Next Article in Journal
Cefiderocol as a Successful Therapy for Osteomyelitis Due to XDR Pseudomonas aeruginosa: A Case Report and Literature Review
Previous Article in Journal
Antibiotic Residues in Raw Cow Milk Collected from Smallholder Dairy Farms in Kasama and Mbala, Zambia
Previous Article in Special Issue
Antimicrobial and Metal Resistance Genes in Bacteria Isolated from Mine Water in Austria
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Antibiotics
Volume 14
Issue 12
10.3390/antibiotics14121198
antibiotics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Integrated Genetic Characterization and Quantitative Risk Assessment of Cephalosporin- and Ciprofloxacin-Resistant Salmonella in Pork from Thailand

by
Thawanrut Kiatyingangsulee
1,2,
Si Thu Hein
3,
Rangsiya Prathan
4,5,
Songsak Srisanga
4,5,
Saharuetai Jeamsripong
4,5 and
Rungtip Chuanchuen
4,5,*
1
National Institute of Animal Health, Department of Livestock Development, Bangkok 10900, Thailand
2
Department of Veterinary Public Health, Chulalongkorn University, Bangkok 10330, Thailand
3
Department of Anatomy, University of Veterinary Science, Yezin 15013, Myanmar
4
Research Unit in Microbial Food Safety and Antimicrobial Resistance, Department of Veterinary Public Health, Faculty of Veterinary Science, Chulalongkorn University, Bangkok 10330, Thailand
5
Center for Antimicrobial Resistance Monitoring in Food-Borne Pathogens, Faculty of Veterinary Science, Chulalongkorn University, Bangkok 10330, Thailand
*
Author to whom correspondence should be addressed.
Antibiotics 2025, 14(12), 1198; https://doi.org/10.3390/antibiotics14121198
Submission received: 27 October 2025 / Revised: 22 November 2025 / Accepted: 24 November 2025 / Published: 27 November 2025
(This article belongs to the Special Issue Distribution, Sources and Risks of Bacteria and Their Antimicrobial Resistance Genes in the Environment, 2nd Edition)
Browse Figures
Review Reports Versions Notes

Abstract

Background/Objectives: This study assessed the risk associated with third-generation cephalosporin- and fluoroquinolone-resistant Salmonella from pork consumption by integrating phenotypic resistance profiles with genetic data to characterize the risks and transmission pathways. Methods: Salmonella were isolated from raw pork meat samples (n = 793) collected from fresh markets and hypermarkets across Bangkok during 2021–2022, of which 150 were extended-spectrum β-lactamase (ESBL)-producing and 31 were fluoroquinolone-resistant isolates. Phenotypic and genotypic resistance profiles were characterized. Quantitative antimicrobial resistance risk assessment (AMR RA) was conducted using a dose–response model. Results: Salmonella spp. was detected in 42.75% of pork samples, with a higher prevalence in fresh markets (75.5%) than in hypermarket samples and with concentrations ranging from 1.3 to 180 MPN/g. Twenty-eight percent of isolates were ESBL producers, with ciprofloxacin and levofloxacin resistance observed in 5.3% and 3.0%, respectively. The blaCTX-M55 genes were located on conjugative plasmids. Whole genome sequencing revealed both vertical and horizontal gene transfer. IncHI2/N and IncC plasmids shared conserved backbones and resistance gene architectures, indicating horizontal dissemination of resistance genes. Phylogenomics suggested possible clonal transmission among pigs, pork, and humans. AMR RA estimated 88,194 annual illness cases per 100,000 people from ESBL-producing Salmonella and 61,877 from ciprofloxacin-resistant strain, compared with 95,328 cases predicted by QMRA from Salmonella contamination. Cooking pork at ≥64 °C for 3 min eliminated the risk in all scenarios. Sensitivity analysis identified initial contamination level and cooking temperature as key determinants. Conclusions: Raw pork meat consumption represents the highest risk, which can be mitigated by thorough cooking (>64 °C, ≥3 min), while integrating genomic data enhances AMR hazard identification, source attribution, and exposure assessment. Therefore, promoting well-cooked meat consumption and safe cooking practices, alongside the use of AMR genetic data to inform targeted interventions, is recommended.

1. Introduction

Salmonella enterica is a leading foodborne pathogen, causing over 94 million cases of salmonellosis and approximately 150,000 deaths annually worldwide [1]. Salmonella contamination in raw meat is a major food safety concern in developing countries, including Thailand where over 90% of the population consume pork daily, with annual consumption reaching 1.3 million tons [2,3]. Thailand’s pig production ranged from 16 to 22 million heads, and pork consumption from 1.13 to 1.49 million tons during 2020–2024 [3]. High Salmonella contamination in pork sold in traditional markets (40–80%) has been reported [4,5], highlighting the ongoing risk to consumers and the need to enhance pork supply chain safety. Importantly, raw pork meat consumption is popular in certain parts of Thailand as some traditional dishes such as Larb Moo Dib (raw pork salad), Luh (raw pork blood dish), and Nham (fermented raw pork) are sometimes prepared with uncooked pork. Such consuming behavior intensifies the risk of foodborne pathogens. This risk is further compounded by the emergence of antimicrobial resistance in Salmonella, which can disseminate across the food chain.
AMR represents a major global health challenge, causing 1.27 million deaths annually and over $100 billion in economic losses [6]. Its emergence and spread is primarily driven by antibiotic overuse and misuse in humans and livestock [7]. It was estimated that antimicrobial use in food animals accounted for 73% of global sales, with pigs consuming the most at 193 mg/PCU [8]. Extensive antibiotic use for treatment, prevention, and growth promotion in pigs could facilitate the selection and spread of AMR bacteria and genes, such as multidrug-resistant Salmonella, that can enter the food chain and pose serious risks to human health [9].
AMR spreads through vertical (VGT) and horizontal (HGT) gene transfer. VGT involves the inheritance of resistance from parent to daughter cells, while HGT enables the rapid AMR spread across species, often through transmissible plasmids and transposons that carry multiple resistance genes. Third-generation cephalosporins and fluoroquinolones are critically important antibiotics and among the most effective treatments for severe Salmonella infections with bacteremia and severe gastroenteritis. New-generation cephalosporin resistance is primarily mediated by extended-spectrum β-lactamase (ESBL) enzymes, which commonly spread via the horizontal transfer of plasmids carrying ESBL genes [10]. The global emergence of ESBL-producing Salmonella isolates have been increasingly reported in human, livestock, and meat products [11,12,13]. Concurrently, fluoroquinolones (e.g., ciprofloxacin, norfloxacin, and levofloxacin) are vital for treating multidrug-resistant (MDR) infections [14]. Fluroquinolone resistance typically arises from chromosomal mutations in gyrA and parC, which can be vertically transmitted to subsequent bacterial generations [15]. An emerging concern is the co-occurrence of ESBL genes and mutation in gyrA and parC in Salmonella from various sources in different world regions [16], limiting treatment options and leading to a higher risk of treatment failure and complications.
As a key strategy for controlling AMR, quantitative AMR risk assessments (AMR RA) systematically evaluate the probability and impact of AMR, providing insights into exposure risks, infection outcomes, and mitigation effectiveness, to identify high-risk transmission pathways and guide evidence-based interventions and policies [17]. The Codex Alimentarius Commission uses quantitative AMR RA to scientifically assess and manage risks from AMR pathogens in the food chain [17].
Understanding resistance mechanisms, transmission, evolution, and the distinction between intrinsic and acquired resistance is crucial for tracking AMR spread, predicting emerging threats, and designing targeted interventions. Such insights can be gained through genomic analysis using whole genome sequencing (WGS). It is recommended to collaborate AMR genetic data into risk assessment; however, it is not essential for basic calculation [18]. To date, genetic data remains underutilized in AMR RA and should be fully integrated to improve risk assessment. Therefore, this study aimed to quantitatively assess the health effects of consuming pork contaminated with Salmonella resistant to third-generation cephalosporins (HGT) and ciprofloxacin (VGT), by integrating complementary genetic data on AMR.

2. Results

2.1. Salmonella Contamination and Concentration

Overall, Salmonella spp. was detected in 42.75% (339/793) of pork samples, with a higher prevalence in samples from fresh markets (75.5%, 305/404) compared to those from hypermarkets (8.74%, 34/389). The Salmonella concentration in contaminated pork samples ranged from 1.3 to 180 MPN/g with an average of 18.88 MPN/g and a median of 6.40 MPN/g. Pork from fresh markets contained a higher average concentration (19.59 MPN/g, 1.3–180 MPN/g in range) compared to pork from hypermarkets (13.04 MPN/g, 1.3–140 MPN/g in range).
A total of 531 Salmonella isolates were collected, of which 33 serovars were identified. The most common serovars were Rissen (22.6%), followed by Sinstorf (17.8%) and Derby (10.2%).

2.2. Antimicrobial Susceptibilities and ESBL Production

Most isolates were resistant to ampicillin (87.95%), florfenicol (56.12%), and streptomycin (54.43%), with lower rates for cefotaxime (31.45%), gentamicin (28.81%), ceftazidime (23.92%), and others (Figure 1). Twelve percent of the isolates were MDR. None were resistant to colistin, imipenem, or meropenem. Eighty-two resistance patterns were observed, of which most common patterns were AMP-OXA (18.46%), AMP-OXA-STR (9.79%), and AMP-FFC-OXA-STR (8.85%).
Twenty-eight percent of all Salmonella isolates were confirmed as ESBL producers, accounting for 92.6% of third-generation cephalosporin resistance (n = 162). Most (97%) originated from pork in fresh markets, mainly serovar Sintorf (34%). Nearly all were cefotaxime-resistant (99.33%), while 75.33% were ceftazidime-resistant (Figure 1). Up to 32 resistance patterns were defined, of which OXA-FFC-CRO-AMP-FOT-TAZ-GEN-STR (28.66%) was most common. Fluoroquinolone-resistant Salmonella were resistant to ampicillin (100%), nalidixic acid (93.55%), streptomycin (93.55%), florfenicol (87.10%), cefotaxime (64.52%), and gentamicin (61.29%) (Figure 1).

2.3. Genotype Underlying ESBL Production and Fluoroquinolone Resistance

Among the ESBL producers (n = 150), β-lactamase genes in blaTEM (66.67%), and blaCTX-M (92.67%) groups were identified. Of all blaTEM-positive isolates (n = 100), blaTEM1 (57%) and blaTEM215 (43%) were found. Among blaCTX-M-positive isolates (n = 139), blaCTX-M-55 was most prevalent (84.17%), followed by blaCTX-M-14 (15.82%).
Point mutations were observed in QRDR of all fluoroquinolone-resistant Salmonella, including C-248-T, C-248-A and G-259-A in gyrA leading to Ser-83-Phe amino acid substitution (45.16%), Ser-83-Tyr (3.22%), and Asp-87-Asn in GyrA (6.45%), respectively, and G-239-T in parC leading to Ser-80-Ile in ParC (6.45%) (Table 1). Isolates with Ser-83-Phe additionally carried qnrS (n = 2) or both qnrS and aac(6′)-Ib-cr (n = 1). Two isolates simultaneously carried Ser-83-Phe and Asp87Asn in GyrA and Ser80Ile in ParC but lacked PMQR genes. Two others carried Ser83Tyr in GyrA, but of which also harbored qnrS. Seven ciprofloxacin-resistant Salmonella carried qnrS only.

2.4. In Vitro Conjugative Transfer of ESBL Genes

Twenty Salmonella isolates (two Wandsworth, one Rissen, three Give, and fourteen Sinstorf) yielded transconjugants carrying blaCTX-M-55, corresponding to their respective donors (Table 2). Donors and transconjugants displayed similar resistance profiles, while minor differences were observed in Salmonella Wandsworth (SA149 and SA150) and Sinstorf (SA181, SA219 and SA365) with the absence of azithromycin resistance phenotype in their corresponded transconjugants. The conjugation rate ranged from 3.3 × 10−6 to 2.06 × 10−8, with an average of 1.06 × 10−6.

2.5. Genomic and Plasmid Characteristics

2.5.1. Genomic Characteristics of Salmonella

Twelve ESBL-producing and fluoroquinolone-resistant Salmonella isolates had genome sizes of 5,006,248 bp–5,188,134 bp and GC contents of 51.9–52.2%. The number of genes ranged from 4963 to 5157 (Table A1, Appendix A). The number of protein coding sequences varied from 4627 to 4924. Sequence typing identified five sequence types (STs) including ST198 (Kentucky, n = 3), ST13 (Derby and Agona, n = 4), ST29 (Stanley, n = 1), ST34 (Typhimurium, n = 1), and ST1498 (Wandsworth and Mons, n = 3) (Table 3).

2.5.2. WGS-Based AMR Phenotype Prediction and Genotypic Analysis

Resistance patterns determined by AST and predicted by WGS (Table 3). Genome analysis revealed several AMR genes, mainly associated with ESBL and fluoroquinolone resistance (Table A2, Appendix B). Each isolate carried 12–18 AMR genes, including at least 1 β-lactamase gene, blaCTX-M-55 (n = 7), blaTEM-1 (n = 9), blaCMY (n = 3), and blaVEB (n = 1). The blaCTX-MM in SA105 was detected by PCR but not WGS. Mutations in gyrA (G259A, C248T, C248A) and parC (G239T, C170G) were found. Seven isolates (SA69, SA74, SA81, SA231, SA510, SA523, SA485) carried mutations in both genes, while others had a single QRDR mutation in either gyrA or parC with qnrS1, and SA175 additionally carried qepA. Two fluoroquinolone resistance genes were identified in eight isolates, qnrS (n = 8) aac(6′)-Ib-cr (n = 3) and qepA (n = 1), with one isolate carrying both genes (Table 3).

2.5.3. Comparison of R Plasmid

All isolates tested contained at least one plasmid in four incompatibility (Inc) groups including IncHI2/N (n = 6), IncC (n = 4), IncFII (n = 3), and IncN (n = 1) (Table 3). Four isolates (SA175, SA485, SA510 and SA523) contained two plasmids including IncC/IncN or IncC/IncFII. The blaCTX-M-55 gene was carried on IncHI2/N plasmids in serovars Kentucky (SA69, SA74 and SA231), Wandsworth (SA149 and 150), and Mons (SA153) and IncC plasmids in serovars Typhimurium (SA175). IncFII plasmids in SA485, SA510, and SA523 carried no resistance genes. The qnrS1 gene was located on IncHI2/N and IncC plasmids in eight isolates, of which SA175 additionally carried qepA on IncN plasmid (Table 3).
A comparative analysis of IncHI2/N, IncC, IncN, and IncF plasmids is presented (Figure 2). The IncHI2/N plasmids (pSA69-HI2/N, pSA74-HI2/N, pSA149-HI2/N, pSA150-HI2/N, pSA153-HI2/N, and pSA231-HI2/N) carried two AMR gene clusters, including tet(M)-blaTEM-1-blaCTX-M-55-aac(3)-IId-dfrA12-aadA2-qacE-sul1, flanked by Tn3 and IS26 and aph(3″)Ib-aph(6)Id-tet(A)-floR-tet, adjacent to tnpA and insA elements (Figure 2A).
The cluster sequence alignment showed similarity to E. coli plasmids from humans in different countries (i.e., pLA065 from Laos, accession number OP242285 and p2017.03.03CC from Vietnam, accession number LC511658) and from pigs (i.e., pD208 from Thailand, accession number LC807803 and p65 from the US, accession number MT077888). The IncHI2/N plasmids exhibited closest similarity to p65 (Figure 3) but differed from pD208 that lacked the tet(M)-KTR-Tn3-blaTEM-1 region. Both human plasmids (pLA065 and p2017.03.03CC) harbored blaCTX-M-55 but lacked the tet(M)-KTR-Tn3-blaTEM-1 region as well as class 1 integrons carrying dfrA12-aadA gene cassette arrays.
IncC plasmids (i.e., pSA523-C, pSA485-C, pSA81-C, pSA175-C, and pSA510-C) carried int2 together with several AMR genes including aac(3)-Via, ant(3″)-IIa, tet(A), floR, aph(6)-Id, aph(3″)-Ia, aph(3″)-Ib, blaCMY-2, blaTEM-1 and qacE (Figure 2C). pSA510-C from S. Kentucky and pSA523-C and pSA485-C from S. agona displayed conserved gene organization whereas alignment gaps were observed in pSA81-C from S. derby and pSA175-C in S. typhimurium. The tet(A), floR, aph(6)-Id and cmy-2 region in the IncC plasmids were similar to those in pSNE3-1928, pSB109, and pFSIS11809860-1.
IncFII plasmids (pSA510-F, pSA523-F, pSA485-F) lacked ESBL genes but encoded virulence genes (virB and hin) (Figure 2D). An IncN plasmid carrying blaTEM-1 (pSA175-N) was co-harbored with pSA175-C, an IncC plasmid harboring blaCTX-M-55 (Figure 2B).

2.5.4. Genetic Relatedness of Salmonella

Phylogenetic tree of the core genome sequences was constructed for 12 Salmonella isolates from pork and deposited into GenBank. The isolates are clustered into five distinct clades: clade 1, SA153; clade 2, SA175; clade 3, SA149; clade 4, SA69, SA231, and SA74; and clade 5, SA105, SA150, SA523, SA81, SA485, and SA510 (Figure 4).
The Salmonella isolates in clade 5 clustered with Salmonella Kentucky SSSE-138 from a slaughterhouse worker’s hand in Thailand, Salmonella Agona R21.2429 from a diarrhea case in Taiwan, and Salmonella Agona 8878_Sal_21 from a pig in Thailand.

2.6. Exposure Assessment

Of the 793 pork samples, 339 (42.75%) were contaminated with Salmonella, including 127 (16%) with ESBL-producing strains and 25 (3%) with ciprofloxacin-resistant strains. Based on the three risk scenarios, the final Salmonella concentrations across all cooking conditions followed an exponential distribution. For ESBL-producing Salmonella (Scenario 1), the mean final concentrations were 3.34, 2.76 × 10−2, 7.57 × 10−4, and 6.25 × 10−6 CFU/g after 0, 1, 2, and 3 min of cooking, respectively. Similarly, for ciprofloxacin-resistant Salmonella (Scenario 2), the mean final concentrations were 4.90 × 10−1, 4.05 × 10−3, 7.57 × 10−4, and 2.76 × 10−5 CFU/g. In scenario 3, for all Salmonella-contaminated samples, the mean final concentrations were 1.11 × 101, 2.76 × 10−2, 7.57 × 10−4, and 6.25 × 10−6 CFU/g after 0, 1, 2, and 3 min of cooking, respectively.
For the exposure estimation, a daily pork consumption rate of 10.8 g/person/day was assumed, following an exponential distribution. The probability of exposure (Pe) at a 95% confidence interval for cooking times of 0 to 3 min ranged from 9.15 × 10−1 to 6.65 × 10−5 for ESBL-producing Salmonella (scenario 1), 7.11 × 10−1 to 3.01 × 10−4 for ciprofloxacin-resistant Salmonella (scenario 2) and 9.13 × 10−1 to 6.41 × 10−5 for any Salmonella (scenario 3) (Table 4).

2.7. Hazard Characterization

The probability of illness (Pi) was estimated at a 95% confidence interval based on the daily dose of ingested bacteria derived from the exposure assessment. A comprehensive statistical analysis was conducted to incorporate the uncertainty associated with the exposure data into the final risk characterization equation.
The average Pi in scenario 1 ranged from 0.05 to 1.77 × 10−8, while in scenario 2, it ranged from 0.01 to 7.44 × 10−7 across cooking times of 0, 1, 2, and 3 min. In Scenario 3, the probability ranged from 5.10 × 10−2 to 1.64 × 10−7 over the same cooking durations (Table 4).

2.8. Risk Characterization

By combining the probabilities of exposure and illness, the annual risk of illness from consuming pork contaminated with ESBL-producing Salmonella, ciprofloxacin-resistant Salmonella, and any Salmonella was estimated at a 95% confidence interval.
Scenario 1 for ESBL-producing Salmonella predicted that the worst-case scenario, consumption of raw pork meat, would result in an average annual risk of 0.8849, equivalent to 88,492 cases per 100,000 population in Bangkok (Table 4). Cooking pork at 64 °C for 1, 2, and 3 min significantly reduced the average annual risk to 9497; 25, and 0 cases per 100,000 population, respectively. In Scenario 2, which focused on ciprofloxacin-resistant Salmonella isolates, the predicted average annual illness (Pi) at 64 °C was 63,508; 570; 23; and 0 cases per 100,000 persons, respectively, for cooking times of 0, 1, 2, and 3 min. In Scenario 3, which examined the entire Salmonella population, the predicted average annual illness at 64 °C were 95,328; 28,232; 21.51; and 0 cases per 100,000 persons for the same cooking durations.

2.9. Scenarios Comparison and Sensitivity

Compared to the uncooked condition, cooking pork for 1 to 3 min significantly reduced the annual risk of illness from ESBL-producing Salmonella from 100% to 10.7%, 0.02%, and 0%, respectively, per 100,000 people. Similarly, for ciprofloxacin-resistant Salmonella, the risk of illness decreased from 100% to 0.90%, 0.04%, and 0% for the same cooking durations. In scenario 3, the risk of illness from Salmonella (QMRA) reduced from 100% to 29.6%, 0.02%, and 0% across the 0, 1, 2, and 3 min of cooking time, respectively, for the same cooking durations. A comparison of the average illness per 100,000 population per year among the three scenarios is shown in Figure 5. ESBL-producing Salmonella posed a greater risk than ciprofloxacin-resistant Salmonella but a risk comparable to that of all Salmonella. Across all scenarios, the risk became negligible after 3 min of cooking.
A sensitivity analysis was conducted to assess the impact of two key input variable factors: initial bacterial concentration and daily pork consumption (Table 5). The R-value represents the magnitude of the impact of each factor on the output variable (risk of illness). In all scenarios, the two factors exhibited similar levels of influence, with most R-values being positive and relatively weak (below 0.5). However, a stronger positive correlation was observed between initial concentration and risk of illness in scenario 1 of ESBL-producing Salmonella at condition 2 for 1 min cooking.

3. Discussion

A key finding of this study was the high Salmonella contamination rate in pork (42.75%) sold in markets. Although this rate was lower than those in previous studies from Thailand and neighboring countries [4,13], it represents a significant public health concern. The prevalence of Salmonella-contaminated pork in fresh markets was about nine times higher than in hypermarkets, reflecting comparatively lower hygiene standards in fresh markets. Similar observations have been documented in Thailand [4] and neighboring countries, including Lao PDR, Myanmar [13], Cambodia [19], Vietnam [20], and China [21]. The concentration of Salmonella in pork averaged 18.88 MPN/g, in agreement with a previous study in Thailand [22], but this is lower than that of other studies in Cambodia, Vietnam, and China [19,21,23]. This quantitative data is crucial for microbial risk assessment, as higher concentrations increase the risk of human illness. These findings underscore the need to further strengthen food safety practices and sanitation measures at points of sale.
While serovar Rissen has been widely reported in pork and poultry across Europe and Southeast Asia [24,25,26], serovar Sinstorf emerged as the second-most common serovar in this study (16.57%). Historically, serovar Sinstorf was rarely reported but its prevalence has been increasingly reported [27], suggesting a potential shift in serovar distribution. This serovar grows rapidly but exhibits reduced pathogenicity due to the lower expression of its virulence genes [28]; however, factors behind its raising prevalence remain unclear. The serovar Sinstorf exhibited MDR phenotype, with over half (51/89) producing ESBLs and capable of transferring ESBL genes. In contrast, only 4% of the most common serovar, Rissen, were ESBL producers, similar to a previous study [27]. However, the phenotypic and genotypic AMR profiles were not statistically associated with serovar Sinstorf. Further studies are warranted to elucidate the underlying characteristics of this serovar.
The prevalence of ESBL producers (28.2%) in this study was comparable to a previous report in pork from Cambodia [29], higher than those in Thailand [4,13] but lower than in China [30] and Portugal [31]. Concurrently, ciprofloxacin resistance rate in this study was rather low (5.8%), above a previous report [4] but below that in fresh markets in Northern Thailand [32], China, Brazil, and the USA [33,34,35].
This study conducted a quantitative AMR risk assessment using a market-to-fork model based on bacterial concentrations in pork and consumption patterns. Genetic AMR data for a subset of isolates was in cooperation to complement hazard identification and exposure assessment.
HGT is expected to pose a higher AMR risk by promoting broader dissemination and the emergence of new resistant strains, whereas VGT maintains AMR within populations over time [36]. Consistently, illness from consuming raw pork meat contaminated with ESBL-producing Salmonella (88,194 per 100,000 population per year) exceeded ciprofloxacin-resistant Salmonella (61,877 per 100,000 population per year), supported by the conjugal transfer of blaCTX-M-55 carrying plasmids. These results highlight the importance of antimicrobial stewardship in controlling AMR propagation along the food chain.
The QMRA was extended to assess the risk of Salmonella in pork, allowing comparison of the potential health impacts of generic versus AMR strains. The risk of illness from consuming raw pork meat contaminated with ESBL-producing Salmonella was comparable to that from raw pork meat contaminated with any Salmonella (95,328.26 per 100,000 population per year). In contrast, the lowest risk of illness was observed for consuming pork contaminated with ciprofloxacin-resistant Salmonella, primarily due to its low prevalence and limited clonal spread.
Due to the unavailability of mortality data for infections caused by ESBL-producing and ciprofloxacin-resistant Salmonella, the risk of illness was used for the assessment. Salmonella infections rarely cause death, except in vulnerable populations such as young children, the elderly, and people with complications or compromised immunity. However, AMR can increase the risk of death both directly and indirectly [37]. Moreover, the mortality data for Salmonellosis from studies conducted over two decades ago and from different geographical regions may not accurately reflect the current trends. The use of such data could lead to overestimation or underestimation of the actual risk.
Cooking pork at 64 °C for one minute significantly reduced the risk, and for 3 min eliminated the risk of illness in all scenarios, in agreement with a previous study [38]. The fixed time intervals of 0, 1, 2, and 3 min were used to estimate bacterial reduction, rather than variable durations to ensure result clarity and facilitate effective risk communication with the public. While there was no report of AMR RA of ESBL-producing or cephalosporin-resistant Salmonella in Thailand, a previous study of ciprofloxacin-resistant Salmonella from retail pork was conducted in Northern Thailand [38]. The latter showed that cooking above 60 °C effectively reduced bacterial counts in pork products and 2 min cooking at 64 °C eliminated all risk.
All ESBL-producing Salmonella in this study were MDR, consistent with a previous study [12] and likely reflecting co-localization of resistance genes and efficient horizontal gene transfer via plasmids. Almost all ESBL producers carried either blaCTX-M-55 or blaCTX-M-14, indicating a strong concordance between phenotypic and genotypic resistance. This gene has been frequently reported in Salmonella from pork and humans in multiple countries, suggesting its widespread dissemination through the food chain [29,39].
Under ampicillin selective pressure, blaCTX-M-55 in all ESBL-producing Salmonella donors were found to be located on conjugative plasmids, consistent with previous observations in both meat-derived and clinical human isolates [40]. All transconjugants exhibited MDR phenotype, indicating the co-localization of resistance genes on the same plasmid. These findings suggest that E. coli from pork can serve as a reservoir of ESBL genes capable of transferring to other bacteria, highlighting the public health risk associated with transmission through meat consumption. These findings confirm that Salmonella and its resistance determinants are hazards for risk assessment [17]. The co-selection of AMR genes by a single antibiotic further emphasizes that sustainable control of AMR demands a coordinated, whole system approach to the regulation of antimicrobial use.
WGS of the selected isolates provided comprehensive information on the AMR characteristics of each isolate, allowing comparative analysis with genomes or plasmid sequences from humana and pig sources. In addition to supporting hazard identification, such comprehensive genomic information strengthens AMR risk assessment by identifying potential reservoirs and transmission routes, improving the understanding of exposure pathways, and supporting evidence-based evaluation of the likelihood and consequences of AMR dissemination through the food chain. Comparative analysis of plasmids revealed that shared backbones and resistance gene arrays among IncHI2/N and IncC plasmids reflect conserved core structures, indicating that their spread is driven by horizontal gene transfer. Despite their high similarity, IncHI2/N plasmids in this study carried the AMR gene clusters aph(3″)Ib-tet(A)-floR-aph(6)Id that are absent in p65 and may reflect differences in antimicrobial use across geographic regions. Co-occurring plasmids in different Inc groups (IncN, IncC and IncFII[pMET]) in the same isolates (i.e., SA175, SA485, SA510, and SA523) may enhance the dissemination and stability of AMR genes through co-selection and horizontal transfer, supporting the intersectoral dissemination of MDR Salmonella by promoting bacterial fitness, adaptability, and persistence and posing public health concerns.
Fluoroquinolone-resistant Salmonella in this study carried GyrA substitutions Ser83Phe/Tyr and Asp87Asn, consistent with a previous report on pigs [32] and patients in Thailand [41]. Further investigations are required to determine whether these findings reflect the persistence and possible transmission of resistant clones among meat, animals, and humans in the country. The ParC substitution Ser80Ile was detected for the first time in Thailand but has been previously reported in animal and human isolates elsewhere [42,43]. Although combined GyrA–ParC mutations are known to confer high-level resistance, it was not the case for this study [44]. The frequent finding of qnrS (58%) agrees with previous findings in pork, beef, and chicken [11,45,46]. PMQR genes alone usually confer low-level fluoroquinolone resistance but can promote the selection of gyrA and parC mutations [47], and co-select other AMR genes [48,49].
The phylogenomic analysis demonstrated possible clonal transmission of Salmonella among pigs, pork, and humans. The close genetic relatedness between the Salmonella isolates from pork in this study and those from a human diarrhea clinical case provides evidence of zoonotic transmission, despite originating from different countries. Importantly, the isolates were clustered with those from the hand of a slaughterhouse worker and a pig in Thailand, indicating the potential cross-contamination during the slaughtering process and persistence of resistant strains along the pork production chain. This finding highlights the foodborne risks associated with AMR Salmonella and underscores the need for strengthened hygiene practices and integrated surveillance from farm to slaughterhouse to reduce transmission within the pork production system.
The sensitivity analysis indicated that bacterial concentration and pork consumption quantities had moderate-positive effects on the risk of illness, inconsistent with a previous study highlighting the highest risk factor derived from cross-contamination such as wash meat before cooking or sharing utensils [19].
This risk model used in this study offers flexibility for application to other foodborne bacteria and in other geographical contexts, with appropriate parameter adjustments. Limitations of this study include the challenges of model validation due to limited data on these specific resistant Salmonella strains, reliance on some outdated data sources, the absence of uncertainty and variability analyses, and the use of in vitro conjugation data, which may not fully reflect in vivo transfer dynamics. Therefore, further studies are warranted to strengthen the validation of this risk model and better assess its predictive accuracy. The validation could be improved by integrating longitudinal surveillance of Salmonella and AMR along pork supply chains, using more realistic data on horizontal gene transfer in animals, and incorporating variability and uncertainty analyses through probabilistic simulations. Comparing the model with independent datasets and human salmonellosis data would provide a better assessment of its performance, while testing it against field-based intervention studies would further support its practical applicability.
Several countries have culinary traditions that include the consumption of raw or undercooked pork, e.g., Thailand, Japan, China, Vietnam [50,51]. A recent survey revealed that 66% of the Thai population regularly consumes undercooked meat, with nearly 80% of these individuals residing in the northeastern and northern regions of the country [52]. This dietary habit heightens the risk of exposure to foodborne pathogens. Therefore, targeted risk communication and culturally sensitive interventions are crucial to encourage safe cooking practices.
In conclusion, consumption of raw pork meat poses the highest risk, reinforcing the importance of thoroughly cooking meat. Communication with consumers, focusing on safe food handling, and thorough cooking is crucial to mitigate the risks associated with Salmonella-contaminated pork. Public education and awareness regarding the risks of eating raw pork meat should be promoted. The data could beneficially inform policymakers for risk management. While interventions at the point of sale are important, a comprehensive approach encompassing the entire pork production chain, from farm to fork, is essential for effective AMR control.

4. Materials and Methods

4.1. Sampling Design and Sample Collection

A total of 793 pork samples were collected during 2021–2022 from fresh markets (n = 404) and hypermarkets (n = 389) in Bangkok, exceeding the calculated sample size of 770 based on an estimated Salmonella prevalence of 50%, with a 95% confidence interval, and a 5% margin of error. This cross-sectional study utilized a stratified sampling design, with districts as the stratification units. Nineteen of Bangkok’s 50 districts were proportionally selected based on population density to ensure representative geographic coverage, encompassing at least 60% of the city’s population [53,54]. Each district has 1 retail market and 1–2 hypermarkets. Eighty-three vendors from 43 participating markets were sampled, with each vendor being visited once.
Pork samples (~300 g each) from retailed markets and a whole pack from hypermarkets were aseptically collected at the point of sale. Each was individually placed in a sterile, sealed double-layered plastic bag and stored on ice. The samples were delivered to the laboratory within four hours and analyzed immediately upon arrival.

4.2. Salmonella Isolation and Confirmation

Salmonella was isolated and biochemically confirmed, following ISO 6579-1:2017 [55]. Three to five Salmonella colonies from each positive pork sample were serotyped by slide agglutination [56] using commercial antisera (S & A Reagents Lab, Bangkok, Thailand). A single colony of each serovar from each positive sample was stored in 20% glycerol solution at −80 °C for future analysis.

4.3. Salmonella Enumeration

Salmonella enumeration was performed in all pork samples using the miniaturized Most Probable Number (MPN) method [57]. Briefly, a 25 g sample was homogenized in 225 mL of Buffered Peptone Water (BPW) to achieve a 1:10 dilution. The sample was serially diluted in a 12-well titer plate with triplicate dilutions and incubated at 37 °C for 18 h. The wells were examined for the presence of Salmonella as described above. The number of positive wells across four plate columns was used to determine MPN values using Jarvis’s software version 6 [58] as recommended by ISO7218:2024 [59]. MPN values were converted to CFU using a correction factor of 0.8 to align with dose–response model calculations [60].

4.4. Determination of Antimicrobial Susceptibilities and ESBL Production

A total of 531 Salmonella isolates were obtained in this study and tested for antimicrobial susceptibilities using the automated Sensititre™ system (Thermo Fisher Scientific, Cleveland, OH, USA) with Asia surveillance plates ASSECAF (TREK Diagnostic Systems, West Sussex, UK). The antimicrobial panel (abbreviation, clinical breakpoint in parenthesis) included cefotaxime (FOT, 4 µg/mL), ceftazidime (TAZ, 16 µg/mL), ciprofloxacin (CIP, 4 µg/mL), nalidixic acid (NAL, 32 µg/mL), gentamicin (GEN, 16 µg/mL), streptomycin (STR, 16 µg/mL), meropenem (MERO, 4 µg/mL), azithromycin (AZI, 16 µg/mL), ampicillin (AMP, 32 µg/mL), and colistin (COL, 2 µg/mL). Disk diffusion method was conducted for cefoxitin (30 µg), cefepime (30 µg), ceftriaxone (30 µg), levofloxacin (5 µg), florfenicol (30 µg), imipenem (10 µg), and oxacillin (1 µg) [60]. All antimicrobial disks were commercially obtained (BD DifcoTM, Becton, Dickinson and Company, Sparks, MD, USA). Interpretive criteria were according to CLSI [61]. Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, and Staphylococcus aureus ATCC 29213 served as quality control strains. Multidrug resistance (MDR) is defined as being resistant to at least three antimicrobials from different classes.
The Salmonella isolates resistant to ceftazidime and/or cefotaxime were subsequently confirmed for ESBL production using the combination disk method. The difference in ≥5 mm in the inhibition zone between cephalosporin/clavulanic acid combination disks and cephalosporin disks alone was interpreted as ESBL-positive. Klebsiella pneumoniae ATCC 700603 was used as a quality control strain.

4.5. PCR and DNA Sequencing

Template DNA was prepared using whole-cell boiled lysates [62]. Primers used in this study are listed in Table 6. The PCR reaction for QRDR genes consisted of pre-denaturation at 94 °C for 5 min, followed by 30 cycles of denaturation at 94 °C for 45 s, annealing at 57 °C for 45 s, and extension at 72 °C for 1 min, with final extension at 72 °C for 5 min. Identical conditions were used for qnrA, qnrB, and qnrS genes except aac(6′)-Ib-cr (annealing at 55 °C for 45 s) and qepA (denaturation at 95 °C for 1 min, annealing at 60 °C for 1 min). For blaCTX-M, the protocol consisted of pre-denaturation at 94 °C for 3 min, 25 cycles of denaturation at 94 °C for 1 min, annealing at 60 °C for 1 min, extension at 72 °C for 1 min, and final extension at 72 °C for 10 min. Subgroups 1, 2, and 8/25 required 5 min pre-denaturation and annealing at 56 °C, while subgroup 9 used 5 min pre-denaturation with annealing at 60 °C. blaTEM and blaSHV followed the same conditions as blaCTX-M but with annealing at 50 °C. PCR amplicons were purified using NucleoSpin® Gel and PCR Clean-up Kit (Macherey-Nagel, Düren, Germany) and submitted for nucleotide sequencing at Bionics laboratory (Seoul, Republic of Korea). The DNA sequences were analyzed using BLAST version 2.15.0 searches against the GenBank database available at the National Center for Biotechnology Information (NCBI) website.

4.5.1. Detection of β-Lactamase Genes

All ESBL-producing Salmonella isolates (n = 150) were screened for three bla gene groups including blaCTX-M, blaTEM, and blaSHV using specific primers [67,68]. The CTX-M subgroups were further determined using primers specific for groups 1, 2, 9, and 8/25 [69]. The PCR products were purified and subjected to nucleotide sequencing.

4.5.2. Detection of Quinolone Resistance Mechanisms

All fluoroquinolone-resistant Salmonella isolates (n = 31) comprising those resistant to ciprofloxacin only (n = 15), levofloxacin only (n = 3), and both ciprofloxacin and levofloxacin (n = 13) were examined for mutations in gyrA and parC [63]. Two fluoroquinolone-susceptible isolates were included as controls. The nucleotide sequences were compared to the published gyrA (GenBank accession number 1253794) and parC (GenBank accession number 1254697) of Salmonella Typhimurium str. LT2 NC_003197.2. The PMQR genes, i.e., qnrA, qnrS, aac(6′)-Ib-cr, and qepA, were PCR-screened in all the isolates (n = 531) [64,65,66]. Previously confirmed Salmonella isolates carrying the PMQR genes were used as positive controls [71].

4.6. Conjugation Transfer of ESBL Genes

Plasmid transfer was evaluated by biparental filter mating following standard protocols. The MDR ESBL-producing Salmonella positive (n = 30) served as donors. The rifampicin-resistant E. coli K12 strain MG1655 (MG1655rifr; MIC = 256 µg/mL) served as the recipient. Transconjugants were selected on LB agar containing rifampicin (32 μg/mL) and ampicillin (100 μg/mL). The presence of ESBL genes in transconjugants was confirmed by PCR.

4.7. Whole Genome Sequencing (WGS) and Bioinformatics

4.7.1. DNA Extraction

Genomic DNA was extracted from ESBL-producing and fluoroquinolone-resistant Salmonella isolates with MDR phenotype (n = 11) and one non-ESBL-producing, fluoroquinolone-resistant isolate with MDR characteristics using ZymoBIOMICS DNA kits (Zymo Research Corp., Irvine, CA, USA) (Table 3).

4.7.2. Whole Genome Sequencing

The integrity of the extracted genomic DNA was evaluated by electrophoresis. DNA quality and concentration were determined using a NanoDrop™ 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). WGS was performed using long-read sequencing with Oxford Nanopore Technologies (ONT) at the Siriraj Long-read Lab, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, complemented by short-read sequencing with the Illumina HiSeq platform at GENEWIZ, Suzhou, China. Read quality was assessed using NanoPlot [72] and FastQC [73]. Hybrid genome assembly was performed with Unicycler [74], and annotation was carried out using the NCBI Prokaryotic Genome Annotation Pipeline [75]. The annotated genomes were deposited under BioProject accession PRJNA946550.

4.7.3. Genomic Analysis

AMR genes were identified using Resfinder [76] and point mutations in quinolone resistance-determining regions (QRDRs) were detected with PointFinder [77]. Plasmid replicon types were determined using PlasmidFinder [78]. Sequence types and serovars were confirmed by MLST and SeqSero, respectively [40]. Comparative plasmid analysis was performed with Proksee [79] and Clinker [80] to align assembled plasmids from pork isolates, with reference plasmids retrieved from GenBank, including p65 (CP037991), pLA065 (AP018139), and p2017.03.03CC (LC483178) for IncHI2/N and pHNTS45-1 (CP070282) and pN268-2 (CP031658) for IncC.
Phylogenetic analysis was conducted by integrating publicly available Salmonella isolates from humans (S. Agona R21.2429, accession no. SAMN24782759, Taiwan and S. Kentucky SSSE-136, accession no. SAMN08386787, Bangkok, Thailand) and pigs (S. Agona 8878_Sal21, accession no. SAMN14945123, Bangkok, Thailand). A maximum-likelihood tree based on core genome SNPs was generated using CSI Phylogeny [81] and visualized with iTOL v6 [82].

4.8. Risk Assessment Models

4.8.1. Model Overview

The health risks of infection and illness from consuming pork contaminated with third-generation cephalosporins and fluoroquinolone-resistant Salmonella were assessed following the Codex Alimentarius guidelines CXG 77-2011 [17]. ESBL-producing and ciprofloxacin-resistant Salmonella represented resistance to third-generation cephalosporins and fluoroquinolones, respectively. Three risk scenarios were established: Scenario 1, ESBL-producing Salmonella representing HGT of AMR; Scenario 2, ciprofloxacin-resistant Salmonella representing VGT; and Scenario 3, for the quantitative microbial risk assessment of Salmonella from pork (Figure 6). Scenario 3 served as a baseline for comparing the AMR risk estimates. The outcomes representing the annual risk of infection per person were modeled with an exponential distribution and analyzed using @Risk v8.1 (Palisade Corporation, Ithacha, NY, USA). All equations (Eq.) used in this study are listed in Table 7.

4.8.2. Exposure Assessment

  • Initial concentration variable
The initial contamination level or concentration (Cc, CFU/g) was determined by enumerating Salmonella in all 793 pork samples. The data was analyzed using @Risk program. The exponential (Expon) distribution model was identified as the best fit and applied to estimate the concentration variables.
2.
Cooking reduction model
Raw pork meat is typically cooked before consumption; however, certain traditional Thai dishes involve eating pork raw or only partially cooked. Proper cooking can effectively destroy pathogens such as Salmonella. A Log Reduction (LR) approach was applied at a cooking temperature of 64 °C, in line with FAO guidelines [83] (Equation (1)).
3.
Remaining concentration variable
The remaining concentration of Salmonella in pork (RCc) after cooking at 64 °C for 0, 1, 2, and 3 min was calculated by multiplying the LR with the initial concentration variable (Equation (2)).
4.
Consumption variable
The consumption variable (Cs) represents the daily pork consumption by Thai individuals obtained from the Agricultural Commodity and Food Standard in 2016. The variability in Cs was modeled using an exponential distribution. Subsequently, the dose of infection (D, CFU per day), that is, the daily dose of Salmonella ingested by an individual in Bangkok, was calculated (Equation (3)).
5.
Probability of exposure
The exposure assessment was to determine the probability of an individual consuming at least one CFU of Salmonella, either ESBLs-producing, ciprofloxacin-resistant, or any Salmonella from pork. The probability of exposure (Pe) was calculated using the daily ingestion dose of Salmonella (D) (Equation (4)).

4.8.3. Hazard Characterization

Hazard was defined as the probability of illness (Pi) due to the consumption of pork contaminated with Salmonella, either ESBLs-producing, ciprofloxacin-resistant, or any Salmonella from pork. The dose–response model was employed to define Pi [84] (Equation (5)).

4.8.4. Risk Characterization

The results from the exposure assessment (Pe) and hazard characterization (Pi) were used to estimate the daily probability of illness (Piday) (Equation (6)), assuming that exposure and illness occur independently. Since health data is typically reported in annual units, the annual probability of illness (Piyear) was estimated by using a binomial distribution. The Piyear simulations were performed 10 times with 10,000 iterations, using Monte Carlo methods for risk prediction (Equation (7)).

4.8.5. Evaluation of Four Cooking Conditions

The three risk scenarios representing the consumption of pork contaminated with ESBL-producing, ciprofloxacin-resistant Salmonella or all Salmonella were assessed under four different cooking times at 64 °C: 0 min (uncooked), 1 min, 2 min, and 3 min.

4.8.6. Sensitivity Analysis

The impact of each input variable across different conditions in each scenario was evaluated and factors with high impact on human illness were identified. Spearman rank correlation coefficients were calculated using Monte Carlo simulations with 10,000 iterations.

Author Contributions

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

Funding

This research was funded by the Agricultural Research Department Agency (Public Organization) fiscal year 2020 (POP6305030650) and 2022 (POP6305030660). It was partially supported by the 90th anniversary of Chulalongkorn university fund year 2022 (GCUGR1125652075D) and National Research Council of Thailand (NRCT) Project ID N42A660897.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The genome sequencing and assembly data in this study was available in NCBI database The BioProject number PRJNA946550.

Acknowledgments

We thank Suthep Rungwises, the faculty of veterinary science, Chulalongkorn university for assistance with risk analysis. TK is a recipient of the Second Century Fund (C2F), Chulalongkorn University, 2019.

Conflicts of Interest

The authors were not an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for this journal and were not involved in the editorial review or the decision to publish this article. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
AMRantimicrobial resistance
CFUcolony-forming unit
CLSIclinical and laboratory standards institute
DNAdeoxyribonucleic acid
Ddose of ingestion
ESBLextended-spectrum β-lactamase
HGThorizontal gene transfer
LRlog reduction in Salmonella
MICminimum inhibitory concentration
MPNminiaturized most probable number
Peprobability of exposure
Piprobability of illness
PMQRplasmid-mediated quinolone resistance
QMRAquantitative microbial risk assessment
QRDRquinolone resistance-determining regions
VGTvertical gene transfer
WGSwhole genome sequencing

Appendix A

Table A1. Genome characteristics of Salmonella harboring ESBL and fluoroquinolone resistance.
Table A1. Genome characteristics of Salmonella harboring ESBL and fluoroquinolone resistance.
ID.Genome Size%GC
Content		Genes (n)Proteins (n)Plasmid (n)Plasmid
Replicon		Plasmid ID. Accession No.
SA695,113,36951.9499947183IncHI2/NpSA69-HI2/NNZ_CP121416.1
SA745,113,36951.9500447773IncHI2/NpSA74-HI2/NNZ_JASBCL010000002.1
SA815,062,19352.0500947676IncCpSA81-CNZ_JASBCK010000004.1
SA1055,006,24852.1496346275---
SA1495,141,38051.9499747694IncHI2/NpSA149-HI2/NNZ_CP121411.1
SA1505,138,69651.9499347593IncHI2/NpSA150-HI2/NNZ_CP121407.1
SA1535,138,68951.9499347783IncHI2/NpSA153-HI2/NNZ_CP121403.1
SA1755,188,13452.2515749245IncCpSA175-CNZ_JARVXA010000002.1
IncNpSA175-NNZ_JARVXA010000004.1
SA2315,117,23251.9500347563IncHI2/NpSA231-HI2/NNZ_CP121399.1
SA5105,115,84252.0501548075IncCpSA510-CNZ_CP121393.1
IncFIIpSA510-FNZ_CP121394.1
SA5235,115,84252.0501648085IncCpSA523-CNZ_CP121387.1
IncFIIpSA523-FNZ_CP121388.1
SA4855,115,84252.0501547885IncC pSA485-CNZ_CP121381.1
IncFII pSA485-FNZ_CP121382.1
- indicate not detected.

Appendix B

Table A2. Antimicrobial resistance genes predicted from WGS (n = 12).
Table A2. Antimicrobial resistance genes predicted from WGS (n = 12).
ID.SA69SA74SA81SA105SA149SA150SA153SA175SA231SA510SA523SA485
aac(3)-IId100100.100100100100100100...
aac(3)-VIa.........100100100
aac(6′)-IIa..99.........
aac(6′)-Iaa100100100100100100100100100100100100
aac(6′)-Ib-cr b.........979797
aadA2_9898.98989898.98...
ant(2″)-Ia..100.........
ant(3″)-Ia.........999999
aph(3″)-Ib100100..100100100.100...
aph(3″)-Ib..100....100.100100100
aph(3′)-Ia..100......100100100
aph(6)-Id100100100.100100100100100100100100
blaCMY-2.........100100100
blaCTX-M-55 a100100..100100100100100...
blaTEM-1100100100100...100100100100100
blaVEB-1..100.........
catA2_1.......100....
dfrA12100100.100100100100.100...
erm(42)....100100100.....
floR10010010089100100100100100100100100
fosA7..100......100100100
lnu(G).........100100100
mcr-3.24.......100....
qepA1 b.......100....
qnrS1 b...100100100100100.100100100
sul1100100..100100100.100100100100
sul2..100....100.100100100
tet(A)10010010097.8100100100100100100100100
tet(M)100100.100100100100.100...
tet(X)9999..999999.99...
No. of genes13131291414141313161616
a ESBL gene. b fluoroquinolone resistance genes. Numbers in each cell indicate percent nucleotide identity—indicate not detected gene.

References

  1. WHO. Salmonella (Non-Typhoidal). Factsheets. 2018. Available online: https://www.who.int/news-room/fact-sheets/detail/Salmonella-(non-typhoidal) (accessed on 1 April 2025).
  2. ACFS. Food Consumption Data of Thailand. 2016. Available online: https://www.m-society.go.th/ewtadmin/ewt/mso_web/article_attach/19305/20675.pdf (accessed on 15 April 2025).
  3. OAE. Major Agricultural Commodities, Report and Trend in 2024. 2023. 178p. Available online: https://oae.go.th/uploads/files/2025/08/07/853f981624250b80.pdf (accessed on 7 August 2025).
  4. Chonsin, K.; Changkwanyeun, R.; Siriphap, A.; Intarapuk, A.; Prapasawat, W.; Changkaew, K.; Pulsrikarn, C.; Isoda, N.; Nakajima, C.; Suzuki, Y.; et al. Prevalence and Multidrug Resistance of Salmonella in Swine Production Chain in a Central Province, Thailand. J. Food Prot. 2021, 84, 2174–2184. [Google Scholar] [CrossRef] [PubMed]
  5. Sinwat, N.; Angkittitrakul, S.; Coulson, K.F.; Pilapil, F.; Meunsene, D.; Chuanchuen, R. High prevalence and molecular characteristics of multidrug-resistant Salmonella in pigs, pork and humans in Thailand and Laos provinces. J. Med. Microbiol. 2016, 65, 1182–1193. [Google Scholar] [CrossRef] [PubMed]
  6. O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. 2016. 84p. Available online: https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf (accessed on 31 March 2025).
  7. Van Boeckel, T.P.; Glennon, E.E.; Chen, D.; Gilbert, M.; Robinson, T.P.; Grenfell, B.T.; Levin, S.A.; Bonhoeffer, S.; Laxminarayan, R. Reducing antimicrobial use in food animals. Science 2017, 357, 1350–1352. [Google Scholar] [CrossRef]
  8. Tiseo, K.; Huber, L.; Gilbert, M.; Robinson, T.P.; Van Boeckel, T.P. Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030. Antibiotics 2020, 9, 918. [Google Scholar] [CrossRef]
  9. Samtiya, M.; Matthews, K.R.; Dhewa, T.; Puniya, A.K. Antimicrobial Resistance in the Food Chain: Trends, Mechanisms, Pathways, and Possible Regulation Strategies. Foods 2022, 11, 2966. [Google Scholar] [CrossRef]
  10. Castanheira, M.; Simner, P.J.; Bradford, P.A. Extended-spectrum β-lactamases: An update on their characteristics, epidemiology and detection. JAC-Antimicrob. Resist. 2021, 3, dlab092. [Google Scholar] [CrossRef]
  11. Adel, W.A.; Ahmed, A.M.; Hegazy, Y.; Torky, H.A.; Shimamoto, T. High Prevalence of ESBL and Plasmid-Mediated Quinolone Resistance Genes in Salmonella enterica Isolated from Retail Meats and Slaughterhouses in Egypt. Antibiotics 2021, 10, 881. [Google Scholar] [CrossRef]
  12. Gambino, D.; Gargano, V.; Butera, G.; Sciortino, S.; Pizzo, M.; Oliveri, G.; Cardamone, C.; Piraino, C.; Cassata, G.; Vicari, D.; et al. Food Is Reservoir of MDR Salmonella: Prevalence of ESBLs Profiles and Resistance Genes in Strains Isolated from Food. Microorganisms 2022, 10, 780. [Google Scholar] [CrossRef]
  13. Lay, K.K.; Jeamsripong, S.; Sunn, K.P.; Angkititrakul, S.; Prathan, R.; Srisanga, S.; Chuanchuen, R. Colistin Resistance and ESBL Production in Salmonella and Escherichia coli from Pigs and Pork in the Thailand, Cambodia, Lao PDR, and Myanmar Border Area. Antibiotics 2021, 10, 657. [Google Scholar] [CrossRef]
  14. WHO. Critically Important Antimicrobials for Human Medicine: 6th Revision 2018; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  15. Ren, Z.; Wang, S.; Liu, D.; Yu, J.; Zhang, X.; Zhao, P.; Sun, Y.; Han, S. Control strategies for the vertical gene transfer of quinolone ARGs in Escherichia coli through molecular modification and molecular dynamics. J. Hazard. Mater. 2021, 420, 126667. [Google Scholar] [CrossRef] [PubMed]
  16. Carmosino, I.; Bonardi, S.; Rega, M.; Luppi, A.; Lamperti, L.; Ossiprandi, M.C.; Bacci, C. Evolution of ß-lactams, fluroquinolones and colistin resistance and genetic profiles in Salmonella isolates from pork in northern Italy. Ital. J. Food Saf. 2022, 11, 9972. [Google Scholar] [CrossRef] [PubMed]
  17. CODEX. Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance. 2011, CAC/GL 77-2011. Available online: https://www.fao.org/fao-who-codexalimentarius/sh-proxy/en/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCXG%2B77-2011%252FCXG_077e.pdf (accessed on 30 April 2025).
  18. Collineau, L.; Chapman, B.; Bao, X.; Sivapathasundaram, B.; Carson, C.A.; Fazil, A.; Reid-Smith, R.J.; Smith, B.A. A farm-to-fork quantitative risk assessment model for Salmonella Heidelberg resistant to third-generation cephalosporins in broiler chickens in Canada. Int. J. Food Microbiol. 2020, 330, 108559. [Google Scholar] [CrossRef] [PubMed]
  19. Rortana, C.; Nguyen-Viet, H.; Tum, S.; Unger, F.; Boqvist, S.; Dang-Xuan, S.; Koam, S.; Grace, D.; Osbjer, K.; Heng, T.; et al. Prevalence of Salmonella spp. and Staphylococcus aureus in Chicken Meat and Pork from Cambodian Markets. Pathogens 2021, 10, 556. [Google Scholar] [CrossRef]
  20. Grace, D.; Unger, F.; Cook, M.; Luong, N.T.; Hung, N.V.; Quan, N.V.; Phuc, P.D.; Sinh, D.-X. Salmonella prevalence across different pork value chains in Hanoi, Vietnam. In Proceedings of the SafePork, Berlin, Germany, 26–29 August 2019. [Google Scholar]
  21. Sun, H.; Ling, X.; Li, Y.; Cui, S.; Bai, L. Research on quantitative method and contamination level of Salmonella enterica in raw pork from farmer’s markets in Chengdu. Zhonghua Yu Fang Yi Xue Za Zhi [Chin. J. Prev. Med.] 2021, 55, 999–1005. [Google Scholar] [CrossRef]
  22. Prasertsee, T.; Chokesajjawatee, N.; Santiyanont, P.; Chuammitri, P.; Deeudom, M.; Tadee, P.; Patchanee, P. Quantification and rep-PCR characterization of Salmonella spp. in retail meats and hospital patients in Northern Thailand. Zoonoses Public Health 2019, 66, 301–309. [Google Scholar] [CrossRef]
  23. Nhung, N.T.; Van, N.T.B.; Cuong, N.V.; Duong, T.T.Q.; Nhat, T.T.; Hang, T.T.T.; Nhi, N.T.H.; Kiet, B.T.; Hien, V.B.; Ngoc, P.T.; et al. Antimicrobial residues and resistance against critically important antimicrobials in non-typhoidal Salmonella from meat sold at wet markets and supermarkets in Vietnam. Int. J. Food Microbiol. 2018, 266, 301–309. [Google Scholar] [CrossRef] [PubMed]
  24. Pungpian, C.; Lee, S.; Trongjit, S.; Sinwat, N.; Angkititrakul, S.; Prathan, R.; Srisanga, S.; Chuanchuen, R. Colistin resistance and plasmid-mediated mcr genes in Escherichia coli and Salmonella isolated from pigs, pig carcass and pork in Thailand, Lao PDR and Cambodia border provinces. J. Vet. Sci. 2021, 22, e68. [Google Scholar] [CrossRef]
  25. EFSA. The European Union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2021–2022. EFSA J. 2024, 22, e8583. [Google Scholar] [CrossRef]
  26. Xaymountry, S.; Chukanhom, K.; Klangair, S.; Jiwakanon, J.; Angkititrakul, S. Seasonal and antimicrobial resistance distributions of Salmonella isolated from pork, beef and chicken meat in Vientiane Capital, Lao PDR. Thai J. Vet. Med. 2022, 52, 57–62. [Google Scholar] [CrossRef]
  27. Liu, G.; Qian, H.; Lv, J.; Tian, B.; Bao, C.; Yan, H.; Gu, B. Emergence of mcr-1-Harboring Salmonella enterica Serovar Sinstorf Type ST155 Isolated From Patients with Diarrhea in Jiangsu, China. Front. Microbiol. 2021, 12, 723697. [Google Scholar] [CrossRef]
  28. Wang, H.; Jiang, Y.; Liu, X.; Qian, W.; Xu, X.; Zhou, G. Behavior variability of Salmonella enterica isolates from meat-related sources. Lebensm.-Wiss. Technol. 2016, 73, 375–382. [Google Scholar] [CrossRef]
  29. Nadimpalli, M.; Fabre, L.; Yith, V.; Sem, N.; Gouali, M.; Delarocque-Astagneau, E.; Sreng, N.; Le Hello, S.; BIRDY Study Group. CTX-M-55-type ESBL-producing Salmonella enterica are emerging among retail meats in Phnom Penh, Cambodia. J. Antimicrob. Chemother. 2019, 74, 342–348. [Google Scholar] [CrossRef]
  30. Wang, X.; Wang, B.; Lu, X.; Ma, J.; Wang, Z.; Wang, Y. Prevalence and characteristics of ESBL-producing Salmonella in Weifang, China. Acta Microbiol. Immunol. Hung. 2024, 71, 220–227. [Google Scholar] [CrossRef] [PubMed]
  31. Gonçalves, C.; Silveira, L.; Rodrigues, J.; Furtado, R.; Ramos, S.; Nunes, A.; Pista, Â. Phenotypic and Genotypic Characterization of Escherichia coli and Salmonella spp. Isolates from Pigs at Slaughterhouse and from Commercial Pork Meat in Portugal. Antibiotics 2024, 13, 957. [Google Scholar] [CrossRef]
  32. Poomchuchit, S.; Kerdsin, A.; Chopjitt, P.; Boueroy, P.; Hatrongjit, R.; Akeda, Y.; Tomono, K.; Nuanualsuwan, S.; Hamada, S. Fluoroquinolone resistance in non-typhoidal Salmonella enterica isolated from slaughtered pigs in Thailand. J. Med. Microbiol. 2021, 70, 001386. [Google Scholar] [CrossRef] [PubMed]
  33. Gomes, V.T.M.; Moreno, L.Z.; Silva, A.P.S.; Thakur, S.; La Ragione, R.M.; Mather, A.E.; Moreno, A.M. Characterization of Salmonella enterica Contamination in Pork and Poultry Meat from São Paulo/Brazil: Serotypes, Genotypes and Antimicrobial Resistance Profiles. Pathogens 2022, 11, 358. [Google Scholar] [CrossRef]
  34. Yin, X.; Dudley, E.G.; Pinto, C.N.; M’Ikanatha, N.M. Fluoroquinolone sales in food animals and quinolone resistance in non-typhoidal Salmonella from retail meats: United States, 2009–2018. J. Glob. Antimicrob. Resist. 2022, 29, 163–167. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, Z.; Bai, J.; Zhang, X.; Wang, S.; Chen, K.; Lin, Q.; Xu, C.; Qu, X.; Zhang, H.; Liao, M.; et al. Highly prevalent multidrug resistance and QRDR mutations in Salmonella isolated from chicken, pork and duck meat in Southern China, 2018–2019. Int. J. Food Microbiol. 2021, 340, 109055. [Google Scholar] [CrossRef]
  36. Tao, S.; Chen, H.; Li, N.; Wang, T.; Liang, W. The Spread of Antibiotic Resistance Genes In Vivo Model. Can. J. Infect. Dis. Med. Microbiol. 2022, 2022, 3348695. [Google Scholar] [CrossRef]
  37. Gajdács, M.; Urbán, E.; Stájer, A.; Baráth, Z. Antimicrobial Resistance in the Context of the Sustainable Development Goals: A Brief Review. Eur. J. Investig. Health Psychol. Educ. 2021, 11, 71–82. [Google Scholar] [CrossRef]
  38. Pulsrikarn, C.; Kedsin, A.; Boueroy, P.; Chopjitt, P.; Hatrongjit, R.; Chansiripornchai, P.; Suanpairintr, N.; Nuanualsuwan, S. Quantitative Risk Assessment of Susceptible and Ciprofloxacin-Resistant Salmonella from Retail Pork in Chiang Mai Province in Northern Thailand. Foods 2022, 11, 2942. [Google Scholar] [CrossRef]
  39. Zhang, S.; den Bakker Hendrik, C.; Li, S.; Chen, J.; Dinsmore Blake, A.; Lane, C.; Lauer, A.C.; Fields Patricia, I.; Deng, X. SeqSero2: Rapid and Improved Salmonella Serotype Determination Using Whole-Genome Sequencing Data. Appl. Environ. Microbiol. 2019, 85, e01746-19. [Google Scholar] [CrossRef]
  40. Li, L.; Olsen, R.H.; Xiao, J.; Meng, H.; Peng, S.; Shi, L. Genetic context of blaCTX–M–55 and qnrS1 genes in a foodborne Salmonella enterica serotype Saintpaul isolate from China. Front. Microbiol. 2022, 13, 899062. [Google Scholar] [CrossRef]
  41. Onseedaeng, S.; Ratthawongjirakul, P. Rapid Detection of Genomic Mutations in gyrA and parC Genes of Escherichia coli by Multiplex Allele Specific Polymerase Chain Reaction. J. Clin. Lab. Anal. 2016, 30, 947–955. [Google Scholar] [CrossRef]
  42. Lu, Z.; Zheng, Y.; Wu, S.; Lin, X.; Ma, H.; Xu, X.; Chen, S.; Huang, J.; Gao, Z.; Wang, G.; et al. Antimicrobial Resistance Genes and Clonal Relationships of Duck-Derived Salmonella in Shandong Province, China in 2023. Microorganisms 2024, 12, 2619. [Google Scholar] [CrossRef]
  43. Wang, S.; Wang, S.; Hao, T.; Zhu, S.; Qiu, X.; Li, Y.; Yang, X.; Wu, S. Detection of Salmonella DNA and drug-resistance mutation by PCR-based CRISPR-lbCas12a system. AMB Express 2023, 13, 100. [Google Scholar] [CrossRef]
  44. Hamzah, H.A.; Sirat, R.; Mustafa Mahmud, M.I.A.; Baharudin, R. Mutational Analysis of Quinolone-Resistant Determining Region gyrA and parC Genes in Quinolone-Resistant ESBL-Producing E. Coli. IIUM Med. J. Malays. 2021, 20, 93–101. [Google Scholar] [CrossRef]
  45. Supadej, K.; Nuanmuang, N.; Kummasook, A. Prevalence and antimicrobial resistance of Salmonella isolated from pork in the Northern part of Thailand. J. Assoc. Med. Sci. 2024, 58, 137–149. [Google Scholar] [CrossRef]
  46. Sinwat, N.; Angkittitrakul, S.; Chuanchuen, R. Characterization of Antimicrobial Resistance in Salmonella enterica Isolated from Pork, Chicken Meat, and Humans in Northeastern Thailand. Foodborne Pathog. Dis. 2015, 12, 759–765. [Google Scholar] [CrossRef] [PubMed]
  47. Manik, M.R.K.; Mishu, I.D.; Mahmud, Z.; Muskan, M.N.; Emon, S.Z. Association of fluoroquinolone resistance with rare quinolone resistance-determining region (QRDR) mutations and protein-quinolone binding affinity (PQBA) in multidrug-resistant Escherichia coli isolated from patients with urinary tract infection. J. Infect. Public Health 2025, 18, 102766. [Google Scholar] [CrossRef] [PubMed]
  48. Tacão, M.; Moura, A.; Correia, A.; Henriques, I. Co-resistance to different classes of antibiotics among ESBL-producers from aquatic systems. Water Res. 2014, 48, 100–107. [Google Scholar] [CrossRef]
  49. Joel, E.O.; Akinlabi, O.C.; Olaposi, A.V.; Olowomofe, T.O.; Adekanmbi, A.O. High carriage of plasmid-mediated quinolone resistance (PMQR) genes by ESBL-producing and fluoroquinolone-resistant Escherichia coli recovered from animal waste dumps. Mol. Biol. Rep. 2024, 51, 424. [Google Scholar] [CrossRef]
  50. Kerdsin, A.; Segura, M.; Fittipaldi, N.; Gottschalk, M. Sociocultural Factors Influencing Human Streptococcus suis Disease in Southeast Asia. Foods 2022, 11, 1190. [Google Scholar] [CrossRef]
  51. Zhou, M.; Zhang, N.; Zhang, M.; Ma, G. Culture, eating behavior, and infectious disease control and prevention. J. Ethn. Foods 2020, 7, 40. [Google Scholar] [CrossRef]
  52. MoPH. Thai Consuming Raw Meat Situation in 2024. 2024. Available online: https://hss.moph.go.th/show_topic.php?id=5978 (accessed on 31 August 2025).
  53. CXG 50-2004; General Guidelines on Sampling CXG 50-2004. WHO: Geneva, Switzerland, 2004.
  54. EFSA. Technical specifications on a randomisation of sampling for the purpose of antimicrobial resistance monitoring from food-producing animals and food as from 2021. EFSA J. 2020, 18, e06364. [Google Scholar] [CrossRef]
  55. ISO 6579-1:2017; Microbiology of the Food Chain—Horizontal Method for the Detection, Enumeration and Serotyping of Salmonella Part 1: Detection of Salmonella spp. ISO: Geneva, Switzerland, 2017.
  56. Grimont, P.A.; Weill, F.-X. Antigenic Formulae of the Salmonella Serovars; WHO Collaborating Centre for Reference and Research on Salmonella: Paris, France, 2007; Volume 9, pp. 1–166. [Google Scholar]
  57. ISO/TS 6579-2; Microbiology of Food and Animal Feed—Horizontal Method for the Detection, Enumeration and Serotyping of Salmonella—Part 2: Enumeration by a Miniaturized Most Probable Number Technique. ISO: Geneva, Switzerland, 2012.
  58. Jarvis, B.; Wilrich, C.; Wilrich, P.T. Reconsideration of the derivation of Most Probable Numbers, their standard deviations, confidence bounds and rarity values. J. Appl. Microbiol. 2010, 109, 1660–1667. [Google Scholar] [CrossRef] [PubMed]
  59. ISO 7218:2024; Microbiology of the Food Chain—General Requirements and Guidance for Microbiological Examinations. ISO: Geneva, Switzerland, 2024.
  60. Haas, C.N.; Thayyar-Madabusi, A.; Rose, J.B.; Gerba, C.P. Development of a dose-response relationship for Escherichia coli O157:H7. Int. J. Food Microbiol. 2000, 56, 153–159. [Google Scholar] [CrossRef]
  61. CLSI M100; Performance Standards for Antimicrobial Susceptibility Testing. CLSI: Wayne, PA, USA, 2021.
  62. Lévesque, C.; Piché, L.; Larose, C.; Roy, P.H. PCR mapping of integrons reveals several novel combinations of resistance genes. Antimicrob. Agents Chemother. 1995, 39, 185–191. [Google Scholar] [CrossRef] [PubMed]
  63. Chuanchuen, R.; Padungtod, P. Antimicrobial resistance genes in Salmonella enterica isolates from poultry and swine in Thailand. J. Vet. Med. Sci. 2009, 71, 1349–1355. [Google Scholar] [CrossRef]
  64. Stephenson, S.; Brown, P.D.; Holness, A.; Wilks, M. The emergence of qnr-mediated quinolone resistance among Enterobacteriaceae in Jamaica. West Indian Med. J. 2010, 59, 241–244. [Google Scholar] [CrossRef]
  65. Yamane, K.; Wachino, J.; Suzuki, S.; Arakawa, Y. Plasmid-mediated qepA gene among Escherichia coli clinical isolates from Japan. Antimicrob. Agents Chemother. 2008, 52, 1564–1566. [Google Scholar] [CrossRef]
  66. Park, C.H.; Robicsek, A.; Jacoby, G.A.; Sahm, D.; Hooper, D.C. Prevalence in the United States of aac(6′)-Ib-cr encoding a ciprofloxacin-modifying enzyme. Antimicrob. Agents Chemother. 2006, 50, 3953–3955. [Google Scholar] [CrossRef]
  67. Batchelor, M.; Hopkins, K.; Threlfall, E.J.; Clifton-Hadley, F.A.; Stallwood, A.D.; Davies, R.H.; Liebana, E. bla(CTX-M) genes in clinical Salmonella isolates recovered from humans in England and Wales from 1992 to 2003. Antimicrob. Agents Chemother. 2005, 49, 1319–1322. [Google Scholar] [CrossRef] [PubMed]
  68. Hasman, H.; Mevius, D.; Veldman, K.; Olesen, I.; Aarestrup, F.M. beta-Lactamases among extended-spectrum beta-lactamase (ESBL)-resistant Salmonella from poultry, poultry products and human patients in The Netherlands. J. Antimicrob. Chemother. 2005, 56, 115–121. [Google Scholar] [CrossRef]
  69. Dallenne, C.; Da Costa, A.; Decre, D.; Favier, C.; Arlet, G. Development of a set of multiplex PCR assays for the detection of genes encoding important beta-lactamases in Enterobacteriaceae. J. Antimicrob. Chemother. 2010, 65, 490–495. [Google Scholar] [CrossRef] [PubMed]
  70. Sabaté, M.; Tarragó, R.; Navarro, F.; Miró, E.; Vergés, C.; Barbé, J.; Prats, G. Cloning and Sequence of the Gene Encoding a Novel Cefotaxime-Hydrolyzing β-Lactamase (CTX-M-9) from Escherichia coli in Spain. Antimicrob. Agents Chemother. 2000, 44, 1970–1973. [Google Scholar] [CrossRef] [PubMed]
  71. Pungpian, C.; Sinwat, N.; Angkititrakul, S.; Prathan, R.; Chuanchuen, R. Presence and Transfer of Antimicrobial Resistance Determinants in Escherichia coli in Pigs, Pork, and Humans in Thailand and Lao PDR Border Provinces. Microb. Drug Resist. 2021, 27, 571–584. [Google Scholar] [CrossRef]
  72. De Coster, W.; D’Hert, S.; Schultz, D.T.; Cruts, M.; Van Broeckhoven, C. NanoPack: Visualizing and processing long-read sequencing data. Bioinformatics 2018, 34, 2666–2669. [Google Scholar] [CrossRef]
  73. Andrew, S.; Baele, G.; Vrancken, B.; Suchard, M.A.; Rambaut, A.; Lemey, P. FASTQC. A Quality Control Tool for High Throughput Sequence Data. 2010; Babraham Bioinformatics: Cambridge, UK, 2010. [Google Scholar]
  74. Wick, R.R.; Judd, L.M.; Gorrie, C.L.; Holt, K.E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 2017, 13, e1005595. [Google Scholar] [CrossRef]
  75. Tatusova, T.; DiCuccio, M.; Badretdin, A.; Chetvernin, V.; Nawrocki, E.P.; Zaslavsky, L.; Lomsadze, A.; Pruitt, K.D.; Borodovsky, M.; Ostell, J. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016, 44, 6614–6624. [Google Scholar] [CrossRef]
  76. Florensa, A.F.; Kaas, R.S.; Clausen, P.; Aytan-Aktug, D.; Aarestrup, F.M. ResFinder—An open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb. Genom. 2022, 8, 000748. [Google Scholar] [CrossRef]
  77. Zankari, E.; Allesøe, R.; Joensen, K.G.; Cavaco, L.M.; Lund, O.; Aarestrup, F.M. PointFinder: A novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J. Antimicrob. Chemother. 2017, 72, 2764–2768. [Google Scholar] [CrossRef] [PubMed]
  78. Carattoli, A.; Hasman, H. PlasmidFinder and In Silico pMLST: Identification and Typing of Plasmid Replicons in Whole-Genome Sequencing (WGS). Methods Mol. Biol. 2020, 2075, 285–294. [Google Scholar] [CrossRef]
  79. Grant, J.R.; Enns, E.; Marinier, E.; Mandal, A.; Herman, E.K.; Chen, C.-Y.; Graham, M.; Van Domselaar, G.; Stothard, P. Proksee: In-depth characterization and visualization of bacterial genomes. Nucleic Acids Res. 2023, 51, W484–W492. [Google Scholar] [CrossRef] [PubMed]
  80. Gilchrist, C.L.M.; Chooi, Y.-H. Clinker & clustermap.js: Automatic generation of gene cluster comparison figures. Bioinformatics 2021, 37, 2473–2475. [Google Scholar] [CrossRef] [PubMed]
  81. Kaas, R.S.; Leekitcharoenphon, P.; Aarestrup, F.M.; Lund, O. Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms. PLoS ONE 2014, 9, e104984. [Google Scholar] [CrossRef]
  82. Letunic, I.; Bork, P. Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024, 52, W78–W82. [Google Scholar] [CrossRef]
  83. FAO. Risk Assessments of Salmonella in Eggs and Broiler Chickens; Food and Agriculture Organization: Rome, Italy, 2002; Volume 2. [Google Scholar]
  84. FAO/WHO. Hazard Characterization for Pathogens in Food and Water GUIDELINES; FAO: Geneva, Switzerland, 2003. [Google Scholar]
Antibiotics 14 01198 g001
Figure 1. Resistance rates of Salmonella and ESBL-producing Salmonella. Each antibiotic (abbreviation in three letters) shows resistance rate in percentage among Salmonella (Antibiotics 14 01198 i001) (n = 531) and ESBL-producing Salmonella (Antibiotics 14 01198 i002) (n = 150). CEF-TAZ stands for resistance to both cefotaxime and ceftazidime as screening of ESBL-producing. MDR stands for resistance to more than three groups of antibiotics.
Figure 1. Resistance rates of Salmonella and ESBL-producing Salmonella. Each antibiotic (abbreviation in three letters) shows resistance rate in percentage among Salmonella (Antibiotics 14 01198 i001) (n = 531) and ESBL-producing Salmonella (Antibiotics 14 01198 i002) (n = 150). CEF-TAZ stands for resistance to both cefotaxime and ceftazidime as screening of ESBL-producing. MDR stands for resistance to more than three groups of antibiotics.
Antibiotics 14 01198 g001
Antibiotics 14 01198 g002
Figure 2. Circular alignment of plasmid replicons. (A) Comparison of IncHI2/N plasmids (pSA69-HI2/N, pSA74-HI2/N, pSA149-HI2/N, pSA150-HI2/N, pSA153-HI2/N, and pSA231-HI2/N) from this study with reference plasmids p65 (pig, USA), pD208 (human, Thailand), p2017_03_03CC (human, Thailand), and pLAO65 (human, Lao PDR) revealed two AMR clusters including “tet(M)-blaTEM-1-blaCTX-M-55-aac(3)-IId-dfrA12-aadA2-qacE-sul1” and “aph(3″)Ib-aph(6)Id-tet(A)-floR-tet”. The first region, containing blaTEM-1 and blaCTX-M-55, was conserved across all plasmids, while the second region showed variation, particularly in p65. (B) IncN plasmid pSA175-N carried blaTEM-1. (C) Comparison of IncC plasmids (pSA510-C, pSA523-C, pSA485-C, pSA81-C, and pSA175-C) from this study with plasmids from different geographic regions and hosts including pSNE3-1928 (human, USA), pSB109 (pig, Spain), and pFSIS11809860-1 (pig, USA) revealed that the tet(A), floR and aph(6)-Id gene cluster was present in all plasmids. (D) IncF plasmids pSA510-F, pSA523-F, and pSA485-F from this study carried virulence-associated genes (vir).
Figure 2. Circular alignment of plasmid replicons. (A) Comparison of IncHI2/N plasmids (pSA69-HI2/N, pSA74-HI2/N, pSA149-HI2/N, pSA150-HI2/N, pSA153-HI2/N, and pSA231-HI2/N) from this study with reference plasmids p65 (pig, USA), pD208 (human, Thailand), p2017_03_03CC (human, Thailand), and pLAO65 (human, Lao PDR) revealed two AMR clusters including “tet(M)-blaTEM-1-blaCTX-M-55-aac(3)-IId-dfrA12-aadA2-qacE-sul1” and “aph(3″)Ib-aph(6)Id-tet(A)-floR-tet”. The first region, containing blaTEM-1 and blaCTX-M-55, was conserved across all plasmids, while the second region showed variation, particularly in p65. (B) IncN plasmid pSA175-N carried blaTEM-1. (C) Comparison of IncC plasmids (pSA510-C, pSA523-C, pSA485-C, pSA81-C, and pSA175-C) from this study with plasmids from different geographic regions and hosts including pSNE3-1928 (human, USA), pSB109 (pig, Spain), and pFSIS11809860-1 (pig, USA) revealed that the tet(A), floR and aph(6)-Id gene cluster was present in all plasmids. (D) IncF plasmids pSA510-F, pSA523-F, and pSA485-F from this study carried virulence-associated genes (vir).
Antibiotics 14 01198 g002
Antibiotics 14 01198 g003
Figure 3. Genomic comparison of IncHI2/N plasmids highlighting conserved MDR region containing blaCTX-M-55. Homologous sequences are shown and linked in the same color. Sequences from IncHI2/N plasmid pSA69-HI2/N, pSA74-HI2/N, pSA149-HI2/N, pSA150-HI2/N, pSA153-HI2/N, and pSA231-HI2/N in this study were aligned with reference plasmids from p65, pD208, p2017_03_03CC, and pLAO65. Genes highlighted in red font represent AMR genes. The ESBL gene blaCTX-M-55 is indicated by red arrow. The direction of the arrow indicates the direction of gene transcription.
Figure 3. Genomic comparison of IncHI2/N plasmids highlighting conserved MDR region containing blaCTX-M-55. Homologous sequences are shown and linked in the same color. Sequences from IncHI2/N plasmid pSA69-HI2/N, pSA74-HI2/N, pSA149-HI2/N, pSA150-HI2/N, pSA153-HI2/N, and pSA231-HI2/N in this study were aligned with reference plasmids from p65, pD208, p2017_03_03CC, and pLAO65. Genes highlighted in red font represent AMR genes. The ESBL gene blaCTX-M-55 is indicated by red arrow. The direction of the arrow indicates the direction of gene transcription.
Antibiotics 14 01198 g003
Antibiotics 14 01198 g004
Figure 4. Phylogenetic tree by WGS analysis among Salmonella isolates (n = 18). Chromosomal sequences of twelve Salmonella SA69, SA74, SA81, SA105, SA149, SA150, SA153, SA175, SA231, SA485, SA510, and SA523 from pork in this study were aligned with S. Agona R21.2429 (human, Taiwan) S. Kentucky SSSE-136 (human, Thailand) and S. Agona 8878_Sal21 (pig, Thailand). The isolates clustered into five distinct clades: clade 1, SA153; clade 2, SA175; clade 3, SA149; clade 4, SA69, SA231, and SA74; and clade 5, SA105, SA150, SA523, SA81, SA485, and SA510. The numerical values displayed on the branches represent branch lengths, scaled to the number of substitutions per site.
Figure 4. Phylogenetic tree by WGS analysis among Salmonella isolates (n = 18). Chromosomal sequences of twelve Salmonella SA69, SA74, SA81, SA105, SA149, SA150, SA153, SA175, SA231, SA485, SA510, and SA523 from pork in this study were aligned with S. Agona R21.2429 (human, Taiwan) S. Kentucky SSSE-136 (human, Thailand) and S. Agona 8878_Sal21 (pig, Thailand). The isolates clustered into five distinct clades: clade 1, SA153; clade 2, SA175; clade 3, SA149; clade 4, SA69, SA231, and SA74; and clade 5, SA105, SA150, SA523, SA81, SA485, and SA510. The numerical values displayed on the branches represent branch lengths, scaled to the number of substitutions per site.
Antibiotics 14 01198 g004
Antibiotics 14 01198 g005
Figure 5. The probability of illness from consuming pork contaminated with Salmonella per 100,000 population per year. The lines show trends of probability of illness (mean) sorted by cooking time (0, 1, 2 and 3 min) including three scenarios: antimicrobial-resistant risk assessment of ESBL-producing Salmonella (orange line), ciprofloxacin-resistant Salmonella (blue line), and QMRA Salmonella (gray line).
Figure 5. The probability of illness from consuming pork contaminated with Salmonella per 100,000 population per year. The lines show trends of probability of illness (mean) sorted by cooking time (0, 1, 2 and 3 min) including three scenarios: antimicrobial-resistant risk assessment of ESBL-producing Salmonella (orange line), ciprofloxacin-resistant Salmonella (blue line), and QMRA Salmonella (gray line).
Antibiotics 14 01198 g005
Antibiotics 14 01198 g006
Figure 6. Risk assessment (RA) model of Salmonella-contaminated pork (n = 793) in 3 scenarios. Scenario 1: RA for ESBL-producing Salmonella (n = 127), Scenario 2: RA for ciprofloxacin-resistant Salmonella (n = 25), Scenario 3: RA for Salmonella (n = 339).
Figure 6. Risk assessment (RA) model of Salmonella-contaminated pork (n = 793) in 3 scenarios. Scenario 1: RA for ESBL-producing Salmonella (n = 127), Scenario 2: RA for ciprofloxacin-resistant Salmonella (n = 25), Scenario 3: RA for Salmonella (n = 339).
Antibiotics 14 01198 g006
Table 1. Mutations in gyrA and parC of fluoroquinolone-resistant Salmonella (n = 31).
Table 1. Mutations in gyrA and parC of fluoroquinolone-resistant Salmonella (n = 31).
IDQRDR a Mutation in Gene (Amino Acid Change)PMQR a GeneResistance Pattern (n)
gyrA(GyrA)parC(ParC)
1C248T(Ser83Phe)ND aqnrSCIP a,b-LEV (1)
qnrS-aac(6′)-Ib-crCIP a,b-LEV (2)
NDCIP (6) c, CIP c-LEV (5)
2C248T(Ser83Phe)G293T(Ser80Ile)NDCIP a-LEV (2)
G259A(Asp87Asn)
3C248A(Ser83Tyr)NDqnrSCIP b-LEV (1) LEV (1)
4NDNDqnrSCIP b (7)
NDCIP b (2), CIP c-LEV (2)
a abbreviation: CIP, ciprofloxacin; LEV, levofloxacin; PMQR, Plasmid-Mediated Quinolone Resistance; QRDR, Quinolone Resistance-Determining Region. ND, not detected. b CIP MIC, 16 µg/mL. c CIP MIC, 4–16 µg/mL.
Table 2. Characteristics of ESBL-producing Salmonella donors and transconjugants in conjugation experiments (n = 20).
Table 2. Characteristics of ESBL-producing Salmonella donors and transconjugants in conjugation experiments (n = 20).
ID.SerovarsDonors Transconjugants
Resistance Pattern aESBL GenesResistance PatternESBL Genes
SA149WandsworthAMP-AZI-CIP-CRO-FEP-FFC-FOT-GEN-NAL-OXA-STR-TAZblaCTX-M-55AMP-AZI-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZ bblaCTX-M-55
SA150WandsworthAMP-AZI-CIP-CRO-FEP-FFC-FOT-GEN-NAL-OXA-STR-TAZblaCTX-M-55AMP-AZI-CRO-FEP-FFC-FOT-GEN-OXA-STR bblaCTX-M-55
SA24RissenAMP-CRO-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA75GiveAMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA82GiveAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA90SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA113SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA173SinstorfAMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA181SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-AZI-CRO-FFC-FOT-GEN-OXA-STR bblaCTX-M-55
SA202SinstorfAMP-CRO-FFC-FOT-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-OXA-STR-TAZblaCTX-M-55
SA219SinstorfAMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-AZI-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZ bblaCTX-M-55
SA296GiveAMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA316SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA342SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA365SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-AZI-CRO-FFC-FOT-GEN-OXA-STR-TAZ bblaCTX-M-55
SA398SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA435SinstorfAMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FEP-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA467SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
SA480SinstorfAMP-CRO-FFC-FOT-OXA-STRblaCTX-M-55AMP-CRO-FFC-FOT-OXA-STRblaCTX-M-55
SA519SinstorfAMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55AMP-CRO-FFC-FOT-GEN-OXA-STR-TAZblaCTX-M-55
a Abbreviation: AMP, ampicillin; AZI, azithromycin; CIP, ciprofloxacin; CRO, ceftriaxone; FEP, cefepime; FFC, florfenicol; FOT, cefotaxime; FOX, cefoxitin; GEN, gentamicin; NAL, nalidixic acid; OXA, oxacillin; STR, streptomycin; TAZ, ceftazidime. b Difference resistance profile observed of transconjugant compared to donor.
Table 3. Characteristics of MDR ESBL-producing and fluoroquinolone-resistant Salmonella isolates based on WGS analysis (n = 12).
Table 3. Characteristics of MDR ESBL-producing and fluoroquinolone-resistant Salmonella isolates based on WGS analysis (n = 12).
ID.Serovar cResistance Pattern bAMR GeneQRDR MutationsLocalization
Determined by ASTPredicted by WGSgyrAparC
SA69KentuckyAMP-CIP-CRO-FFC-FEP-FOT-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMP-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-DKB-DOX-FOT-GEN-MNO-NET-PIP-SIS-SPT-STR-TCY-TIC-TGC-TOBaadA2, aac(6′)-Iaa, aph(3″)-Ib, florR, blaTEM-1, blaCTX-M-55, tet--pSA69-HI2/N
-G259A, C248TG239T, C170Gchromosome
SA74KentuckyAMP-AZI-CIP-CRO-FFC-FEP-FOT-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMP-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-DKB-DOX-FOT-GEN-MNO-NET-PIP-SIS-SPT-STR-SMX-TCY-TIC-TGC-TOB-TMPflorR, tet, blaTEM-1, blaCTX-M-55, aac(6′)-Iaa, aac(6′)-IIb, aph(3′)-Ib, sul1, dfrA, aadA--pSA74-HI2/N
-G259A, C248TG239T, C170Gchromosome
SA81DerbyAMP-AZI-CIP-CRO-FFC-FOT-LEV-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-ATM-FEP-FOT-FOX-TAZ-CEP-CHL-CIP-DKB-DOX-FOT-FOS-GEN-KAN-NAL-NEO-NET-PAR-PIP-TZP-RST-SIS-STR-SMX-TCY-TIC-TCC-TOBflorR, tet, blaVEB, blaTEM-1, sul2, aph(3′)-Ia, aac(6′)-IIa,--pSA81-C
-C248TC170Gchromosome
SA105StanleyAMP-CIP-CRO-FFC-FEP-FOT-GEN-NAL-OXA-STR-TAZAMK-AMX-AMP-APR-CEP-CHL-CIP-DKB-DOX-FOT-GEN-MNO-NET-PIP-SIS-SPT-STR-TCY-TIC-TOB-TMPqnrS, acc(3′)-IId, acc(6′)IIa, aadA2, tet, blaTEM-1, florR, dfrA---
--C170Gchromosome
SA149 aWandsworthAMP-AZI-CIP-CRO-FFC-FEP-FOT-GEN-NAL-OXA-STR-TAZAMK-AMP-AMP+C-FEP-FOT-TAZ-CHL-CIP-GEN-SMX-TCY-TGC-TOB-TMPacc(3′)-IId, acc(6′)Iaa, florR, blaTEM, dfrA, sul1, qnrS, tet--pSA149-HI2/N
--C170Gchromosome
SA150 aWandsworthAMP-AZI-CIP-CRO-FFC-FEP-FOT-GEN-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-CIP-CLI-DKB-DOX-ERY-FOT-GEN-LIN-MNO-NET-PIP-TZP-PRI-QDA-SIS-SPT-STR-SMX-TCY-TIC-TCC-TGC-TOB-TMPaadA2, aph(6′)-Id, aac(3′)-IId, aac(6′)-Iaa, floR, blaCTX-M-55, sul1, dfrA, erm, qnrS, tet--pSA150-HI2/N
--C170Gchromosome
SA153MonsAMP-AZI-CIP-CRO-FFC-FEP-FOT-FOX-GEN-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-CIP-CLI-DKB-DOX-ERY-FOT-GEN-LIN-MNO-NET-PIP-TZP-PRI-QDA-SIS-SPT-STR-SMX-TCY-TIC-TCC-TGC-TOB-TMPblaTEM-1, sul, dfrA, erm, aadA2, floR, qacE, erm, tet(M), qnrS, aac(3′)-IId, aph(3′)-Ib, aac(3′)-Iaa--pSA153-HI2/N
--C170Gchromosome
SA175TyphimuriumAMP-CIP-CRO-FFC-FEP-FOT-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMP-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-CIP-COL-DKB-DOX-FOT-GEN-NAL-NET-PIP-SIS-STR-SMX-TCY-TIC-TOBmcr, tet, blaCTX-M-55, qepA, qnrS, catA, sul, floR, aac(3′)IId, aph(3′)Ib, aac(6′)Iaa--pSA175-C
blaTEM-1--pSA175-N
-C248A-chromosome
SA231KentuckyAMP-CIP-CRO-FFC-FEP-FOT-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMP-APR-ATM-FEP-FOT-TAZ-CRO-CEP-CHL-CIP-DKB-DOX-FOT-GEN-MNO-NAL-NET-PIP-SIS-SPT-STR-SMX-TCY-TIC-TGC-TOB-TMPblaTEM-1, blaCTX-M-55, floR, qacE, sul1, dfrA, aadA2, aac(3)IId, aadA2, aph(3′)Ib, aac(6′)Iaa, tet.--pSA231-HI2/N
-G259A, C248TG239T, C170Gchromosome
SA510AgonaAMP-CIP-CRO-FFC-FOT-FOX-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-FOT-FOX-TAZ-CEP-CHL-CIP-DKB-DOX-FOT-FOR-FOS-GEN-KAN-LIN-NAL-NEO-NET-PIP-TZP-SIS-SPT-STR-SMX-TCY-TIC-TCC-TOBaph(3′)Ia, aac(6′)Ib, aac(3)VIa, aph(6)Id, tet, sul2, floR, blaTEM-1, blaCMY, fosA, aadA1, Inu--pSA510-C
---pSA510-F
-C248TC170Gchromosome
SA523AgonaAMP-AZI-CIP-CRO-FFC-FOT-FOX-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-FOT-FOX-TAZ-CEP-CHL-CIP-DKB-DOX-FOT-FOR-FOS-GEN-KAN-LIN-NAL-NEO-NET-PIP-TZP-SIS-SPT-STR-SMX-TCY-TIC-TCC-TOBInu, sul2, aac(6′)Ib, aph(3′)Ia, aadA1, aac(3)VIa, blaTEM-1, blaCMY, qnrS, fosA, floR, tet--pSA523-C
---pSA523-F
-C248TC170Gchromosome
SA485Agona cAMP-AZI-CIP-FFC-FOT-FOX-GEN-LEV-NAL-OXA-STR-TAZAMK-AMX-AMC-AMP-AMP+C-FOT-FOX-TAZ-CEP-CHL-CIP-DKB-DOX-FOT-FOR-FOS-GEN-KAN-LIN-NAL-NEO-NET-PIP-TZP-SIS-SPT-STR-SMX-TCY-TIC-TCC-TOBfloR, aac(6′)Ib, aadA1, sul2, tet, blaCMY, blaTEM-1, fosA, Inu, aac(6′)Iaa, aac(3)VIa, aac(6′)Ib, aph(3′)Ia--pSA485-C
---pSA485-F
-C248TC170Gchromosome
a Capable of conjugative transfer, b Abbreviation: AMR, amikacin; AMX, amoxicillin; AMC, amoxicillin+clavulanic acid; AMP, ampicillin; AMP+C, ampicillin/clavulanic acid; APR, apramycin; ATM, Aztreonam; AZI, azithromycin; CEP, cephalothin; CHL, chloramphenicol; CIP, ciprofloxacin; CRO, ceftriaxone; DKB, dibekacin; DOX, doxycycline; FEP, cefepime; FFC, florfenicol; FOT, cefotaxime; FOS, fosfomycin; FOX, cefoxitin; GEN, gentamicin; KAN, kanamycin; LIN, lincomycin; MNO, minocycline; NAL, nalidixic acid; NEO, neomycin; NET, netilmicin; OXA, oxacillin; PIP, piperacillin; QSA, Quinupristin; RST, ribostamycin; SIS, sisomicin; SMX, sulfamethoxazole; SPT, spectinomycin; STR, streptomycin; TAZ, ceftazidime; TCY, tetracycline; TCC, ticarcillin/clavulanic acid; TIC, ticarcillin; TGC, tigecycline; TMP, trimethoprim; TOB, tobramycin; TZP, piperacillin/tazobactam. c Non-ESBL producer for comparison.
Table 4. Probability of exposure, probability of illness, and annual risk of illness per person.
Table 4. Probability of exposure, probability of illness, and annual risk of illness per person.
StatisticsProbability of Exposure (Pe)Probability of Illness (Pi)Annual Risk of Illness per Person
Cooking Time at 64 °C
Uncooked1 min b2 min3 minUncooked1 min2 min3 minUncooked1 min2 min3 min
Scenario 1: ESBL-producing Salmonella
Mean9.15 × 10−1 a1.98 × 10−17.96 × 10−36.65 × 10−55.06 × 10−27.61 × 10−42.09 × 10−51.77 × 10−78.85 × 10−19.49 × 10−22.52 × 10−41.77 × 10−8
Std Dev2.08 × 10−12.23 × 10−11.34 × 10−21.11 × 10−45.29 × 10−21.29 × 10−33.57 × 10−53.15 × 10−72.61 × 10−11.842 × 10−11.29 × 10−38.90 × 10−8
Min c4.19 × 10−51.28 × 10−52.13 × 10−71.55 × 10−99.00 × 10−76.80 × 10−96.57 × 10−111.31 × 10−139.59 × 10−81.52 × 10−100.00 × 1000.00 × 100
Max d19.99 × 10−12.17 × 10−11.82 × 10−33.57 × 10−12.58 × 10−25.44 × 10−45.71 × 10−60 9.98 × 10−14.00 × 10−22.86 × 10−6
Scenario 2: Ciprofloxacin-resistant Salmonella
Mean7.11 × 10−13.99 × 10−28.16 × 10−33.01 × 10−41.15 × 10−21.15 × 10−42.12 × 10−57.44 × 10−76.35 × 10−15.70 × 10−32.29 × 10−43.25 × 10−7
Std Dev3.31 × 10−15.96 × 10−21.39 × 10−25.30 × 10−41.64 × 10−22.07 × 10−43.78 × 10−51.67 × 10−113.88 × 10−12.31 × 10−21.09 × 10−31.50 × 10−6
Min6.22 × 10−52.64 × 10−72.38 × 10−82.96 × 10−92.80 × 10−84.20 × 10−91.24 × 10−101.67 × 10−111.99 × 10−108.10 × 10−140.00 × 1000.00 × 100
Max16.79 × 10−12.55 × 10−17.63 × 10−31.88 × 10−14.83 × 10−37.06 × 10−42.29 × 10−505.19 × 10−14.40 × 10−24.38 × 10−5
Scenario 3: Salmonella
Mean 9.13 × 10−1 4.03 × 10−1 7.51 × 10−3 6.41 × 10−5 5.10 × 10−2 7.62 × 10−4 1.96 × 10−5 1.64 × 10−7 9.53 × 10−1 2.82 × 10−12.15 × 10−4 1.59 × 10−8
Std Dev 2.10 × 10−1 3.25 × 10−1 1.24 × 10−2 1.15 × 10−4 5.37 × 10−2 1.31 × 10−3 3.41 × 10−5 2.80 × 10−7 1.71 × 10−1 3.32 × 10−1 1.13 × 10−3 1.13 × 10−7
Min8.99 × 10−4 2.02 × 10−6 8.25 × 10−8 3.03 × 10−10 8.57 × 10−7 3.42 × 10−9 1.57 × 10−11 1.82 × 10−13 6.22 × 10−6 2.08 × 10−11 0 0
Max 11 2.00 × 10−1 2.35 × 10−3 3.39 × 10−1 2.16 × 10−2 5.37 × 10−4 4.95 × 10−6 115.27 × 10−2 7.06 × 10−1
a E, exponent notation representing ×10^. b min, minute. c min, minimum d max, maximum.
Table 5. Sensitivity correlation or regression values of risk model.
Table 5. Sensitivity correlation or regression values of risk model.
Conditions
(Cooking Time/min c)		Regression (R)
Concentration (CFU/g) aConsumption (g/day) b
Scenario 1 ESBL-producing Salmonella
uncooked0.3060.309
1 min0.5840.593
2 min0.3830.380
3 min0.3930.375
Scenario 2 Ciprofloxacin-resistant Salmonella
uncooked0.4880.475
1 min0.4240.435
2 min0.3830.382
3 min0.3990.404
Scenario 3 Salmonella
uncooked0.4880.475
1 min0.4240.435
2 min0.3830.382
3 min0.3990.404
a CFU/g, colony-forming unit/gram, b g/day, gram per day, c min, minute.
Table 6. Primers used in this study.
Table 6. Primers used in this study.
TargetsPrimersAmplicon
Size (bp)		Primer Sequence (5′–3′)References
QRDR
gyrAgyrA-F436GCTGAAGAGCTCCTATCTGGChuanchuen and Padungtod [63]
gyrA-R GGTCGGCATGACGTCCGG
parCparC-F390GTACGTGATCATGGATCGTGChuanchuen and Padungtod [63]
parC-R TTCCTGCATGGTGCCGTCG
PMQR
qnrAqnrA-F516ATTTCTCACGCCAGGATTTGStephenson, et al. [64]
qnrA-R GATCGGCAAAGGTTAGGTCA
qnrBqnrB-F469GATCGTGAAAGCCAGAAAGGStephenson, Brown, Holness and Wilks [64]
qnrB-R ACGATGCCTGGTAGTTGTCC
qnrSqnrS-F417ACGACATTCGTCAACTGCAAStephenson, Brown, Holness and Wilks [64]
qnrS-R TAAATTGGCACCCTGTAGGC
qepAqepA-F199GCAGGTCCAGCAGCGGGTAGYamane, et al. [65]
qepA-R CTTCCTGCCCGAGTATCGTG
aac(6′)-Ib-craac(6′)-Ib-F482TTGCGATGCTCTATGAGTGGCTAPark, et al. [66]
aac(6′)-Ib-R CTCGAATGCCTGGCGTGTTT
ESBLs
blaCTX-MblaCTX-M-F585CGATGTGCAGTACCAGTAABatchelor, et al. [67]
blaCTX-M-R AGTGACCAGAATCAGCGG
blaTEMblaTEM-F964GCGGAACCCCTATTTHasman, et al. [68]
blaTEM-R TCTAAAGTATATATGAGTAAACTTGGTCT
blaSHVblaSHV-F854TTCGCCTGTGTATTATCTCCCTGHasman, Mevius, Veldman, Olesen and Aarestrup [68]
blaSHV-R TTAGCGTTGCCAGTGYTG
blaCTXM
subgroup 1 a		CTXMgr1-F688TTAGGAARTGTGCCGCTGYA bDallenne, et al. [69]
CTXMgr1-R CGATATCGTTGGTGGTRCCAT b
blaCTXM
subgroup 2 c		CTXMgr2-F404CGTTAACGGCACGATGACDallenne, Da Costa, Decre, Favier and Arlet [69]
CTXMgr2-R CGATATCGTTGGTGGTRCCAT b
blaCTXM
subgroup 8/25 d		CTX-M gr.8/25-F326AACRCRCAGACGCTCTAC bDallenne, Da Costa, Decre, Favier and Arlet [69]
CTX-M gr.8/25-R TCGAGCCGGAASGTGTYAT b
blaCTXM
subgroup 9 e		CTX-M gr.9-F850GTGACAAAGAGAGTGCAACGGSabaté, et al. [70]
CTX-M gr.9-R ATGATTCTCGCCGCTGAAGCC
a Group 1 includes blaCTX-M-1, -3, -10 to -12, -15, -22, -23, -28, -29, and -30. b Y = T or C; R = A or G; S = G or C; D = A, G, or T. c Group 2 includes blaCTX-M-2, -4 to -7, and -20. d Group 8/25 includes blaCTX-M-8, CTX-M-25, CTX-M-26, and CTX-M-39 to CTX-M-41. e Group 9 includes blaCTX-M-9, -14, -16 to -19, -21, and -27.
Table 7. Risk assessment equations.
Table 7. Risk assessment equations.
Eq.EquationDescription
(1) L R = t D R 64 LR: log reduction
t: cooking time at 0, 1, 2, and 3 min
DR64: decimal reduction time at 64 °C for Salmonella with the value of 0.48
(2) R C c = C c   ×   10 − L R RCc: remaining concentration of Salmonella in pork after cooking at 64 °C
Cc: concentration of Salmonella in pork (CFU/gram)
(3) D = R C c     ×   C s D: dose of ingestion (CFU/day)
C s : daily consumption of pork
(4) P e = 1 − e − D Pe: probability of consuming at least 1 CFU of Salmonella from pork
e : Euler’s constant = 2.71828182845904
(5) P i = 1 − 1 + D 51.45 − 0.1324 Pi: probability of illness
(6) P i d a y = P i   ×   P e Pi day: probability of illness per day
(7) P i y e a r = 1 − 1 − P i d a y 365 Pi year: probability of illness per year
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kiatyingangsulee, T.; Hein, S.T.; Prathan, R.; Srisanga, S.; Jeamsripong, S.; Chuanchuen, R. Integrated Genetic Characterization and Quantitative Risk Assessment of Cephalosporin- and Ciprofloxacin-Resistant Salmonella in Pork from Thailand. Antibiotics 2025, 14, 1198. https://doi.org/10.3390/antibiotics14121198

AMA Style

Kiatyingangsulee T, Hein ST, Prathan R, Srisanga S, Jeamsripong S, Chuanchuen R. Integrated Genetic Characterization and Quantitative Risk Assessment of Cephalosporin- and Ciprofloxacin-Resistant Salmonella in Pork from Thailand. Antibiotics. 2025; 14(12):1198. https://doi.org/10.3390/antibiotics14121198

Chicago/Turabian Style

Kiatyingangsulee, Thawanrut, Si Thu Hein, Rangsiya Prathan, Songsak Srisanga, Saharuetai Jeamsripong, and Rungtip Chuanchuen. 2025. "Integrated Genetic Characterization and Quantitative Risk Assessment of Cephalosporin- and Ciprofloxacin-Resistant Salmonella in Pork from Thailand" Antibiotics 14, no. 12: 1198. https://doi.org/10.3390/antibiotics14121198

APA Style

Kiatyingangsulee, T., Hein, S. T., Prathan, R., Srisanga, S., Jeamsripong, S., & Chuanchuen, R. (2025). Integrated Genetic Characterization and Quantitative Risk Assessment of Cephalosporin- and Ciprofloxacin-Resistant Salmonella in Pork from Thailand. Antibiotics, 14(12), 1198. https://doi.org/10.3390/antibiotics14121198

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop