Samples in this study were collected as part of the routine NARMS retail meat testing program. From January to December 2018, a total of 958 fresh samples consisting of 478 skin-on/bone-in chicken, 240 ground turkey, 120 pork chop, and 120 ground beef were purchased from retail stores in California twice each month. Sampling locations were selected based on randomization of grocery stores by zip codes in northern (City and County of San Francisco, Contra Costa County, and Alameda County) and southern California (West Los Angeles, East Los Angeles, Ontario, and Irvine). A variety of meat types and cuts from different brands were purchased at each store. Packaging of samples in this study included modified atmospheric packaging (MAP), plastic bag, vacuum sealed, chub, paper wrapped, and plastic film packaging. Samples were transported on ice to the laboratory, refrigerated, and processed within 72 h of purchase.

Samples were processed per the NARMS Retail Meat Surveillance protocol. Briefly, 25 g of each sample in 250 ml buffered peptone water (Becton Dickinson, Franklin Lakes, NJ, United States) was hand massaged for 3 min or placed on a mechanical shaker at 200 rpm for 15 min. Fifty milliliters of rinsate was added to 50 ml of double-strength lactose broth (Becton Dickinson, Franklin Lakes, NJ, United States) and incubated at 35°C for 24 h. After overnight enrichment, 0.1 ml of lactose broth was transferred to 9.9 ml Rappaport-Vassiliadis R10 (RVR10) broth (Becton Dickinson, Franklin Lakes, NJ, United States) and incubated at 42°C for 16–20 h. The RVR10 enrichments were then streaked onto XLT-4 (Remel, Lenexa, KS, United States) and Hektoen Enteric (Becton Dickinson, Franklin Lakes, NJ, United States) agars and incubated at 35°C for 18–24 h. Up to two suspect Salmonella colonies based on colony morphology from each selective agar were then streaked to purity on blood agar plates. Isolates were shipped on dry ice to the FDA’s Center for Veterinary Medicine (CVM) for antimicrobial susceptibility testing and WGS.

Salmonella isolates were tested using a broth microdilution method for 14 antimicrobial drugs using the NARMS Gram-negative plates (Thermo Fisher Scientific, Waltham, MA, United States) per standard protocols (Food and Drug Administration [FDA], 2016). Minimum inhibitory concentration (MIC) values for each drug were used to classify isolates based on the Clinical and Laboratory Standards Institute (CLSI) guidelines. NARMS consensus interpretive criteria were used for streptomycin and azithromycin, due to unavailability of CLSI breakpoints for these two drugs (Food and Drug Administration [FDA], 2021b). Breakpoints used to classify resistant isolates for each antimicrobial drug were as follows: amoxicillin/clavulanate (≥32/16 μg/ml), ampicillin (≥32 μg/ml), azithromycin (≥32 μg/ml), cefoxitin (≥32 μg/ml), ceftriaxone (≥4 μg/ml), chloramphenicol (≥32 μg/ml), ciprofloxacin (≥1 μg/ml), gentamicin (≥16 μg/ml), meropenem (≥4 μg/ml), nalidixic acid (≥32 μg/ml), streptomycin (≥32 μg/ml), sulfisoxazole (≥512 μg/ml), tetracycline (≥16 μg/ml), and trimethoprim-sulfamethoxazole (≥4/76 μg/ml). Phenotypic resistance was presented as resistant isolates, with intermediate and susceptible isolates grouped together for analysis. Multidrug resistance was defined as resistance to ≥1 drug in ≥3 antimicrobial classes (Magiorakos et al., 2012). Due to the significance of ciprofloxacin for salmonellosis treatment and the expansion of CLSI criteria for its intermediate susceptibility range, reduced susceptibility to ciprofloxacin was also noted (≥0.12 μg/ml) (Food and Drug Administration [FDA], 2021b).

Salmonella isolates were streaked to blood agar plates, and pure colonies were extracted from overnight cultures per manufacturer’s protocol using the Qiagen DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, United States). DNA purity and quantification was assessed using the NanoDrop and Qubit fluorometer, respectively. Libraries were prepared using the Illumina Nextera XT kit per manufacturer’s protocol with quality control and quantification done on the Bioanalyzer and Qubit. Final libraries were sequenced using v2 chemistry for 2 × 250-bp paired end reads on the Illumina MiSeq platform. Sequences were demultiplexed, and adapters were removed using MiSeq Reporter. Read trimming and assembly were conducted as previously described (Tyson et al., 2015), with de novo assembly done using the CLC Genomics Workbench and genome annotation using NCBI’s Prokaryotic Genome Automated Pipeline (Tyson et al., 2015). Species confirmation and serotyping were determined from WGS data per the FDA NARMS Manual of Laboratory Methods (Food and Drug Administration [FDA], 2016); SeqSero1 and SeqSero2 were used for serotyping with any discrepant isolates additionally serotyped according to the Kauffmann–White scheme (Food and Drug Administration [FDA], 2016; Zhang et al., 2019). Serotypes used for analysis correspond to the final serotype determinations submitted to NCBI as attributes with whole-genome sequences, which are deposited under BioProject PRJNA292661 (Supplementary Table 1).

Resistance genes were identified from assemblies by methods previously described (Tyson et al., 2015), with Perl scripts used to identify hits (≥85% amino acid identity and ≥50% sequence length) from a reference database of compiled genes from the ResFinder (Center for Genomic Epidemiology, DTU), ARG-ANNOT (IHU Méditerranée Infection), and CARD (McMaster University) public databases. Additionally, quinolone resistance-determining region (QRDR) mutations were assessed through extraction and analysis of the gyrA, gyrB, parC, and parE genes using ClustalW in MEGA (McDermott et al., 2016). Plasmid replicons were identified using PlasmidFinder (Center for Genomic Epidemiology), with hits determined as having ≥95% identity and ≥60% coverage.

To assess the relationship between Salmonella isolates in this study, the PubMLST database¹ was used to determine the sequence type (ST) from WGS data for each isolate based on the seven-gene legacy multilocus sequence typing (MLST) loci for Salmonella: aroC, dnaN, hemD, hisD, purE, sucA, and thrA. MLST data was then used to generate and visualize minimum spanning trees using the Global Optimal eBURST (goeBURST) algorithm (Francisco et al., 2009) with PHYLOViZ (Francisco et al., 2012).

A total of 43 Salmonella isolates from 41 retail meat samples were included in the analysis. Two isolates from a ground turkey and two from a ground beef sample were included due to more than one unique AMR phenotype profile recovered from each of these samples. Descriptive statistics (prevalence of Salmonella, distribution of covariates, and antimicrobial susceptibility testing results) and logistic regression models were conducted using SAS 9.4. The outcome binary variable for logistic regression was designated as the presence or absence of Salmonella, and the covariates included region of sample collection (northern and southern California), season, meat type, packaging, and label claim (conventional and reduced antibiotic use). Categorization of reduced antibiotic use included samples with packaging claims of organic and/or no antibiotic usage. All other samples with absence of organic or antibiotic claims were categorized as conventional. Univariate logistic regression was performed to determine the crude associations between the outcome and each covariate. A multivariable logistic regression model was then fitted using candidate variables with p < 0.25 from univariate analysis. The significance of all two-way interactions was tested, and the final model was selected based on the lowest Akaike’s Information Criterion (AIC). Variable selection for the final model was also guided by the literature where associations between Salmonella and factors such as meat source have been previously substantiated. Given the smaller sample sizes present in this study, a significance level of α = 0.10 was used to reduce the probability of a type II error. Genotype was considered concordant with phenotype when an isolate with phenotypic resistance to a drug had known resistance genes or mutations conferring resistance to the corresponding drug (true positive, TP) or when an isolate with phenotypic susceptibility to a drug had absence of resistance genes or mutations conferring resistance to the corresponding drug (true negative, TN). False negatives (FN) were defined as isolates that were phenotypically resistant but genotypically susceptible, and false positives (FP) were defined as isolates that were phenotypically susceptible but genotypically resistant. Sensitivity was calculated as TP/(TP + FN) and specificity as TN/(TN + FP). Matrices were created for phenotypic antimicrobial testing results and the presence/absence of resistance genes and plasmid replicons. A heatmap and hierarchical clustering were performed using the heatmap3 package in R, with dissimilarity matrices constructed using the Manhattan distance algorithm and clustered using the UPGMA method.

Out of 958 retail meat products, Salmonella was isolated from 41 (4.28%) samples, with the highest recovery in ground turkey (14/240, 5.83%) followed by chicken (23/478, 4.81%), ground beef (2/120, 1.67%), and pork chops (2/120, 1.67%) (Table 1).

Region of sample purchase (northern and southern CA), packaging type, and label claim were not significantly associated with the recovery of Salmonella, with the final multivariable logistic regression model including season and meat type as significant covariates. Odds of Salmonella isolation was 3.70 (90% CI 1.05–13.07) times higher in ground turkey when compared to pork chops, adjusting for season. Adjusting for meat type, samples collected in the fall, spring, and summer months had a 6.47 (90% CI 2.29–18.27), 3.81 (90% CI 1.29–11.27), and 3.12 (90% CI 1.03–9.45) times higher odds of Salmonella contamination compared to those collected in the winter months, respectively (Table 1).

From serotyping and MLST analysis, 14 distinct serotypes and 17 STs were identified (Table 2). The most frequently isolated serotypes were S. Kentucky (11/43, 25.58%) and S. Reading (8/43, 18.60%), with all S. Kentucky isolates recovered from chicken samples and all S. Reading isolates recovered from ground turkey. The remaining 12 serotypes displayed distinctive source trends, with exceptions of S. Infantis being recovered from three different meat types—chicken, ground turkey, and ground beef—and S. Schwarzengrund recovered from chicken and ground turkey (Table 2). Each serotype was associated with one ST, with the exception of S. Kentucky isolates, which were distributed across four different STs (Figure 1A). By source, ST32 isolates were recovered across different retail meats (Figure 1B). The greatest serotype and ST diversity was observed in isolates from chicken samples (Figure 1B), though the wide distribution of isolates overall is indicative of a high degree of diversity in genetic profiles across all Salmonella isolates recovered in this study (Figure 1).

All 43 of the Salmonella isolates in this study were susceptible to azithromycin, meropenem, and trimethoprim-sulfamethoxazole; 32.56% (14/43) of isolates were susceptible to all antimicrobials tested, 25.58% (11/43) were resistant to one drug, 27.91% (12/43) to two drugs, and 13.95% (6/43) to three or more antimicrobial drugs tested. The highest resistance was observed for tetracycline (17/43, 39.53%), followed by streptomycin (15/43, 34.89%) and ampicillin (10/43, 23.26%). Resistance to aminoglycoside drugs—gentamicin and streptomycin—were observed in chicken and ground turkey isolates, with over half of the isolates from chicken samples displaying resistance to streptomycin (12/23, 52.17%). Resistance to sulfonamides—sulfisoxazole—was only detected in chicken and ground turkey isolates (Table 3).

Resistance to all three beta-lactam combination agent drugs tested (amoxicillin-clavulanic acid, cefoxitin, and ceftriaxone) was observed in one chicken isolate. This was a S. Kentucky isolate resistant to beta-lactam/combination, aminoglycoside, and penicillin drug classes and one of the six MDR isolates identified in this study. The other five MDR isolates included four S. Infantis isolates recovered from a ground turkey (n = 2), a ground beef (n = 1), and a chicken (n = 1) sample and a S. Reading isolate from a ground turkey sample. Notably, four of the six MDR isolates (all S. Infantis) displayed resistance to nalidixic acid and reduced susceptibility to ciprofloxacin, and resistance to ceftriaxone was only observed in MDR isolates (Tables 3, 4).

Five distinct antibiogram profiles were observed in MDR isolates, with two S. Infantis isolates displaying the tetra-resistant pattern ampicillin, ceftriaxone, tetracycline, and nalidixic acid in addition to reduced susceptibility to ciprofloxacin. In non-MDR isolates, the most common resistance patterns observed were streptomycin and tetracycline (STR-TET, n = 6) followed by sulfisoxazole and tetracycline (FIS-TET, n = 4) (Table 4).

Among the 43 Salmonella isolates, 16 distinct antimicrobial genes and 17 plasmid replicons were identified. Resistance genes encoding all three types of aminoglycoside-modifying enzymes (AMEs)—acetyltransferases (aac(6’)-Iaa and aac(3’)-IVa), nucleotidyltransferases (ant(3’)-Ia), and phosphotransferases (aph(3’)-Ib, aph(6’)-Id, aph(3’)-Ia, and aph(4’)-Ia)—were detected in this study. Beta-lactamase genes detected included bla_TEM–1C and extended spectrum beta-lactamase (ESBL) bla_CTX–M–65 from class A and AmpC-type bla_CMY–33 from class C. Quinolone resistance-mediating genes detected included a mutation of the QRDR of gyrA (D87Y). No plasmid-mediated quinolone resistance (PMQR) genes were detected. Other genes detected included those encoding resistance to tetracycline (tetA and tetB), sulfonamide (sul1 and sul2), and florfenicol-chloramphenicol (floR). Eighty-six percent (37/43) of isolates carried at least one plasmid replicon, 55.8% (24/43) carried two or more, and 39.5% (17/43) carried three or more. The most commonly detected plasmids were ColpVC (16/43, 37.21%), IncX1 (12/43, 27.91%), IncI1 (10/43, 23.26%), and IncFIB(AP001918) (9/43, 20.93%). The distribution of all AMR genes and plasmid replicons is presented in Figure 2.

In this study, resistance genes identified from WGS correlated with phenotypic testing results with an overall sensitivity and specificity of 95.31% and 100%, respectively. Sensitivity was not calculated for azithromycin, meropenem, trimethoprim-sulfamethoxazole, and ciprofloxacin, due to the absence of resistant isolates to these drugs. Discrepancies were present in aminoglycosides, in which two of the three gentamicin-resistant isolates and one of the 15 streptomycin-resistant isolates did not carry resistance-conferring genes, resulting in a sensitivity of 33.33% and 93.33% respectively, for these drugs (Table 5).

Hierarchical clustering of Salmonella isolates depicts the co-occurrence of specific AMR profiles and plasmid replicon types by serotype and source of isolates. The row dendrogram produced three notable clusters based on isolate-specific profiles. Cluster A corresponds to over half of the S. Reading isolates in this study, with the five isolates in this cluster all ampicillin resistant through carriage of a bla_TEM–1C gene and displaying intermediate resistance to amoxicillin-clavulanic acid. Three of the four S. Typhimurium isolates in this study were presented in cluster B, sharing chicken source and phenotypic resistance to sulfisoxazole and tetracycline conferred through sul2 and tetA genes, respectively. Cluster C included the S. Kentucky isolates resistant to streptomycin and tetracycline (n = 7), with resistance conferred through aph(3’)-Ib (strA) and aph(6’)-Id (strB), and the other tetracycline gene detected in this study, tetB (Figure 3).

Main clusters from the column dendrogram depict the co-occurrence of phenotypes, AMR genes, and plasmid replicons. IncI1, IncX1, and IncFIB(AP001918) replicons were detected primarily in tetracycline (tetB) and streptomycin (aph(6’)-Id and aph(3’)-Ib) resistant S. Kentucky isolates (cluster D). The Col440II and ColpVC replicons were detected in ampicillin-resistant and amoxicillin-clavulanic acid intermediate-resistant S. Reading isolates with carriage of bla_TEM–1C (cluster E). IncC and ColpHAD28 replicons were detected in tetracycline (tetA) and sulfisoxazole-resistant (sul2) S. Typhimurium isolates (cluster F). Lastly, the IncFIB(pN55391) plasmid replicon was detected in MDR S. Infantis isolates exhibiting third-generation cephalosporin (ceftriaxone, bla_CTX–M–65) and quinolone (nalidixic acid, gyrA mutation) resistance and fluoroquinolone reduced susceptibility (ciprofloxacin, gyrA mutation) (cluster G).

In this study, we observed similar frequencies of Salmonella contamination in fresh retail meat products purchased in California (4.3%) compared to the national average from routine NARMS surveillance in the same year (4.0%, 2018) (Food and Drug Administration [FDA], 2020b). A comparison of prevalence findings from different studies should be assessed with caution, as variability in sample collection methods, location, time, and isolation protocols may affect results. Nevertheless, our results here are consistent with previous findings of poultry products being more frequently contaminated with Salmonella than other meat types (Jørgensen et al., 2002; Hyeon et al., 2011; Kim et al., 2012; Li et al., 2020) due to the high-frequency colonization and carriage of Salmonella in the microbiome of healthy poultry animals (Antunes et al., 2016). The highest recovery of Salmonella in our study was in ground turkey (14/240, 5.83%) followed by chicken (23/478, 4.81%) samples, likely due to ground poultry counterparts requiring additional processing steps, which increase opportunities for cross-contamination.

The highest recovery of Salmonella from retail meats was observed in fall months in this study. While a previous study also found greater prevalence in fall months (Xu et al., 2020), overall findings have been inconsistent with respect to seasonality trends (Zhao et al., 2001; Zdragas et al., 2012; Wang et al., 2014; Erickson et al., 2018). It has been suggested that temporal trends may occur by year as opposed to season (Sivaramalingam et al., 2013) and that the discordances in observed seasonal contamination of Salmonella in food products are likely confounded by other contributing factors from production, processing, and distribution processes that serve as primary drivers of pathogen contamination and proliferation. From a food safety perspective, this contrasts with the seasonality trends observed in incidences of human salmonellosis, which has been associated with factors such as temperature and oscillations in human activity (e.g., increased consumption of meat products during certain times of the year) rather than differences in the frequency of retail meat contamination itself (Ravel et al., 2010).

An important facet to Salmonella epidemiology is the fluctuating significance, distribution, and AMR profiles of serotypes over time. Salmonella serotypes by nature display host specificity and varied pathogenicity for human and animal hosts depending on their degree of adaptation (Jajere, 2019). Common serotypes that have been associated with foodborne disease have been broad-spectrum host adapted such as S. Enteritidis, S. Typhimurium, and the monophasic S. Typhimurium variant S. 1,4,[5],12:i:- (Yang et al., 2015; Antunes et al., 2016; Jajere, 2019; Mandilara et al., 2021).

The two S. Enteritidis and four S. Typhimurium isolates recovered in this study were all from retail chicken products, of which both S. Enteritidis isolates were susceptible to all 14 antibiotics tested and the four S. Typhimurium isolates displayed resistance to two drugs, tetracycline and sulfisoxazole. Tetracycline and sulfonamides are two major classes of antibiotics that have been conventionally utilized for prophylactic and therapeutic treatment of food animals (Granados-Chinchilla and Rodríguez, 2017). Tetracycline is a broad-spectrum bacteriostatic agent that was traditionally widely administered to poultry flocks through drinking water and feed (Chopra and Roberts, 2001). As of January 2017, its application in feed has been limited to therapeutic use through a requirement of the Veterinary Feed Directive (Granados-Chinchilla and Rodríguez, 2017). Despite this restriction, the highest frequency of resistance in our study was to tetracycline (17/43, 39.5%), driven by the proportionately large number of resistant chicken isolates (12/23, 52.2%). We observe a similar trend in streptomycin (15/43, 34.9%) due to the high level of resistance across all serotypes from chicken isolates (12/23, 52.2%). Streptomycin is another drug that is historically used in food-producing animals and serves as both an indicator for aminoglycoside resistance in the food supply chain (McDermott et al., 2016; Wang et al., 2019) and an epidemiologic marker for presence of penta resistance to ampicillin, chloramphenicol, streptomycin, sulfonamide, and tetracycline (ACSSuT), a pattern observed in widely disseminated virulent and MDR strains like S. Typhimurium DT104 and U302 (Yu et al., 2008). The third most frequent resistant drug observed in our study was ampicillin (10/43, 23.3%) attributed by ground turkey isolates (8/15, 53.3%), which is consistent to the routinely higher level of ampicillin resistance detected through NARMS surveillance in retail turkey isolates (35.5%, 2018) relative to those from retail chicken, beef, and pork (Food and Drug Administration [FDA], 2020b,2021c; Singer et al., 2020). Lastly, the detection of four MDR S. Infantis isolates from retail chicken, ground turkey, and ground beef products in our study mirrors NARMS surveillance findings from the last few years in which the rise in MDR Salmonella from retail meats is attributed by a marked increase in MDR S. Infantis superseding other leading resistant serotypes (Food and Drug Administration [FDA], 2021c; Tyson et al., 2021).

Increasing affordability and improved turnaround time for WGS have vastly improved the resolution of foodborne bacteria profiling and allowed for its integration in routine surveillance efforts as done here in this study. In particular, its utility for identification of resistance mechanisms provides the genotypic basis for in silico predictions of phenotypic resistance including resistance for drugs that are not included in routine testing (McDermott et al., 2016; Su et al., 2019; NIHR Global Health Research Unit on Genomic Surveillance of AMR, 2020). Despite the small number of isolates in this study and the large proportion that are susceptible, our findings here affirm results from previous studies that demonstrated the robust capacity of WGS for prediction of phenotypic resistance in Salmonella and other bacterial species (Tyson et al., 2015, 2018; McDermott et al., 2016; Neuert et al., 2018). For the 43 Salmonella isolates here, WGS data predicted phenotypic results with an overall sensitivity and specificity of 95.31% and 100%, respectively.

The three discordant isolates resulting in lowered sensitivity were observed for gentamicin and streptomycin, attributed by phenotypic resistance in the absence of detected resistance genes. One potential explanation is that genes may not have been present when a colony was sequenced at a different time from when phenotypic testing was performed, but moreover, the imperfect correlation observed here presents a few important considerations. First, concordant genotypic prediction of phenotypic resistance is reliant on the recommended breakpoints used for classification of isolates. False classification of phenotypic results can occur when MIC values fall just below or above a breakpoint and/or in instances where interpretation of MICs is based on alternative guidelines like NARMS consensus interpretative criteria in the absence of available CLSI criteria. This is a likely explanation for the discordant streptomycin results observed here and previous studies (Tyson et al., 2015, 2018; McDermott et al., 2016; Neuert et al., 2018; Pornsukarom et al., 2018), as streptomycin is a drug traditionally used for food animals but not in the treatment of enteric infections and therefore lacks defined CLSI clinical breakpoints (McDermott et al., 2016; Wang et al., 2019). Secondly, in instances where interpretative criteria are available, alternative use of different breakpoints for classification can also affect concordance of WGS results with phenotype. For instance, specificity for ciprofloxacin in this study was fully concordant only when using CLSI breakpoints for resistance (≥1 μg/ml). The CLSI breakpoint for reduced ciprofloxacin susceptibility (≥0.12 μg/ml) is currently used as an alternative criterion for classification of resistance to capture emerging fluoroquinolone resistance (Food and Drug Administration [FDA], 2021b). Traditionally, resistance to fluoroquinolones is conferred through one or more chromosomal mechanisms mediated by mutations in the QRDRs of target enzymes DNA gyrase (gyrA and gyrB) and topoisomerase IV (parC and parE) (Hooper and Jacoby, 2016). For ciprofloxacin, resistance is observed to be conferred in combinations of mutations within both gyrA and parC (Redgrave et al., 2014; Neuert et al., 2018). Five isolates in this study exhibited reduced susceptibility, but classification of these isolates as resistant would have resulted in lowered specificity—88.37% (38/43) instead of 100%—as these isolates only carried a single gyrA mutation. Lastly, WGS predictions of phenotypic resistance are as robust as our ability to identify the genetic determinants that confer resistance. This highlights the impact of reference database(s) selection, importance of active curation and inclusion of novel genes to ensure database comprehensiveness, and an unavoidable caveat of relying on WGS approaches when unknown resistance mechanisms cannot be detected.

Although the significance of the associations between serotypes and the presence/absence of AMR genes and plasmid replicons could not be assessed due to the small number of isolates recovered for each serotype, results here are congruent with other studies that have reported co-occurrence of certain plasmid(s) and AMR genes with serotype (Mather et al., 2013; Pornsukarom et al., 2018; Zhao et al., 2020). It should be noted that while the presence of a plasmid replicon is likely indicative of the corresponding plasmid type, it is possible that the replicon type may have been chromosomally integrated or co-integrated on plasmids with multiple replicons (Johnson et al., 2007; McMillan et al., 2020). Such occurrences are considered rare, and the detection of plasmid replicons as done here presents a quick and efficient way to screen for the presence of putative plasmids.

In this study, we identified several plasmid replicons previously detected in Salmonella that are associated with resistance. IncX1 and IncI1 are two incompatibility groups frequently distributed in Enterobacteriaceae, with the latter frequently found in Salmonella from food animal sources (Kaldhone et al., 2019), as evident through the S. Kentucky isolates from chicken samples in this study. IncC (formerly grouped as IncA/C) plasmids are also frequently prevalent in pathogenic Enterobacteriaceae and are associated with bla_CMY and bla_NDM genes (Hancock et al., 2017; Ambrose et al., 2018), though here we observed their co-occurrence with sul2 and tetA genes in S. Typhimurium isolates from chicken. In S. Reading turkey isolates from this study, we detected the Col440II replicon and bla_TEM–1C. Carriage of bla_TEM–1C on Col440II was first detected in a S. Hadar turkey isolate in 2007, where thereafter detection of this plasmid with bla_TEM–1C was reported in S. Reading in 2014, also from a turkey source (Miller et al., 2020). Recently, S. Reading isolates with this plasmid were identified in an emerged clade linked to United States and Canadian outbreaks from live turkeys and raw turkey products, including one which occurred during our 2018 study period (November 2017 to March 2019) (Centers for Disease Control and Prevention [CDC], 2019; Hassan et al., 2019; Miller et al., 2020). The genomic investigation of isolates from clinical, meat product, environmental, and animal sources from Miller et al. indicated that a novel clone of S. Reading emerged and disseminated across North America in parallel to expansion of commercial turkey production, likely through vertical transmission from a common source (Miller et al., 2020). Detection of turkey isolates with the distinguishing carriage of Col440II and bla_TEM–1C gene here supports their findings and highlights the value of integrated surveillance in detection and elucidation of emerging microbial hazards in the food supply chain.

Another public health concern to note from this study is the detection of the IncFIB(pN55391) plasmid replicon among four MDR S. Infantis isolates. The IncFIB(pN55391) plasmid was first detected in MDR, ESBL-producing S. Infantis strains in Israel, where thereafter rapid clonal expansion resulted in its worldwide dissemination (Franco et al., 2015; Hindermann et al., 2017; Alba et al., 2020; García-Soto et al., 2020). To date, MDR ESBL-producing S. Infantis has been reported across the United States in humans, food animals, and—as evidenced here and in previous studies—retail meats (Tate et al., 2017; Brown et al., 2018; Alba et al., 2020; M’ikanatha et al., 2021; Tyson et al., 2021). In the past few years, NARMS surveillance has reported a rise in resistant Salmonella isolates from retail meats, which is attributed to increasing numbers of MDR S. Infantis (Tyson et al., 2021). Dissemination of S. Infantis with ESBL carriage on a large conjugative plasmid as indicated by four of the five isolates carrying bla_CTX–M–65 in this study is concerning due to its potential to disseminate resistance genetic elements to other pathogens and the challenges in treating infections exhibiting resistance to penicillins, extended-spectrum cephalosporins, monobactams, and other drugs conferred through MDR status (Tate et al., 2017; M’ikanatha et al., 2021).