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Genotypic virulence profiles and associations in Salmonella isolated from meat samples in wet markets and abattoirs of Metro Manila, Philippines

Abstract

Background

Salmonella are pathogenic foodborne bacteria with complex pathogenicity from numerous virulence genes housed in Salmonella pathogenicity islands (SPIs), plasmids, and other gene cassettes. However, Salmonella virulence gene distributions and mechanisms remain unestablished. In the Philippines, studies mainly report Salmonella incidences and antimicrobial resistance, but little to none on virulence profiles, their associations to animal sources, collection sites and Salmonella serogroups. Hence, a total of 799 Salmonella isolates, previously obtained from pig, cow, and chicken meat samples in wet markets and abattoirs (wet markets: 124 chicken, 151 cow, and 352 pig meat isolates; abattoirs: 172 pig tonsil and jejunum isolates) in Metro Manila, Philippines, were revived and confirmed as Salmonella through invA gene polymerase chain reaction (PCR). Isolates were then screened for eight virulence genes, namely avrA, hilA, sseC, mgtC, spi4R, pipB, spvC and spvR, by optimized multiplex PCR and significant pair associations between virulence genes were determined through Fisher’s exact test. Gene frequency patterns were also determined. Salmonella serogroups in addition to animal sources and location types were also used to predict virulence genes prevalence using binary logistic regression.

Results

High frequencies (64 to 98%) of SPI virulence genes were detected among 799 Salmonella isolates namely mgtC, pipB, avrA, hilA, spi4R and sseC, from most to least. However, only one isolate was positive for plasmid-borne virulence genes, spvC and spvR. Diversity in virulence genes across Salmonella serogroups for 587 Salmonella isolates (O:3 = 250, O:4 = 133, O:6,7 = 99, O:8 = 93, O:9 = 12) was also demonstrated through statistical predictions, particularly for avrA, hilA, sseC, and mgtC. mgtC, the most frequent virulence gene, was predicted by serogroup O:9, while sseC, the least frequent, was predicted by serogroup O:4 and chicken animal source. The highest virulence gene pattern involved SPIs 1-5 genes which suggests the wide distribution and high pathogenic potential of Salmonella. Statistical analyses showed five virulence gene pair associations, namely avrA and hilA, avrA and spi4R, hilA and spi4R, sseC and spi4R, and mgtC and pipB. The animal sources predicted the presence of virulence genes, sseC and pipB, whereas location type for hilA and spi4R, suggesting that these factors may contribute to the type and pathogenicity of Salmonella present.

Conclusion

The high prevalence of virulence genes among Salmonella in the study suggests the high pathogenic potential of Salmonella from abattoirs and wet markets of Metro Manila, Philippines which poses food safety and public health concerns and threatens the Philippine food animal industry. Statistical associations between virulence genes and prediction analyses across Salmonella serogroups and external factors such as animal source and location type and presence of virulence genes suggest the diversity of Salmonella virulence and illustrate determining factors to Salmonella pathogenicity. This study recommends relevant agencies in the Philippines to improve standards in food animal industries and increase efforts in monitoring of foodborne pathogens.

Peer Review reports

Background

Salmonella are Gram-negative, rod-shaped, facultative anaerobic, and motile pathogenic bacteria within the Enterobacteriaceae family [1] that commonly reside in food animals such as livestock and poultry. They are one of the leading causes of food-borne illnesses [2]. Extensive diseases, whether gastroenteritis to systemically disseminated infections, are brought about by numerous antigenic variations among more than 2600 serovars [3,4,5]. Despite this, the genus Salmonella is divided only into S. enterica and S. bongori, with the former further divided into six subspecies [6]. However, subspecies I (S. enterica subsp. enterica) is the only one often associated with diseases among mammals. This includes Enteritidis, Typhimurium, and Typhi, which commonly cause infections [7]. Salmonella classification depends on the antigenic characterization of O (somatic), H (flagellar), and Vi (capsular) antigens and is traditionally performed through the White-Kauffmann-Le Minor scheme for serological testing [8]. The O-antigen corresponds to the O-polysaccharide found in the outermost layer of Gram-negative cells. It varies in structure and sugar composition, thus providing discrimination of Salmonella serogroups, such as B (O:4), C1 (O:6,7), C2-3 (O:8), D (O:9), and E (O:3,10) [9,10,11,12]. Meanwhile, H antigens allow differentiation of serovars and primarily have two types; phase 1 and phase 2 flagellin proteins, encoded by fliC and fliB genes, respectively [13,14,15]. In the Philippines, several studies in genotyping Salmonella using multiplex polymerase chain reaction (PCR) of O, H1, and H2 associated genes have been conducted in Salmonella from abattoirs and wet markets in Metro Manila [16,17,18,19].

Salmonella pathogenicity islands (SPIs) contain a plethora of virulence genes encoding for type III secretion systems (T3SS), transcriptional regulators, transporters, host immune interference proteins, and effectors that mediate invasion within host intestinal cells [20]. With currently 23 SPIs, containing numerous virulence factors and may have different distributions and genetic stabilities across Salmonella serovars [21, 22], Salmonella pathogenesis remains complex and largely unknown. The most studied SPI is SPI1, which is 40 kb in size and contains virulence genes such as inv, avr, hil, spa, sip, among others, and encodes for the T3SS responsible for contact-dependent transport of effector protein complexes into host cells hence contributing to invasion, pathogenesis, and host inflammatory pathways [23]. SPI2, also a well-studied SPI and 40 kb in size, encodes for another T3SS distinct from SPI1 and is activated intracellularly required for Salmonella replication [22, 23]. It contains effectors such as sse, sif, sop, srf, ssp, among others, that affect Salmonella-containing vacuole positioning, host cytoskeleton, and immune signaling [24]. Meanwhile, SPI3, although less studied and only 17 kb in size, has been involved with intramacrophage survival and primarily contains mgt, mis and mar genes [23]. SPI4 is 27 kb in size and has largely unknown functions although has been shown to encode a type 1 secretion system (T1SS) and mediates in adhesion [23, 25]. It harbors sii genes involved with immune modulation and bacterial internalization [26]. Lastly, SPI5 is only 7 kb in size with roles in enteropathogenicity encoding genes such as pip which have been associated with lipid raft accumulation and intramacrophage survival [22, 23, 27]. Meanwhile, plasmid-borne virulence genes in Salmonella, particularly spv genes, have only been found in a small number of subspecies I Salmonella serovars such as Choleraesuis, Dublin, Typhimurium and Enteritidis, among others, with variable sizes and contributes to increased pathogenicity, Salmonella replication in animals and systemic infections in humans [28]. Factors such as the amount of viable Salmonella ingested, Salmonella serovar and pathogenicity, and host status can also influence the clinical outcome [29]. Virulence genes were previously shown to be differentially expressed among S. enterica serovars and strains. Invasive forms of Enteritidis and Typhimurium, for example, exhibited repression of SPI1 and SPI4 virulence genes. In contrast, less invasive serovars Infantis and Hadar exhibited upregulation during intramacrophage infection experiments [30]. In contrast, a comparison of invasive and non-invasive phenotypes within a serovar Typhimurium strain from phase-variation, showed that SPI1 virulence genes, such as those encoding flagellins and bacterioferritin, were upregulated in invasive phenotypes [31]. Besides incidence, serogroup and serovar data [12, 16,17,18,19] and in silico serotyping using invA virulence gene [32], there is little to no information about the prevalence of virulence-associated genes in Salmonella in the Philippines with only spvC being detected from wet markets of Metro Manila [16]. Hence, this study detects virulence genes through multiplex PCR of Salmonella that were previously isolated and serogrouped in earlier studies from various retail meat of pig, cow, or chicken origins in wet markets, and pig tonsils and jejuna in abattoirs of Metro Manila. This study also determines statistical associations among virulence genes and predictions of their prevalence by Salmonella serogroups and external factors.

Methodology

Revival of Salmonella isolates

A total of 799 Salmonella isolates were previously collected from 2013 to 2016 by the Pathogen-Host-Environment Interactions Research Laboratory, Institute of Biology, College of Science, University of the Philippines Diliman from various meat samples obtained from wet markets and slaughtered pig tonsil and jejunum samples from abattoirs of Metro Manila, Philippines [12, 18, 19]. Of the 799 isolates, only 587 were previously subjected to molecular serogrouping, while the remaining 212 isolates were either not previously subjected to serogrouping or possessed putative identities [12, 19]. For wet market location type, various meat samples resulted in a total of 672 isolates wherein 151 isolates were from cows, 124 from chickens and 352 from pigs. Various meat samples included different meat products in retail wet markets such as different parts and raw or processed meats of the three animal sources. For abattoir location type, there were a total of 172 isolates, all from tonsils and jejuna of slaughtered pigs. In the case of Salmonella serogroups, 250 belong to the O:3 serogroup, 133 to O:4, 99 to O:6,7, 93 to O:8, and 12 to O:9. Culture-based isolation of Salmonella from these studies followed standard protocols [16, 17, 19]. Additionally, confirmed isolates stored in glycerol stocks at − 20 °C were subjected to a revival process for this study based on protocols from previous studies [12, 19] with some modifications. Briefly, 100-μL glycerol stock culture was transferred to 900-μL trypticase soy broth (TSB) (BD Diagnostics System, NJ, USA) and incubated at 37 °C for 18-24 h. Then, a loopful of TSB culture was streaked onto xylose lysine deoxycholate (XLD) agar (BD Diagnostics System, NJ, USA) plates and incubated at 37 °C for 18-24 h. Typical Salmonella colonies were then sub-cultured and purified on nutrient agar (NA) (BD Diagnostics System, NJ, USA) for DNA extraction and molecular confirmation through invA gene detection.

DNA extraction

DNA extraction was conducted using the boil-lysis method [16, 17, 19]. Two to three colonies of Salmonella grown on NA for 18-24 h at 37 °C were suspended in 50-μL 1X Tris-EDTA (TE) buffer and heated at 100 °C for 10 min. After cooling to room temperature, suspensions were then centrifuged at 2656 x g for 5 min. The supernatant, which contains the DNA was then transferred to a new sterile microcentrifuge tube and stored at − 20 °C for subsequent assays.

Molecular confirmation of Salmonella

DNA extracts in TE buffer were subjected to confirmatory PCR for Salmonella by amplifying and detecting the invA gene based on protocols from previous studies [16, 17, 32]. Each PCR reaction was 12.5 μL in volume, which consisted of 6.25 μL 2× GoTaq Green Master Mix (Promega, WI, USA), 4.25 μL nuclease-free water, 0.5 μL each of 10 μM forward and reverse primers for invA gene, and 1 μL DNA template. Descriptions, primer sequences, amplification conditions, amplicon size, and corresponding references for invA gene can be found in Table 1.

Table 1 Descriptions, primer sequences, amplicon sizes, amplification conditions and references for Salmonella virulence genes

Multiplex PCR optimization and detection of virulence genes

Multiplex and singleplex assays to detect eight virulence genes, namely avrA, sseC, mgtC, pipB, spi4R, hilA, spvC, and spvR, representing SPIs 1–5 and plasmid-borne genes, were optimized by temperature gradient PCR and used to screen invA confirmed Salmonella. Each multiplex PCR reaction was 12.5 μL in volume, which consisted of 6.25 μL 5X MyTaq HS Red Mix (Bioline, London, UK), 0.25 μL each of 10 μM forward and reverse primers, and 2 μL DNA template in TE buffer while variable amounts of nuclease-free water depending on the number of primer sets used to make up for the 12.5 μL volume. Each singleplex PCR reaction was also 12.5 μL in volume and followed the same composition as invA gene PCR, except for spi4R, which required 20 μM primer concentrations. avrA, sseC, mgtC, and pipB genes were optimized for multiplex PCR. Meanwhile, hilA and spvR genes were also optimized for multiplex PCR with the same conditions as with spvC, which was conducted in singleplex PCR. This is due to lack of amplification if all three genes (hilA, spvR and spvC) were included in the multiplex reaction. Amplification of the spi4R gene was optimized in singleplex PCR. Similar to invA gene, descriptions, primer sequences, amplification conditions, amplicon sizes, and corresponding references for virulence genes investigated in this study can be found in Table 1. Through Kwik-Stik™ (Microbiologics), S. enterica subsp. enterica ATCC (American Type Culture Collection) serovars Typhimurium (ATCC 14028) and Enteritidis (ATCC 13076) were used as positive controls for invA, avrA, sseC, mgtC, pipB, and spi4R, and Choleraesuis (7001) for hilA, spvC, and spvR. Negative controls used include Escherichia coli (ATCC 35218) and Klebsiella pneumoniae (ATCC 7881) also through Kwik-Stik™.

Gel electrophoresis and visualization

All PCR amplicons were analyzed in 2% agarose gels (Vivantis, Malaysia) in 1x Tris-Acetate-EDTA (TAE) with 10,000x SYBR® Safe DNA Gel Stain (ThermoFisher Scientific, MA, USA). 5 μL PCR products were loaded in each well with KAPA 10kbp Universal Ladder (Kapa Biosystems, MA, USA) as the molecular weight marker. Electrophoresis was conducted at 280 V for 45 min using the CBS Scientific gel electrophoresis system (ThermoFisher Scientific, MA, USA) containing 1× TAE solutionas the running buffer. Gels were then viewed using the Vilber Lourmat gel documentation system (Vilber, France).

Data analysis

Frequencies and patterns of virulence genes across Salmonella isolates and serogroups were determined and visualized with Excel Office 365 (Microsoft) and R version 4.0.5 in RStudio (R Foundation). UpSetR 1.4.0 was used to generate intersection plots for virulence gene patterns in RStudio [40, 41]. All statistical analyses were conducted in SPSS version 1.0.0.1447 (IBM). Significant associations in pairs among virulence genes across Salmonella isolates were determined using Fisher’s exact test, a descriptive statistical analysis in place of Chi-Square under cross tabulations. Binary logistic regression was used to determine whether Salmonella serogroup (O:3, O:4, O:6,7, O:8, or O:9), animal source (pig, cow, or chicken), or location type (wet market or abattoir) as independent variables can predict the presence of virulence genes as dependent variables, thereby assessing their contributions to Salmonella virulence [42]. For Salmonella serogroup, O:3 was used as a reference category, the pig for animal source, and wet market for location type. Odds ratios and p-values were determined to signify predictive effects on virulence genes prevalence. The statistical significance of all analyses was based on p-value less than 0.05. Analyses excluded invA virulence gene since it was used as a marker for Salmonella spp. confirmation.

Results

Prevalence of virulence genes across Salmonella isolates

A total of 799 Salmonella isolates were successfully revived using culture-based methods and were positive for invA virulence gene, confirming their Salmonella identity. All virulence genes located in SPIs 1–5 showed more than 60% prevalence (Tables 2 and 3). Among them, the most frequently detected gene was mgtC (98.62%), followed by pipB (97.37%), avrA (88.24%), hilA (71.21%), spi4R (65.71%), and sseC (64.71%). In contrast, plasmid virulence genes spvC and spvR were only detected in one isolate (0.13%).

Table 2 Prevalence of virulence genes in Salmonella based on animal source
Table 3 Prevalence of virulence genes in Salmonella based on location type

Prevalence of virulence genes among Salmonella serogroups

Considering the 587 Salmonella isolates that were previously subjected to molecular serogrouping [12, 19], serogroup-based variations in virulence gene frequencies were observed. While mgtC and pipB showed a high prevalence (> 90%) across all serogroups, other genes, such as avrA, spi4R, sseC, and hilA showed drastic variations across Salmonella serogroups (Fig. 1). For avrA, the highest frequency was observed in O:3 (94.80%) while the lowest in O:4 (75.94%), and for spi4R, the highest was also in O:3 (72.80%) while the lowest in O:9 (58.33%). Interestingly, while other Salmonella serogroups showed more than 60% prevalence of sseC, O:6,7 only showed a 19.19% detection rate. Similarly, O:9 only had a 33.33% prevalence of hilA as compared to more than 50% in other serogroups.

Fig. 1
figure 1

Frequencies of six (6) virulence genes across Salmonella serogroups, excluding spvC and spvR which are both not found across 587 serogrouped Salmonella isolates

Binary logistic regression showed significant predictions (p < 0.05) for the presence of some virulence genes among Salmonella serogroups (Table 4). avrA and sseC were predicted by serogroups O:4 and O:6,7, while hilA was predicted by serogroups O:4, O:6,7 and O:9, and mgtC was predicted by serogroup O:9 all relative to O:3 reference group, while spi4R and pipB had no significant predictions (p > 0.05). Odds ratio values revealed that serogroups O:4 and O:6,7 were less likely to carry avrA (p-values = < 0.001, 0.001; odds ratios = 0.173, 0.285) and sseC (p-values = 0.002, < 0.001; odds ratios = 0.469, 0.061), but more likely to have hilA (p-values = 0.004, < 0.001; odds ratios = 2.073, 2.666) relative to O:3. Serogroup O:9, alternatively, was shown to be less likely positive for hilA (p-value = 0.040, odds ratio = 0.276) and mgtC (p-value = 0.031, odds ratio = 0.044) relative to O:3.

Table 4 Binary logistic regression on whether Salmonella serogroups predict virulence genes prevalence

Patterns and associations of virulence genes

Among all 799 Salmonella isolates, the most frequent (29.16%) gene combination involved six genes which were avrA, hilA, sseC, mgtC, pipB, spi4R, and sseC (Fig. 2), which encompasses SPIs 1–5. This is immediately followed by three combination patterns containing five virulence genes wherein 13.52% with spi4R, hilA, avrA, pipB, and mgtC, 10.76% with sseC, spi4R, avrA, pipB, and mgtC, and 10.26% with sseC, spi4R, avrA, pipB, and mgtC. The virulence gene patterns among Salmonella isolates showing five or more gene combinations at higher frequencies suggest their overall high pathogenic potential. While the single isolate with spvC and spvR also contained hilA, sseC, mgtC, and pipB genes. Statistical analysis using Fisher’s exact test between virulence gene pairs uncovered five significant associations (p < 0.05), namely, avrA and hilA, avrA and spi4R, hilA and spi4R, sseC and spi4R, and mgtC and pipB (Table 5). spvC and spvR were excluded in all statistical analyses as they only occurred in one isolate.

Fig. 2
figure 2

Virulence gene patterns across 799 Salmonella isolates

Table 5 Associations between virulence gene pairs across Salmonella isolates

External factors affecting virulence gene prevalence

Variations in virulence gene prevalence were observed across animal sources and location types (Tables 2 and 3). mgtC and pipB genes showed a high prevalence (> 90%). sseC and spi4R prevalence were lower among pigs than other animal sources and lower in abattoirs than wet markets. hilA were found less frequently in wet markets than in abattoirs but had similar frequencies across different animal sources. spvC and spvR were detected only in one isolate; which was obtained from a pig host source in an abattoir location type.

Using binary logistic regression, animal sources and location types in this study could significantly (p < 0.05) predict some virulence gene occurrences. sseC and pipB were predicted by the animal source chicken and cow respectively, relative to pigs. In contrast, hilA and spi4R were predicted by location type abattoir, relative to the wet market. Meanwhile, avrA and mgtC showed no significant predictions (p > 0.05) (Table 6). Odds ratio values for animal sources relative to pigs suggest that Salmonella from chickens were more likely to carry sseC (p-value = 0.029; odds ratio = 1.663), while cows were less likely to carry pipB (p-value = 0.009; odds ratio = 0.227). For location type, Salmonella from abattoirs were more likely to carry hilA (p-value = 0.001; odds ratio = 2.044) but less likely to carry spi4R (p-value = < 0.001, odds ratio = 0.41) relative to wet markets.

Table 6 Contribution of animal source relative to pig and location type relative to market on virulence genes prevalence using binary logistic regression

Discussion

In this study, nine Salmonella virulence genes were investigated, with most genes except for spvC and spvR showing more than 61.45% prevalence. The occurrence of mgtC and pipB is similar to the findings of Fazl et al. [35] or at higher frequencies than some studies. Joaquim et al. [43] reported a similar prevalence of mgtC (98.31%) and avrA (93.22%) among Salmonella from slaughtered pigs, and from intensive or backyard farms with also low incidence of spvC (5.08%). Meanwhile, less than 50% of Salmonella from feces, organs, and transrectal swabs of healthy swine in farms of Tuscany, Central Italy, carried mgtC and pipB [44]. Similarly, a study on poultry-associated Salmonella reported less than 60% prevalence of these genes, but a higher prevalence (52%) of spv (e.g., R, C, B) genes than the current study [45]. mgtC, found within SPI3, is activated under low Mg2+ concentration, low pH, or in the presence of antimicrobial peptides, such as within host macrophages, which enables the transport of Mg2+ crucial for growth and survivability [46, 47]. mgtC complementation experiments have also been reported to restore wild-type phenotypes of SPI3 mutant Salmonella, suggesting the significance of mgtC within SPI3 [48]. pipB, found in SPI5 and translocated by T3SS [28], also promotes intramacrophage survival [37] and is involved in the accumulation of lipid rafts [23]. avrA, the third most detected virulence gene among Salmonella isolates in this study and within SPI1, has been demonstrated to mediate intracellular survival [49] through reduction of Beclin-1 protein, suppression of autophagy [50], and activation of STAT3 pathway involved in carcinogenesis [51]. The hilA, also in SPI1, is a central transcriptional regulator for other genes within the SPI [52]. In contrast to this study, others showed a 100% avrA or hilA gene occurrence among Salmonella from poultry, such as chicken and pigeons [34, 53, 54] and tested on specific serovars, such as Enteritidis and Typhimurium. Comparatively, 80% of Salmonella isolated from retail beefs in Malaysia and South Africa also possessed hilA which interestingly showed variations among serovar Agona and Enteritidis while none had spvC, albeit a low number of isolates [55, 56]. Whole-genome sequencing of serovars, such as Infantis strain Sal147 revealed numerous deletions of SPI1 genes such as avrA, hilA, and even invA [57]. Similarly, Salmonella from chickens in Egypt showed the absence of avrA in serovars Molade, Bargny, Inganda, and Infantis [58]. Salmonella serovar Heidelberg in a study of poultry abattoirs in Brazil showed a comparable prevalence of hilA (66.6%) and higher frequencies of avrA (98.4%) [59]. These studies collectively suggest serovar-dependent variations in virulence gene frequencies, which may explain the frequencies of avrA and hilA in this study. The role of SPI4, containing sii genes, is still unknown [23] but encodes a type 1 secretion system (T1SS) [22], mediates adhesion [26], or may also be involved in intramacrophage survival and toxin production [39]. Alternately, sseC is required to facilitate protein translocation through insertion into phagosome membranes [60]. The lower occurrence of sseC in this study can be compared with Salmonella from diarrheic children in hospitals which showed only 1.7% occurrence [61]. However, this may be due to differences in hosts sources which have been shown to cause variations in Salmonella serovars present and subsequent virulence gene prevalence. Other studies involving poultry and humans showed a 100% occurrence of sseC [35]. This study’s low frequency of spv genes may be due to associations with specific serovars [59]. On the other hand, Chaudhary et al. [62] showed that while 81% of Salmonella isolates (Typhimurium and Enteritidis) had spvR, none had spvC. An earlier study in Metro Manila, Philippines, showed different occurrence rates of spvC among Salmonella serogroups isolated from wet markets with the highest frequencies among O:7 and O:4 serogroups [12]. In contrast, another study on Salmonella isolates from retail beef showed that all eight different serovars were negative for spvC [61].

Most of the isolates possess five or more virulence genes, which reflect other studies that illustrate SPIs 1–5 as prevalent among serovars, whereas other SPIs are variably distributed across Salmonella [22]. However, Rychlik et al. [63] evaluated the pathogenicity of mutant Salmonella Enteritidis among chickens and found that only SPIs 1–2 were crucial for systemic infection while SPIs 3–5 individually had little effect on colonization capacity. A core genome among invasive Salmonella, involving SPIs 1–5, 9, 13, and 14 as well as other genes, was determined by a study using the microarray technique but phenotypically showed noninvasive strains having superior intracellular replication over the invasive strains [64]. This study also identified significant virulence gene co-occurrences. The co-occurrence of spvC and spvR was not tested in this study due to their low occurrence, which may be attributed to both being within an accessory virulence locus with spvR as the transcriptional regulator among nontyphoidal Salmonella, while their presence may still enhance pathogenicity to extraintestinal levels [65]. Virulence gene associations between hilA with avrA and spi4R may be explained by its role as a major regulatory gene, while avrA and spi4R may be due to other mechanisms between interactions of SPI1 and SPI4 virulence factors [66]. The siiA gene, within SPI4 and encoding an effector involved in T1SS-dependent adhesion of Salmonella [67], has been reported in vitro and in vivo as a direct target of hilA binding and differential regulation, thereby affecting SPI4 expression and the invasion process [68]. In contrast, sseC functions as a translocon component chaperoned by SscA within the SPI2 T3SS [69], sii genes in SPI4 are designated to form the T1SS to secrete the SiiE effector protein [70]. However, crosstalk mechanisms remain unestablished. The association between mgtC and pipB may be due to their similar involvement in Salmonella such as their induction in intramacrophage environments [37]. Similarly, transcriptome analysis by RNA-seq of Salmonella Typhimurium under intramacrophage conditions showed upregulation of the mgtCBR operon and SPI5 genes, such as pipB [71]. Nonetheless, the associations between genotypic variabilities and pathogenicity in Salmonella remain unclear and require further studies [72]. Virulence gene expressions which contribute to Salmonella pathogenicity are affected by numerous factors such as signals, nutrient limitation and other stresses including possible relationships with antimicrobial resistance [73,74,75].

In contrast with the current findings on external factors, Mthembu et al. [42] showed that the animal source and sample type did not significantly predict the prevalence of Salmonella virulence genes in their study of small-scale commercial farms but were instead predicted by location. Animal health conditions may also be a factor for the observed virulence gene predictions in this study. Skyberg et al. [76] compared virulence gene profiles of Salmonella from healthy and clinically ill birds and showed that some virulence genes, such as sopB were more frequently detected in the latter while others, such as lpfC and sifA were more frequently detected in the former, suggesting diverse roles in pathogenicity. The sopE was not found among prevalent serovars isolated from animals in Senegal but was present in all serovars isolated from diarrheic children and animals in Gambia [77], suggesting an interplay of animal source, health condition, and location in Salmonella serovar and virulence genes prevalence. Virulence gene patterns may also vary across a more specific location (e.g., market stalls), wherein a unique pattern, for example, was observed in chicken meat isolate from a given stall, while other patterns were more common in all stalls in a study of Salmonella recovered from wet markets in Thailand [78]. hilA and spi4R differed by location type in the current study which may be due to outside influences and sources such as processing, transport and environmental conditions involved in abattoirs and wet markets. A meta-analysis involving diverse animal sources, geographical locations, and Salmonella revealed extensive serovar prevalence variations across different food animals such as pork, poultry, beef, and seafood but also geographical locations such as America, Asia, Africa, and Europe [79]. Similarly, Simpson et al. [80] showed variations in Salmonella serovar diversity indices in Australia, dependent on the sample type and environment (i.e., from humans to different domestic or wild animals). Moreover, natural or retail environments are also associated with serovars, such as Paratyphi B Java in seafood, natural environment, and wild mammals. These studies imply the indirect contribution of animal source, location, and other external variables to Salmonella serovar and subsequent virulence gene diversity as well as pathogenic potential.

This study has also shown that virulence gene frequencies can be related to serogroup variations. Although these frequencies may also be affected by differences in sample size, serogroup, or serovar genotypic virulence variations, previously documented. For instance, Salmonella Enteritidis within serogroup D (O:9) isolated from diarrheic children in a city of China also showed lower frequencies for hilA (83%) than in other serogroups (84–100%) but also showed a lower prevalence for mgtC (66%) than other serogroups (80–100%) [81]. Similarly, Thung et al. [54] showed serovar differences in frequencies of virulence genes hilA, sopB, and stn with a particularly drastic difference for sopB, which was not present in serovars London and Stanley. Hybridization techniques have also demonstrated distinguishing virulence gene patterns affecting their host range across different Salmonella serovars such as Typhimurium and Choleraesuis, and subsequent mutations of these regions contributed to decreased pathogenicity in vivo [82]. Furthermore, differences in virulence gene distributions among Salmonella serogroups may subsequently present varying degrees of pathogenicity. In a study by Rakov et al. [83], functional associations and allelic variations of virulence factors using protein sequences including sseC, avrA, pipB were revealed among intestinal (noninvasive) and invasive Salmonella serovars. The study reported that proteins, such as SiiE (SPI4) were only present in the former while others, such as MgtB (SPI3) and SseL (SPI2) were only in the latter. Similarly, Salmonella characterization from sand lizards showed variations in virulence gene prevalence among rare serovars consequently affecting pathogenicity; serovar Telhashomer, which had no SPI1 genes, also showed the lowest adhesion and apoptosis induction in vitro [84]. Comparisons between a clinical Salmonella isolate, and two other serovars of different pathogenic potential showed higher disease severity in pigs based on fecal and histopathological scores among the clinical isolate and serovar Typhimurium than Derby, which was reflected accordingly in the absence of some virulence factors in Derby such as lpf, stc, stj, and sodC1 [85]. These studies corroborate with current results in that Salmonella serogroups and serovars contain diverse and complex virulence determinants, which explain their variations in pathogenesis programs and subsequent clinical outcomes.

This study mainly focused on Salmonella SPIs 1-5 due to their wide distribution and documented contributions to pathogenicity and two plasmid-borne virulence genes, their associations, and statistical analyses with external factors and Salmonella serogroups. Some isolates (n = 212) were not serogrouped in previous studies, however, this does not affect the validity of virulence gene profiles of all 799 isolates or the serogroup and virulence gene predictions of 587 isolates. In addition, the potential limitations of this study are other SPI or non-SPI virulence genes, and serovar-level analysis which can provide more in-depth genotypic characterization of Salmonella and their pathogenic insights. The associations and predictions in this study may thus be underestimated due to these caveats as there are extensive diversities in Salmonella serovars and virulence genes.

Conclusions

The high prevalence and co-occurrence of virulence genes mgtC, pipB, avrA, hilA, spi4R, and sseC support the wide distribution of SPIs 1-5 across Salmonella, their high pathogenic potential which present food safety and public health concerns and provide a wealth of virulence data of Salmonella in the Philippines. Statistical analyses also determined the predictability of virulence genes namely, hilA, sseC, spi4R, and pipB based on animal sources and location types, which suggests the contributions of external factors to Salmonella strains present and their subsequent pathogenicity. Salmonella serogroups have also been shown in this study to predict the presence of virulence genes avrA, hilA, sseC, and mgtC, which suggest the diversity of virulence gene distributions across Salmonella and thus emphasizes the complexity of their pathogenesis program. Hence, further studies to elucidate the complex mechanisms of SPI crosstalk and associations of virulence determinants across different Salmonella serovars are needed to define what facilitates the extensive clinical manifestations of Salmonella infections. While this study detected SPIs 1-5 virulence genes, future studies on the prevalence of virulence genes from other SPIs are recommended. In addition, more studies on abattoirs, wet markets and particularly farms, different food animal sources and Salmonella serovars to further elucidate contributions of external and internal factors to Salmonella virulence are recommended. Associations between virulence and antimicrobial resistance can also be explored among pathogens not limited to Salmonella. The authors also recommend policymakers in the Philippines to reinforce and re-evaluate guidelines and regulations within food animal industries involving the entire chain and expand surveillance and monitoring to protect farmers, retailers, and consumers alike.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

SPI:

Salmonella pathogenicity island

PCR:

Polymerase chain reaction

T3SS:

Type III secretion system

T1SS:

Type I secretion system

TSB:

Trypticase soy broth

XLD:

Xylose lysine deoxycholate agar

NA:

Nutrient agar

TE:

Tris-EDTA buffer

ATCC:

American Type Culture Collection

TAE:

Tris-Acetate-EDTA buffer

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Acknowledgements

The authors thank Pauline Dianne M. Santos and Phyllis Anne P. Paclibare for the technical support and Kevin L. Labrador for his assistance in Rstudio UpSetR plot generation.

Funding

This research was funded by the Department of Agriculture-Biotechnology Program Office, DABIOTECH-R1808, of the Philippines and the Department of Science and Technology-Philippine Council for Health Research and Development.

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Authors and Affiliations

Authors

Contributions

AMBC and WLR conceptualized the original research idea, goals and aims. AMBC and WLR designed the methodology. RDNP, PDGM, and CARF optimized the protocols and conditions of experiments. RDNP, PDGM, and CARF acquired the data and performed the investigation. RDNP analyzed and interpreted the acquired data. RDNP implemented software analyses and visualized the acquired data. RDNP drafted the original manuscript. RDNP, PDGM, CARF, AMBC, and WLR reviewed and edited the manuscript. AMBC and WLR supervised, managed, and coordinated responsibility of research activity, planning and execution. WLR acquired the funding for the research. All authors have read and agreed to the final version of the manuscript.

Corresponding author

Correspondence to Windell L. Rivera.

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Ethics approval and consent to participate

Ethical review and approval were waived for this study due to informed consent obtained from the Philippine National Meat Inspection Service. Animal slaughter and evisceration were performed according to national regulations. Informed consent was also obtained from veterinarians in charge of the abattoirs, and farm owners for sample collection.

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Not applicable.

Competing interests

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as potential competing interests.

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Pavon, R.D.N., Mendoza, P.D.G., Flores, C.A.R. et al. Genotypic virulence profiles and associations in Salmonella isolated from meat samples in wet markets and abattoirs of Metro Manila, Philippines. BMC Microbiol 22, 292 (2022). https://doi.org/10.1186/s12866-022-02697-6

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Keywords

  • Association
  • Pathogenicity islands
  • Prevalence
  • Salmonella
  • Virulence genes