer sets, S2: three reference Salmonella (lane, 10–12); lane No.10 = S. Typhimurium (NCCP-14760); No.11 = S. Typhi (NCCP-14641); No.12 = Salmonella enterica subsp. enterica (NCCP-15756); No.13 = reference non-Salmonella, Enterobacter cloacae (NCCP-14621); No.14 = P. bengalensis; No.15 = P. bengalensis; and No.16 = Pica sericea.
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Table
Table 1. The developed single nucleotide polymorphisms (SNPs)-encompassing primers information based on whole-genome sequences (WGS) of Salmonella bongori.
Table 1. The developed single nucleotide polymorphisms (SNPs)-encompassing primers information based on whole-genome sequences (WGS) of Salmonella bongori.
No. cForward Primer
(5′-3′)		Reverse Primer
(5′-3′)		Amplicon Size (bp)Gene (Position of SNP: Nucleotide), Flanking Sequences in between Ambiguous Code bOne-Letter Amino Acid (a.a) Code of Comparing Salmonella bongori Strains RKS3044/N268-08
-a.a Position of Ref. S. bongori NCTC 12419-a.a Code (Mutation Types) a		Respective Genes
01GGGGAAATGTTGGCGGGATTATGCCCGGTGCCATGG735SBG_RS00105 (20978: G) CAACCTGCCDACCCCGATGAGK/Y-145-D (nonsynonymous)Conserved hypothetical protein (SBG)
04TTGCTGGTCGCCTTCCTGCGTATCGCGTGGCAAGGA863dedA (104317: G) TCTGGCTGGHGCCGCTATTGA G/G-163-G (Synonymous)DedA family integral membrane (dedA)
06CGCGTGATGGAGCAGGATCCTCACACAGGCGCTGAA674yacG (146357: T) CCAGACGACBGCTTTACCACAA/P-15-A (Synonymous)Conserved hypothetical protein (yacG)
09GGCGTTGAAGAAGCAGCGACGGCCTACCCAGGTGAT799mrcB (210347: C) CGCCAGCGGBGGAAATCGCGC.G/G-626-G (Synonymous)Penicillin Binding protein (mrcB)
11TTCTGGCCAGCGACCTTGTGCCAGTTTCAGCCACCC714mesJ (263017: G) TGAACTGCGBCAACCGCGCGC.R/R-349-R (Synonymous)tRNA (Ile)-lysidine synthase (mesJ)
12ATTGGCACGCTGTCAGCTTGCCGGTAAAAGCACGCT681metN (270836: G) CGGATCGAGGBCGCTGGTCGC.A/A-169-A(Synonymous)Methionine import ATP-binding protein (metN)
13GCTGTACCTGCCGACTGGGTTCCCCACGGGCTATGG797rihB (576128: T) GCGTATGACDCTGCAGTACG.T/T-69-T (Synonymous)Pyrimidine-specific ribonucleoside hydrolase (rihB)
14TCCCCTGTGTTTCGACGCACGCCGGATAAGACGCTG682rihA (626519: G) CTCGGCAGCBGCGTCCAGTT.P/P-167-P (Synonymous)Pyrimidine-specific ribonucleoside hydrolase(rihA)
15GCGGGAAACTCCTGTGCTCAACACCCGGCAGCAAAC766modA (683698: C) ACTACACCGVCGCTTCATGG.R/R-104-R (Synonymous)Molybdate-binding periplasmic protein (modA)
16ACGGTCTGGGTGAGGTGTCCACCGCATCAGAACCGT836modA (747388: T) TGCGGCGGADTATAAAAAAGA.D/D-47-D (Synonymous)Molybdate-binding periplasmic protein (modA)
18GCATCTGGATCTGCGCCATCGGCGACAAAGGTTCCC751hutG (766287: G) AATGCCGGCBTTTCCGCCCC.A/A-252-A (Synonymous)Formimidoylglutamase (hutG)
19TCACGGCGGGTAAGAGGAATGAGATTCGCCAGGCCG666yehX (792736: T) GGCTTTGCCDAGCTGACTTT.S/S-397-S (Synonymous)Hypothetical ABC transporter ATP-binding (yehX)
21CTGCTTAAACGGCGCGTCTGGTGCGGCATGATCCTG738ybiY (827585: T) TGAGCCAGGHTGGAAAAATGGN/Y-87-S (Nonsynonymous)pyruvate formate-lyase 3-activating enzyme (ybiY)
22CCGAACAGACGGCTCAGGCCGGACATCAAGGGTCGC681moeB (828767: C) GACGCCTGCVCCGGCCAGATAG/G-52-G (Synonymous)Molybdopterin biosynthesis MoeB protein (moeB)
24GTAGTGTGGCGGGCTGAGCTGGTAAGCGTGCTGGCC801sopA (1032435: T) CTCATAAAGHGCCGCGGCTTTA/A-494-A (Synonymous)Candidate type three secretion system effector protein (sopA)
a Reference genome of Salmonella bongori str. NCTC 12419 (NC_015761), the comparing strains (S. bongori serovar 48:z41:-str. RKS3044 (NZ_CP006692), and S. bongori N268-08 (NC_021870) and ‘1-letter’ amino acid codes, K = Lysine, Y = Tyrosine, G = Glycine, A = Alanine, R = Arginine, T = Threonine, P = Proline, D = Aspartate, S = Serine, N = Asparagine; b Ambiguous codes indicate D = A/G/T; B = C/G/T; V = A/C/G; H = A/C/T; c indicates primer code no.-(such as ‘01-Sbon’, ‘04-Sbon’, ‘06-Sbon’and so on, a total 15 primer sets) which acquired based on the suitable primers among multiple primers generating by bioinformatics software and we considered the selected primer indicators i.e., amplicon length, G: C content, synonymous and nonsynonymous amino acid mutation in a protein-coding gene sequence, annealing temperature, the position of the SNPs sites, and so on. SNPs sites are marked by bold International Union of Pure and Applied Chemistry (IUPAC) codes in flanking sequence (D = A/G/T; B = C/G/T; V = A/C/G; H = A/C/T).
Table
Table 2. The developed multiplex primers based on single nucleotide polymorphisms (SNPs) of the whole genome of Salmonella.
Table 2. The developed multiplex primers based on single nucleotide polymorphisms (SNPs) of the whole genome of Salmonella.
GenePrimer Sequence (5′-3′) #(Mer bp)Size (bp)Salmonella Serovar Strainsm-PCR
RNA polymerase sigma factor FliA (fliA)SBG-2FTTACCAGGAAGAGCTCGAC19498Salmonella
Enteritidis		S1
SBG-2RCGGTGCCATGGCTCATCTCG20
Molybdate-binding periplasmic protein (modA)ModA-3-FTCGCAGGGGCGACATTATCTTCCA24373Salmonella
Agona
ModA-3-RAGACGAATCCAGTCCGTTTTGCTA24
E3 ubiquitin-protein ligase sopA (sopA)SBG (2)-6FGCTGGTTCAGCTCCCCATTA20300Salmonella
Abony
SBG (2)-6RCGGACTGGACAACCCGCTCC20
Penicillin binding protein (mrcB)mrcB-1-FTGGCGTTAGGTCTACCGTCA20363Salmonella
Typhi		S2
mrcB-1-RTTGTCGTCCCGGTTTTATCG20
Molybdate-binding periplasmic protein (modA)ModA-4-FTTACGCCTGGTCGCAGGGACA21242Salmonella 
enterica
ModA-4-RCATTTCTGATCAGCAGAGATGGAG24
Penicillin binding protein (mrcB)mrcB-5-FGGCGGAGCCGCAGTATACT19637Salmonella
Typhimurium
mrcB-5-RTGTCGTCCCGGTTTTACTCA20
# Red color indicates the natural SNP and blue color indicates the alterd/artificial mutated nucleotide (transition and transversion).
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Rahman, M.-M.; Lim, S.-J.; Park, Y.-C. Genome-Wide Searching Single Nucleotide-Polymorphisms (SNPs) and SNPs-Targeting a Multiplex Primer for Identification of Common Salmonella Serotypes. Pathogens 2022, 11, 1075. https://doi.org/10.3390/pathogens11101075

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Rahman M-M, Lim S-J, Park Y-C. Genome-Wide Searching Single Nucleotide-Polymorphisms (SNPs) and SNPs-Targeting a Multiplex Primer for Identification of Common Salmonella Serotypes. Pathogens. 2022; 11(10):1075. https://doi.org/10.3390/pathogens11101075

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Rahman, Md-Mafizur, Sang-Jin Lim, and Yung-Chul Park. 2022. "Genome-Wide Searching Single Nucleotide-Polymorphisms (SNPs) and SNPs-Targeting a Multiplex Primer for Identification of Common Salmonella Serotypes" Pathogens 11, no. 10: 1075. https://doi.org/10.3390/pathogens11101075

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