Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, Canada.

To better understand the contribution of wildlife to the dissemination of Salmonella and antimicrobial resistance in Salmonella and Escherichia coli, we examined whole-genome sequence data from Salmonella and E. coli isolates collected from raccoons (Procyon lotor) and environmental sources on farms in southern Ontario. All Salmonella and phenotypically resistant E. coli collected from raccoons, soil, and manure pits on five swine farms as part of a previous study were included. We assessed for evidence of potential transmission of these organisms between different sources and farms utilizing a combination of population structure assessments (using core-genome multi-locus sequence typing), direct comparisons of multi-drug resistant isolates, and epidemiological modeling of antimicrobial resistance (AMR) genes and plasmid incompatibility (Inc) types. Univariable logistic regression models were fit to assess the impact of source type, farm location, and sampling year on the occurrence of select resistance genes and Inc types. A total of 159 Salmonella and 96 resistant E. coli isolates were included. A diversity of Salmonella serovars and sequence types were identified, and, in some cases, we found similar or identical Salmonella isolates and resistance genes between raccoons, soil, and swine manure pits. Certain Inc types and resistance genes associated with source type were consistently more likely to be identified in isolates from raccoons than swine manure pits, suggesting that manure pits are not likely a primary source of those particular resistance determinants for raccoons. Overall, our data suggest that transmission of Salmonella and AMR determinants between raccoons and swine manure pits is uncommon, but soil-raccoon transmission appears to be occurring frequently. More comprehensive sampling of farms, and assessment of farms with other livestock species, as well as additional environmental sources (e.g., rivers) may help to further elucidate the movement of resistance genes between these various sources.

Introduction

The rise of antimicrobial resistance (AMR) is a major global threat to the health of humans and animals alike [1, 2]. There is mounting evidence of widespread movement of AMR determinants (e.g., genes and the plasmids associated with their movement) within natural environments [3-5], and genes conferring resistance to high-priority antimicrobials (e.g., mcr-1) have been identified in avian and mammalian wildlife across the world [6-8]. It is generally recognized that wild animals may act as sentinels of environmental AMR pollution, but recent work suggests that wildlife may also physically disseminate AMR determinants from one location to another through their feces [9, 10]. With recognition of the importance of One Health approaches that consider different sampling sources, there is a need to integrate epidemiological investigations with new technologies such as whole-genome sequencing (WGS), which permit the assessment of the genetic basis of AMR at a higher resolution [11, 12].

A number of investigations combining genomics with epidemiology to examine foodborne pathogens and/or AMR in wildlife have recently been performed [9, 10, 13–15]. With much of the literature on AMR and wildlife focused on wild birds [16], mammalian wildlife such as raccoons (Procyon lotor) arguably merit further examination in this context due to their prevalent populations, tendency to forage in anthropogenic environments, and general proximity to human and domestic animal settings [17]. This work is part of a larger repeated cross-sectional study of wild meso-mammals (raccoons, primarily) on swine farms and conservation areas in southern Ontario, Canada between 2011 and 2013 [18-20]. The study region, within the Grand River Watershed (6800km2), includes intensive agricultural activities, and a population of ~1 million people, providing us with an opportunity to examine the intersection between wildlife, livestock, and environmental sources in a heavily populated region of southern Ontario, Canada. Although the overall prevalence of Salmonella and phenotypic resistance among E. coli isolated from wildlife and soil samples in this previous study did not differ significantly between swine farms and conservation areas, certain strains (e.g., Salmonella Typhimurium var. Copenhagen DT104), serovars (e.g., Salmonella Agona) and resistance patterns appeared only in samples obtained on swine farms [18, 19]. In certain cases, it was unclear if the molecular determinants of resistance were shared or were distinct between different compartments of this environment, even when phenotypic resistance was the same.

Consequently, the primary objective of this study was to examine in detail the subset of samples obtained from swine farms in this previous study, applying WGS data from Salmonella and phenotypically resistant E. coli isolates to assess for evidence of potential transmission of these organisms and associated AMR determinants among raccoons, swine manure pits, and soil, on and between farms. To address this objective, a combination of population structure assessments, epidemiological modeling of select AMR determinants (i.e., genes, predicted plasmids), and direct comparisons of multidrug resistant isolates was performed. For assessments of potential transmission utilizing statistical modeling, our aim was to determine the impact of source type, farm location, and sampling year on the occurrence of AMR determinants (identified in silico). A secondary objective of this work was to examine the validity of genotypic AMR identification using WGS data by calculation of test sensitivity and specificity, using prior phenotypic AMR data as the gold standard.

Methods

Sample collection

Samples for this study were previously collected as part of a repeated cross-sectional study of raccoons on swine farms and conservation areas in southern Ontario between 2011 and 2013 [18, 19]. For the present study, we included only isolates originating from swine farm environments. Sampling sources included raccoons, swine manure pits, and soil. The study region and sampling methods have been previously described and are available in Bondo et al. [18, 19]. Briefly, samples were obtained from monthly sampling of five swine farms in the Grand River watershed, near the cities of Guelph and Cambridge in Ontario, Canada. Raccoons were live-trapped, and animals were chemically immobilised to obtain a rectal fecal swab using a Cary-Blair applicator (BBL CultureSwab, Bd; Becton, Dickinson and Company, Maryland, USA). Individual animals were ear-tagged and microchipped for subsequent identification, and animals were sampled up to once monthly, but animals recaptured within the same trapping month were released immediately. Swine manure pit and soil samples were obtained from each site at the beginning of each trapping week. For soil samples, 10 g of soil was collected from within a 2-m radius of each animal trap and stored in a sterile container. Swine manure pits were sampled by pooling samples from two different depths (i.e., top 1/3, and mid-depth) at three different locations around the pit. All samples were kept on ice in the field until further processing.

Previous culture and susceptibility testing

Samples were previously cultured for Salmonella and E. coli within three days of collection at the McEwen Group Research Group Lab at the Canadian Research Institute for Food Safety, University of Guelph (Guelph, Ontario, Canada) using standard culture-based methodology as previously described [18, 19]. One isolate of Salmonella and one isolate of E. coli from each sample was sub-cultured and tested further. Isolates were confirmed by biochemical testing and submitted for phenotypic susceptibility testing to the Antimicrobial Resistance Reference Laboratory (National Microbiology Laboratory (NML) at Guelph, Public Health Agency of Canada, Guelph, Ontario, Canada). Testing was completed in accordance with methods outlined by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) [21]. Isolates were previously tested using the National Antimicrobial Resistance Monitoring System (NARMS; Sensititre, Thermo Scientific), and antimicrobial panel CMV3AGNF, which included the following 15 antimicrobials: gentamicin (GEN), kanamycin (KAN), streptomycin (STR), amoxicillin-clavulanic acid (AMC), cefoxitin (FOX), ceftiofur (TIO), ceftriaxone (CRO), ampicillin (AMP), chloramphenicol (CHL), sulfisoxazole (SOX), trimethoprim-sulfamethoxazole (SXT), tetracycline (TCY), nalidixic acid (NAL), ciprofloxacin (CIP), and azithromycin (AZM).

Selection of isolates for whole-genome sequencing

All Salmonella isolates originating from swine farms were selected for sequencing and inclusion in this study. Due to resource constraints, only E. coli isolates demonstrating phenotypic resistance to at least one of 15 antimicrobials examined were selected for sequencing and included in the present study. During 2011, three different E. coli isolates were cultured from each sample; for samples with more than one isolate demonstrating phenotypic resistance, a random number generator was used to select one resistant isolate for sequencing.

DNA extraction, whole-genome sequencing and genome assembly

Cultures of Salmonella and E. coli were grown on Mueller Hinton Agar and incubated overnight at 35°C. Cultures were then distributed to the NML (Public Health Agency of Canada) in Winnipeg for DNA extraction and short-read sequencing, or these steps were performed on site, at the University of Guelph and the NML in Guelph, Ontario, respectively. Genomic DNA extraction was performed using 1 ml of culture as input to the Qiagen DNEasy plant and tissue 96 extraction kit, according to manufacturer protocols (Qiagen, Hilden, Germany). Sequencing was then performed at the NML in Guelph or in Winnipeg, using Nextera XT library preparation and Illumina MiSeq version 3 (600-cycle kit) or NextSeq550 platforms, according to manufacturer protocols. Assembly of raw reads was performed using SPAdes [22], as part of the Shovill pipeline (version 1.0.1; https://github.com/tseemann/shovill) using the following settings: "—minlen 200—mincov 2;—assembler spades;—trim".

Analysis of whole-genome assemblies

Prediction of legacy multi-locus sequence types was performed using MLST (version 2.19.0; https://github.com/tseemann/mlst) according to the Achtman 7-loci scheme for Salmonella enterica and E. coli (https://pubmlst.org/mlst/). Isolates were also typed using fsac (version 1.2.0; https://github.com/dorbarker/fsac) according to the core-genome multi-locus sequence typing (cgMLST) schemes available from Enterobase (https://enterobase.warwick.ac.uk/) with 3002- and 2513-loci schemes for Salmonella and E. coli, respectively. Isolates with 25 or more missing cgMLST loci were considered poor quality and excluded from any further analyses. Allelic differences between cgMLST profiles from different sources were calculated using R (version 3.6.3), and minimum spanning trees were created to provide visual representations of population structure based on cgMLST data [23]. Minimum spanning trees were created using the standalone GrapeTree software package (version 1.5) [23] and the "MSTreeV2" algorithm, which accounts for missing data. For minimum spanning trees visualizing overall populations of Salmonella and E. coli, lenient clustering thresholds (k>20 allelic differences) were used to provide a qualitative assessment of overlap between isolates from different sources, while minimizing unnecessary noise. A similar approach was used to construct a minimum spanning tree for isolates of Salmonella serovars common to both raccoons and swine manure pits; however, a more stringent clustering threshold was applied (k<10 allelic differences) to reflect a higher degree of similarity between those isolates. This threshold of 10 allelic differences is also consistent with the strain-level threshold used by PulseNet for Salmonella [24], thereby ensuring this latter minimum spanning tree was less prone to clustering of isolates from potentially different strains. Serotyping of E. coli isolates was performed using ECTyper (version 1.0.0, database version 1.0; https//github.com/phac-nml/ecoli_serotyping) and default settings. Serotyping of Salmonella isolates was performed using SISTR (version 1.1.1; https://github.com/phac-nml/sistr_cmd), and default settings with the "centroid" allele database.

Acquired resistance genes were identified using Abricate (version 0.8.13; https://github.com/tseemann/abricate) and the Resfinder database (current as of May-17-2020), with settings of 90% identity and 60% coverage. Identity and coverage settings were increased to 100% and 90%, respectively, to identify acquired beta-lactamases. Identification of plasmid incompatibility (Inc) types was performed using Abricate (version 0.8.13; https://github.com/tseemann/abricate) and the Plasmidfinder database (current as of May-17-2020). Settings of 98% identity and 70% coverage were used.

For multidrug resistant Salmonella isolates containing the same, or similar, phenotypic resistance patterns and representing identical sequence types, Snippy (version 4.4.0; https://github.com/tseemann/snippy) was used to further distinguish genetic differences based on single-nucleotide polymorphisms (SNPs) using the entire genome. Isolate raw reads were used, along with default settings. Reference genomes used were: Salmonella enterica subsp. enterica serovar Typhimurium str. LT2 (Accession No.: NC_003197.2); Salmonella enterica subsp. enterica serovar Hadar str. RI_05P066 (Accession No.: ABFG00000000.1).

Sensitivity and specificity of in silico AMR prediction

The sensitivity and specificity of in silico AMR prediction were calculated by antimicrobial class and overall (i.e., pooling all individual test results); phenotypic AMR results were considered the gold standard, and the presence of genotypic resistance was considered a positive test result. Isolates with intermediate susceptibility were categorized as susceptible. We elected not to assess test sensitivity or specificity of drug classes for which chromosomal mutations are known to confer a considerable proportion of expressed resistance (i.e., quinolones) [25]. As a quality control measure, isolates with missing genotypes for resistant phenotypic test results for three or more of the seven antimicrobial classes were examined and excluded from further analyses if they were also missing greater than 20 loci based on cgMLST.

Statistical analyses

Univariable multi-level logistic regression was used to model the odds of identifying select Inc types and AMR genes found in E. coli and Salmonella from different sources. All statistical analyses were performed using STATA (STATA Intercooled 14.2; StataCorp, College Station, Texas, USA). Only Inc types and resistance genes with a prevalence greater than 10% and less than 90% were modeled. The following independent variables were examined: year of sampling, farm location (farm sites 6–10, as in Bondo et al. [18]), and source type (i.e., raccoon, swine manure pit, soil). Due to low effective samples sizes, univariable logistic regression was performed, with a random intercept to account for clustering of isolates obtained from the same raccoon or swine manure pit. For models that did not converge using the ‘melogit’ command, the model was subsequently fit using the ‘meqrlogit’ command, which uses QR decomposition of the variance-components matrix. Variance components were used to calculate intraclass correlation coefficients (ICCs) using the latent variable technique [26]. The fit of multi-level models was assessed by examining the best linear unbiased predictions (BLUPS) for normality and homoscedasticity, and Pearson’s residuals were examined for outliers. If variance components were very small (<1x10-3), the Bayesian information criterion (BIC) was used to compare the fit of the multi-level logistic regression model with an ordinary logistic regression; the better fitting model was reported [26]. If low effective sample sizes posed estimation issues for univariable models, exact logistic regression was used, and the score method was used to calculate p-values for these models. A significance level of α = 0.05 was used, and all tests were two-tailed.

Results

Dataset

Salmonella

Based on our study criteria, a total of 159 Salmonella isolates from the following sources were included: raccoon (n = 92), soil (n = 46), and swine manure pit (n = 21). Accession numbers of sequence data for all Salmonella isolates included in this study are available in S1 File. Most of these isolates were obtained from samples collected in 2012 (n = 82, 52%) and in 2011 (n = 50, 31%), with fewer isolates in 2013 (n = 27, 17%). Isolates originated from 80 unique raccoons; among animals captured multiple times, eight individuals contributed two isolates, and two raccoons contributed three isolates from different trapping dates. The majority of Salmonella isolates were phenotypically pan-susceptible to the antimicrobials tested: 96.7% of raccoon isolates (95%CI: 90.8–99.3%), 95.6% of soil isolates (95%CI: 85.2–99.5), and 95.3% of swine manure pit isolates (95%CI: 76.2–99.9%). Six of the 159 isolates demonstrated phenotypic resistance (Table 1), and the overall prevalence of multidrug resistance (3+ drug classes) was 1.9% (n = 3/159, 95%CI: 0.4–5.4%). These multidrug resistant isolates were identified in two raccoon samples and one swine manure pit sample (Table 1).

Table 1

Resistance genes and plasmid incompatibility (Inc) groups identified in silico among phenotypically resistant Salmonella enterica from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 6/159).

NCBI accession number	Isolate id	Sequence type	Serovar	Source	Phenotypic resistance patterna	Resistance genes	Inc group	Year	Farm location
JAIHBY000000000	N18-00467	ST309	Kiambu	Soil	AMP-TCY	blaTEM-1, tet(A)	IncX1	2013	6
JAIHBV000000000	N18-00464	ST19	Typhimurium	Raccoon	STR-SOX-TCY	aadA2, sul1, tet(A)	ColRNAI, IncFIIS, IncFIBS, Col156, Col440I	2013	7
JAIHBU000000000	N18-00463	ST19	Typhimurium	Manure	STR-SOX-TCY	aadA2, sul1, tet(A)	ColRNAI, IncFIIS, IncFIBS, Col156	2013	9
JAIHBW000000000	N18-00465	ST96	Schwarzen-grund	Raccoon	STR-SOX-TCY	aadA4, sul1, tet(B)	IncHI2, pkpccav1321, IncHI2A	2013	6
JAIHBX000000000	N18-00466	ST33	Hadar	Soil	STR-TCY	aph(3’)-Ib, aph(6)-Id, tet(A)	ColRNAI, ColpVC, Col156	2013	6
JAIHBZ000000000	N18-00468	ST33	Hadar	Raccoon	STR-TCY	aph(3’)-Ib, aph(6)-Id, tet(A)	Col440	2013	6

a AMP = ampicillin; SOX = sulfisoxazole; STR = streptomycin; SXT = trimethoprim sulfamethoxazole; TCY = tetracycline.

E. coli

A total of 96 phenotypically resistant E. coli isolates were included, with the following source distribution: raccoon (n = 20), soil (n = 45), and swine manure pit (n = 31). Accession numbers for sequence data from all E. coli isolates can be found in S1 File. Most of these isolates were obtained from samples collected in 2013 (n = 39, 41%), followed by 2011 (n = 37, 39%), and 2012 (n = 20, 21%). Phenotypically resistant raccoon isolates were obtained from 20 unique individuals, with no repeated sampling. Overall, 26.0% of these resistant isolates were multidrug resistant (3+ drug classes) based on phenotype (n = 25/96), with most of these isolates identified in soil samples (n = 13), followed by raccoon samples (n = 7), and swine manure pit samples (n = 5). The corresponding prevalence of multidrug resistance was highest among resistant raccoon isolates (35.0%, 95%CI: 15.4–59.2%) and resistant soil isolates (28.9%, 95%CI: 16.4–44.3%), and lowest in resistant swine manure pit isolates (16.1%, 95%CI: 5.4–33.7%).

Distribution of serovars and MLST types

In total, 21 sequence types representing 21 different serovars were identified (Fig 1 and Table 2). Three isolates were not typeable by MLST (two raccoon, one soil) due to a missing allele, or a partial match. The four most common serovars identified among all source types, in descending order, were S. Newport (28%), S. Agona (18%), S. Infantis (11%), and S. Typhimurium (9%; Fig 1). A number of internationally-recognized sequence types [27] were also identified; seventeen S. Infantis ST32 isolates were identified on all farms from all source types (i.e., manure, raccoon and soil), and, apart from one sample, all were collected in 2011 and 2012. A total of fourteen S. Typhimurium ST19 isolates were identified in all sources on four of five farms; nine of these (64%) were obtained from the same farm (farm 6) in 2012. Finally, one isolate each of S. Schwarzengrund ST96, S. Heidelberg ST15, and S. Brandenburg ST65 were isolated from two raccoons and one swine manure pit sample, respectively.

Fig 1

Population structure of 159 Salmonella enterica isolates from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada based on 3002-loci cgMLST scheme from Enterobase.

Minimum spanning tree created using k = 30 clustering threshold in GrapeTree. Serovars determined using SISTR. Three isolates (2 raccoon, 1 soil) were not typeable by MLST. Frequency counts are in square brackets. Bubble size is proportional to the number of isolates in each cluster, and each cluster contains isolates differing at a maximum of 30 cgMLST loci.

Table 2

Frequency of Salmonella enterica legacy multi-locus sequence types of isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 159).

Sequence typeb (Serovar)	Source type			Totalc (%)
	Raccoon (n = 92)	Swine manure pit (n = 21)	Soil (n = 46)
ST350 (Newport)	31	0	11	42 (26.4%)
ST13 (Agona)	16	5	8	29 (18.2%)
ST32 (Infantis)	9	3	5	17 (10.7%)
ST19 (Typhimurium)	7	1	6	14 (8.8%)
ST26 (Thompson)	6	0	5	11 (6.9%)
ST404 (Paratyphi B var. Java)	9	0	2	11 (6.9%)
ST638 (Livingstone)	0	9	1	10 (6.3%)
ST15 (Heidelburg)	1	0	0	1 (0.6%)
ST96 (Schwarzengrund)	1	0	0	1 (0.6%)
ST65 (Brandenburg)	0	1	0	1 (0.6%)

Bolded STs represent internationally recognized sequence types implicated in human illness.

a Three isolates (two from raccoons, one from soil) were not typeable.

b Sequence types (STs) determined using 7-loci Achtman scheme.

c Other STs identified within 5 or fewer isolates were: ST413 (Mbandaka; n = 1), ST2848 (IIIb 11:k:z53; n = 2), ST22 (Braenderup; n = 1), ST23 (Oranienburg; n = 4), ST33 (Hadar; n = 2), ST309 (Kiambu; n = 1), ST308 (Poona; n = 4), ST405 (Hartford; n = 3), ST469 (Rissen; n = 1), ST11 (Enteritidis; n = 2), ST544 (Molade; n = 1).

This population of resistant E. coli was comprised of 49 sequence types, of which two isolates from manure pit samples were not typeable by MLST (S1 Table), due to a missing allele or a partial match. None of the serovars identified here overlapped with those responsible for the majority of Shiga-toxin producing E. coli infections in humans (i.e., O157, O26, O45, O103, O111, O121, O145; S2 Table) [28]. Apart from eleven ST10 isolates (11.5%), no other major sequence types associated with uropathogenic E. coli (UPEC) strains in humans were identified (e.g., ST131, ST96, ST73, ST127, ST140) [29].

Population structure based on cgMLST

The following Salmonella serovars were identified in both swine manure pit and raccoon isolates: S. Agona, S. Infantis, S. Poona, S. Typhimurium (Fig 2). Identical or similar cgMLST subtypes were identified from all sources for both S. Agona and S. Poona serovars. The 29 S. Agona isolates had between 0 and 15 allelic differences; these isolates were identified in 2011 and 2012 on three of five farms. Four S. Poona isolates that differed by a maximum of 3 loci were isolated on farm 8 in 2013 from all sources. Salmonella Infantis (n = 17) and S. Typhimurium (n = 14) isolates clustered into single groups at thresholds of 51 and 329 allelic differences, respectively. Less commonly identified serovars that were isolated from both raccoon and soil isolates differed by a variety of minimum and maximum allelic differences between the two sources: S. Hadar (39 allelic differences; n = 2), S. Enteritidis (270 allelic differences; n = 2), S. Hartford (1–15 allelic differences; n = 3), S. Thompson (0–36 allelic differences; n = 11), S. Paratyphi B var. Java (0–28 allelic differences; n = 11), and S. Newport (0–68 allelic differences; n = 45). Salmonella Hadar and S. Hartford isolates were isolated from the same farm in different months of the same year. The majority of S. Thompson isolates were obtained from farm 8 (n = 8/10), and most were collected in 2012 (n = 7/10).

Fig 2

Population structure of 64 isolates of Salmonella enterica isolates from raccoons, swine manure pits, and soil on swine farms in southern Ontario, Canada based on 3002-loci cgMLST scheme from Enterobase, for serovars S. Agona, S. Infantis, S. Typhimurium, and S. Poona (only serovars identified both in raccoon and swine manure pit samples).

Minimum spanning tree created using k = 5 clustering threshold in GrapeTree. (A) Population structure with serovars determined using SISTR. (B) Distribution by source type. (C) Distribution by farm. (D) Distribution by year of sampling. Frequency counts are in square brackets. Bubble size is proportional to the number of isolates in each cluster, and each cluster contains isolates differing at a maximum of 5 cgMLST loci.

The population structure of E. coli based on cgMLST by source type and by farm is presented in Fig 3A and 3B; similar or identical subtypes were identified in isolates from raccoons, soil, and swine manure pit samples, regardless of farm location.

Fig 3

Population structure of 96 phenotypically resistant Escherichia coli isolates from raccoons, swine manure pits, and soil on swine farms in southern Ontario, Canada based on 2513-loci cgMLST scheme from Enterobase.

Minimum spanning tree created using k = 50 clustering threshold in GrapeTree. (A) Distribution by source type. (B) Distribution by farm location. Frequency counts are in square brackets. Bubble size is proportional to the number of isolates in each cluster, and each cluster contains isolates differing at a maximum of 50 cgMLST loci.

In silico determination of acquired AMR genes and plasmid Inc types

Eighteen different Inc types and nine different AMR genes were identified in this Salmonella population (Tables 1 and 3). AMR genes identified were aadA2, aadA4, aph(3’’)-Ib, aph(6)-Id, fosA7, sul1, tet(A), tet(B), and blaTEM-1. Gene fosA7 was only identified in phenotypically pan-susceptible isolates, with an overall prevalence of 19.5% (n = 31/159; note that fosfomycin was not included on our antimicrobial test panel). All six phenotypically resistant Salmonella isolates were isolated in 2013 (Table 1). An isolate from a manure pit and an isolate from a raccoon with the same phenotypic resistance patterns (SOX-STR-TCY) previously identified as S. Typhimurium ST19 DT104 by Bondo et al., [19] contained the same resistance genes, and some of the same predicted plasmids. These two S. Typhimurium ST19 DT104 isolates differed from each other at 11 cgMLST loci, and by 28 SNPs; both were isolated in July 2013, but they were collected on different farms within 3km of one another. Two S. Hadar ST33 isolates, one from a raccoon and another from soil displayed the same phenotypic resistance pattern (STR-TCY) mediated by the same resistance genes (tet[A], aph[ but carried different Inc types. These two S. Hadar ST33 isolates differed at 39 cgMLST loci and by 108 SNPs, and both were isolated in different months from the same farm.

Table 3

Frequencies of plasmid incompatibility (Inc) types identified using whole-genome sequencing data from Salmonella enterica and phenotypically resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

	Salmonellaa (n = 159)	E. colib (n = 96)
Rep-type	Count (%)	Count (%)
IncFIB(AP001918)	0 (0%)	41 (42.7%)
IncI1(alpha)	0 (0%)	12 (12.5%)
IncFiip96a	59 (37.1%)	0 (0%)
IncFII	0 (0%)	15 (15.6%)
IncFIIS	64 (40.2%)	0 (0%)
IncFIA	0 (0%)	9 (9.4%)
IncY	3 (1.9%)	11 (11.5%)
IncX1	27 (17.0%)	7 (7.3%)
IncX3	25 (15.7%)	0 (0%)
IncQ1	0 (0%)	5 (5.2%)
IncI1(I-gamma)	5 (3.1%)	0 (0%)
IncR	0 (0%)	8 (8.3%)
ColYe4449	28 (17.6%)	0 (0%)
ColRNAI	6 (3.8%)	0 (0%)
ColpHAD28	7 (4.4%)	0 (0%)
p0111	0 (0%)	8 (8.3%)

a Plasmid Inc types identified in fewer than five Salmonella isolates included: ColpVC (n = 2), IncFIBS (n = 5), Col440ii (n = 3), IncHI2 (n = 1), pkpccav1321 (n = 1), IncHI2A (n = 1), Col156 (n = 3), Col440i (n = 2), IncFIBphcm2 (n = 1).

b Plasmid Inc types identified in fewer than five E. coli isolates included: ColBS512 (n = 2), ColE10 (n = 2), ColpVC (n = 1), IncC (n = 2), IncB/O/K/Z (n = 1), IncFIA(HI1) (n = 3), IncFIB(K) (n = 2), IncFIB(pB171) (n = 1), IncFIC(FII) (n = 3), IncFII(pHN7A8) (n = 1), IncHI2A (n = 2), IncHI2 (n = 2).

A total of 27 resistance genes and 21 Inc types were identified among resistant E. coli isolates (Tables 3 and 4). The distribution of resistance genes among different sources is presented in Table 4. The majority of genes identified confer resistance to aminoglycosides, tetracyclines, and folate pathway inhibitors. Genes conferring resistance to phenicols were uncommonly identified, and no macrolide resistance genes were identified (Table 4). Besides blaTEM-1 (26.0% prevalence), only one other type of beta-lactamase resistance conferring gene, blaCMY-2, was identified, and occurred in a single E. coli O9:H9 ST10 isolate collected from a raccoon (Table 4). This isolate containing the sole blaCMY-2 also displayed phenotypic resistance to five of seven drug classes examined (AMC-AMP-FOX-TIO-CRO-CHL-STR-SOX-TCY-SXT), and contained genes aadA2, sul2 and dfrA12, as well as a single Inc type (IncC). Two additional isolates were phenotypically resistant to five of the seven drug classes examined: a soil isolate (AMP-CHL-KAN-STR-SOX-TCY-SXT), and a swine manure pit isolate (AMP-CHL-STR-SOX-TCY). Despite having similar phenotypic resistance patterns, these isolates represented different sequence types (ST106 [soil] and ST542 [manure pit]), contained different predicted plasmids, and, apart from the presence of tet(A) and blaTEM-1, contained a different profile of genes responsible for conferring resistance.

Table 4

Frequencies of acquired antimicrobial resistance genes identified using whole-genome sequencing data from phenotypically resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 96).

Antimicrobial group	Resistance gene	Accession no.†	Raccoon(n = 20)	Swine manure pit (n = 31)	Soil (n = 45)	Total (%)
Aminoglycoside	aac(3)-IVa	NC_009838	1	0	0	1 (1.0%)
	aadA2	JQ364967	3	1	1	5 (4.8%)
	ant(3’’)-Ia	X02340	4	7	8	19 (19.8%)
	aph(3’)-Ia	V00359/EF015636	1	0	2	3 (3.1%)
	aph(3’)-IIa	V00618	0	1	0	1 (1.0%)
	aph(3")-Ib	AF321551/AF024602	11	13	20	44 (45.8%)
	aph(6)-Ic	X01702	0	1	0	1 (1.0%)
	aph(6)-Id	M28829	11	13	20	44 (45.8%)
Beta-lactam	bla CMY-2	X91840	1	0	0	1 (1.0%)
	bla TEM-1	AY458016/HM749966/FJ560503	7	7	11	25 (26.0%)
Lincosamide	lnuC	AY928180	0	0	1	1 (1.0%)
	lnuF	EU118119	0	0	1	1 (1.0%)
Folate pathway inhibitors	dfrA1	AF203818/X00926	1	1	4	6 (6.2%)
	dfrA5	X12868	0	1	3	4 (4.2%)
	drfA12	AM040708	2	0	0	2 (2.0%)
	dfrA14	DQ388123	2	0	1	3 (3.1%)
	dfrA23	AJ746361	1	0	0	1 (1.0%)
	sul1	EU780013	5	2	4	11 (11.5%)
	sul2	HQ840942/AY034138	6	1	9	16 (16.7%)
	sul3	AJ459418	1	1	2	4 (4.2%)
Phenicol	floR	AF118107	3	1	3	7 (7.3%)
	cmlA1	M64556	1	1	1	3 (3.1%)
Fosfomycin	fosA7	LAPJ01000014	1	0	2	3 (3.1%)
Tetracycline	tet(A)	AF534183	9	11	25	45 (46.9%)
	tet(B)	AF326777/AP000342	5	16	15	36 (37.5%)
	tet(C)	AY046276/AF055345	0	0	2	2 (2.1%)
	tet(M)	X04388	0	0	1	1 (1.0%)

Values from Resfinder database.

Comparison of Salmonella and E. coli

Among Inc types commonly identified in this population of Salmonella and E. coli, few incompatibility types were identified in both organisms (Table 3). The majority of Inc types were restricted to either Salmonella or E. coli, but not found in both (e.g., IncFIB[AP001918] in E. coli, IncFiip96a in Salmonella). We also evaluated whether resistance genes may be shared between E. coli and Salmonella isolates within the same animal; of the three raccoon samples positive for resistant Salmonella (Table 1), no corresponding resistant E. coli were isolated from the same animal during the study period, either on the same capture date or on another capture date. Along with a single resistant Salmonella from a swine manure pit sample originating from farm 9, three resistant E. coli from manure pits were obtained from the same farm, with one collected in the same year; however, apart from two genes in common between two of the E. coli isolates and the Salmonella isolate (i.e., sul1, tet[A]), there was no overlap with regards to resistance genes, resistance patterns, or Inc types between these resistant E. coli isolates and the resistant Salmonella isolate.

Detailed results from phenotypic antimicrobial susceptibility testing were previously reported by Bondo et al. [18, 19]. Test sensitivity could not be assessed for certain drug classes where no phenotypic resistance was identified (e.g., macrolides, phenicols; Table 5). Test sensitivity and specificity were 89% or greater for all drug classes in both Salmonella and E. coli, with the exception of test specificity in E. coli for aminoglycosides (80.9%). The overall test sensitivity and specificity (i.e., all raw counts pooled together) was 97% or greater for both organisms.

Table 5

Test sensitivity and specificity for in silico identification of acquired antimicrobial resistance genes in Salmonella enterica and phenotypically resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

Antimicrobial class	Salmonella (n = 159)		Escherichia coli (n = 96)
	Test sensitivity (95%CI)	Test specificity (95%CI)	Test sensitivity (95%CI)	Test specificity (95%CI)
Aminoglycoside	100% (39.8–100%*)	99.4% (96.5–99.9%)	100% (92.7–100*%)	80.9% (66.7–90.9%)
Beta-lactam	100% (2.5–100%*)	100% (97.7–100%*)	89.6% (72.7–97.8%)	100% (94.6–100%*)
Macrolide	—c	100% (97.7–100%*)	—c	100% (96.2–100%*)
Sulfonamide	100% (29.2–100%*)	100% (97.7–100%*)	100% (86.8–100%*)	98.6% (92.3–99.9%)
Phenicol	—c	100% (97.7–100%*)	100% (66.4–100%*)	100% (95.8–100%*)
Tetracycline	100% (54.1–100%*)	100% (97.6–100%*)	100% (95.5–100%*)	93.3% (68.1–99.8)
Overall b	100% (76.8–100%*)	99.9% (99.4–99.9%)	98.5% (95.5–99.7%)	97.1% (94.9–98.6%)

a Phenotypic antimicrobial resistance test results were considered the gold standard. Detection of 15 antimicrobials performed using the CMV3AGNF panel from National Antimicrobial Resistance Monitoring System (Sensititre, Thermo Scientific). In silico acquired resistance genes detected using Abricate and the Resfinder database.

b Raw counts for all isolates and antimicrobials were pooled together.

c Not applicable since no phenotypic resistance was identified.

* One-sided, 97.5% confidence interval.

Statistical results

All five Inc types and the one resistance gene analyzed (fosA7) were significantly associated with at least one independent variable (Table 6). Among the four predicted plasmids associated with source type (i.e., IncX1, IncFIIS, IncX3, IncFiip96a), the odds of identifying these Inc types were consistently greater in raccoons compared to swine manure pit isolates, and in some cases, the odds were also greater in soil isolates compared to swine manure pits (i.e., IncFIIS, IncFiip96a; Table 6). Two Inc types (i.e., IncX3, Colye4449) and fosA7 were significantly associated with farm location. Contrasts concerning farm location are available in S3 Table. Colye4449 was the only outcome associated with year of sampling and its prevalence was significantly lower in 2013 compared to 2011 and 2012 (Table 6). The random intercept was not retained in any model with a statistically significant fixed effect, since it did not improve the fit of the model, the variance component was negligible (<1x10-3), and/or coefficients could not be estimated without exact logistic regression.

Table 6

Univariable logistic regression models,, assessing the association between source type, farm location, and year of sampling and the occurrence of select antimicrobial resistance genes and plasmid incompatibility (Inc) types in Salmonella enterica isolates from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 159).

		IncX1		IncFIIS		IncX3
		OR (95%CI)	p-value	OR (95%CI)	p-value	OR (95%CI)	p-value
Source type	Swine manure pit	REF	0.012 (global) a	REF	<0.001 (global) b	REF	0.012 (global) a
	Raccoon	9.06* (1.47–∞)	0.012	20.00 (2.57–155.28)	0.004	8.52* (1.37–∞)	0.025
	Soil	3.30* (0.42–∞)	0.173	11.72 (1.44–95.33)	0.021	2.54* (0.30–∞)	0.300
Farm	6	REF	0.288 (global) c	REF	0.089 (global) c	REF	<0.001 (global) a
	7	6.33 (0.45–99.89)	0.171	30.27 (1.63–562.91)	0.022	9.36* (1.39–∞)	0.011
	8	0.73 (0.05–9.83)	0.811	71.63 (2.98–1721.31)	0.008	2.12* (0.22–∞)	0.310
	9	0.71 (0.02–22.64)	0.849	7.65 (0.36–163.68)	0.193	1.41* (0.04–∞)	0.415
	10	25.34 (0.80–797.52)	0.066	242.30 (4.50–13042.56)	0.007	18.9* (2.80–∞)	0.001
Year	2011	REF	0.182 (global) b	REF	0.420 (global) c	REF	0.230 (global) c
	2012	2.53 (0.87–7.32)	0.086	0.46 (0.14–1.48)	0.190	5.72 (0.55–59.64)	0.145
	2013	1.56 (0.38–6.39)	0.533	0.69 (0.15–3.19)	0.641	0.66 (0.05–9.13)	0.755
		IncFiip96a		Colye4449 d		fosA7
		OR (95%CI)	p-value	OR (95%CI)	p-value	OR (95%CI)	p-value
Source type	Swine manure pit	REF	<0.001 (global) a	REF	0.758 (global) c	REF	0.831 (global) b
	Raccoon	26.39* (4.37–∞)	<0.001	0.62 (0.17–2.19)	0.457	0.78 (0.25–2.40)	0.663
	Soil	13.54 (2.10–∞)	0.003	0.68 (0.17–2.67)	0.580	0.67 (0.19–2.38)	0.539
Farm	6	REF	0.117 (global) c	REF	<0.001 (global) a	REF	<0.001 (global) b
	7	95.69 (1.58–5800.67)	0.029	13.40* (2.06–∞)	0.003	10.73 (1.31–87.67)	0.027
	8	325.49 (3.35–31575.21)	0.013	1.00 (0–∞)	NE	0.52 (0.03–8.75)	0.652
	9	20.59 (0.31–1363.38)	0.157	40.00* (5.54–∞)	<0.001	32.86 (3.56–303.41)	0.002
	10	1735.86 (6.29–478596)	0.009	6.32* (0.81–∞)	0.056	4.79 (0.52–44.20)	0.167
Year	2011	REF	0.319 (global) c	REF	0.026 (global) a	REF	0.241 (global) c
	2012	0.41 (0.10–1.65)	0.209	0.77 (0.30–1.98)	0.661	0.81 (0.33–2.00)	0.644
	2013	0.25 (0.03–1.82)	0.173	0.09* (0–0.57)	0.006	0.23 (0.04–1.27)	0.093

REF = referent group, CI = confidence interval, NE = not estimated.

* Median unbiased estimates obtained with exact logistic regression.

a Exact logistic regression model.

b Ordinary logistic regression model.

c Multi-level model. A random intercept to account for repeated sampling of animals and swine manure pits was retained: IncX1 farm intraclass correlation coefficient (ICC): 55.7% (95%CI: 8.1–94.7%); IncFIIS farm ICC: 61.9% (95%CI: 24.8–88.9%); IncFIIS year ICC: 53.6% (95%CI: 19.2–84.9%); IncX3 year ICC: 54.4% (95%CI: 18.2–86.5%); IncFiip96a farm ICC: 70.1% (95%CI: 32.6–91.9%); IncFiip96a year ICC: 64.5% (95%CI: 29.4–88.8%); Colye4449 source ICC: 6.8% (95%CI: 0.0–99.2%); fosA7 year ICC: 10.3% (95%CI: 0.0–96.1%).

d The odds of Colye4449 were significantly lower in 2013 compared to 2012 (OR: 0.11*, 95%CI: 0–0.70).

** Contrasts are available in S3 Table.

Of the eight Inc types and four resistance genes examined statistically, only two Inc types (i.e., IncI1[alpha], IncFIB[AP001918]) and one gene (sul2) were significantly associated with source or farm location, and none were associated with the year of sampling (Table 7). IncI1(alpha) was significantly associated with farm location, whereas sul2 and IncFIB(AP001918) were associated with source type, and both were detected more frequently in raccoons compared to swine manure isolates. Contrasts are available in S4 Table. Although model assumptions were met for sul2 and IncFIB (AP001918) models, the random intercept was not retained since the variance components were negligible (<1x10-3), and the BIC favoured models without the random intercept.

Table 7

Univariable logistic regression models,, assessing the association between source type, farm location, and year of sampling, and the occurrence of select antimicrobial resistance genes and plasmid incompatibility (Inc) types in phenotypically resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 96).

		tet(A)		tet(B)		bla TEM-1		sul1
		OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
Source type	Swine manure pit	REF	0.220 (global) a	REF	0.117 (global) a	REF	0.594 (global) a	REF	0.133 (global) a
	Raccoon	1.49 (0.47–4.69)	0.498	0.47 (0.18–1.20)	0.113	1.85 (0.53–6.41)	0.335	4.83 (0.84–27.93)	0.078
	Soil	2.27 (0.89–5.83)	0.088	0.31 (0.09–1.07)	0.064	1.11 (0.38–3.27)	0.851	1.41 (0.24–8.24)	0.700
Farm	6	REF	0.798 (global) a	REF	0.165 (global) a	REF	0.060 (global) a	REF	0.676 (global) a
	7	1.98 (0.63–6.26)	0.245	0.41 (0.12–1.41)	0.157	0.49 (0.10–2.34)	0.372	0.58 (0.04–5.66)	0.660
	8	1.56 (0.35–6.94)	0.563	1.30 (0.29–5.76)	0.730	0.90 (0.14–5.66)	0.911	0.56* (0–5.65)	0.536
	9	1.09 (0.30–3.91)	0.896	0.40 (0.10–1.61)	0.197	4.05 (1.02–16.00)	0.046	1.41 (0.16–12.20)	0.999
	10	1.41 (0.43–4.68)	0.571	1.43 (0.43–4.69)	0.555	1.44 (0.36–5.67)	0.602	1.11 (0.13–9.37)	0.999
Year	2011	REF	0.077 (global) a	REF	0.417 (global) a	REF	0.836 (global) a	REF	0.688 (global) a
	2012	0.32 (0.09–1.05)	0.060	2.08 (0.68–6.35)	0.197	1.16 (0.35–3.84)	0.812	1.99 (0.36–10.98)	0.425
	2013	1.10 (0.45–2.72)	0.828	1.17 (0.45–3.02)	0.750	0.81 (0.29–2.29)	0.691	1.67 (0.37–7.53)	0.507
		sul2		ant(3”)-Ia		aph(3”)-Ib		aph(6)-Id
		OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
Source type	Swine manure pit	REF	0.017 (global) a	REF	0.876 (global) a	REF	0.638 (global) a	REF	0.638 (global) a
	Raccoon	12.86 (1.41–117.20)	0.024	0.86 (0.21–3.41)	0.827	1.69 (0.54–5.26)	0.363	1.69 (0.54–5.26)	0.363
	Soil	7.5 (0.90–62.61)	0.063	0.74 (0.24–2.31)	0.606	1.11 (0.44–2.79)	0.828	1.11 (0.44–2.79)	0.828
Farm	6	REF	0.636 (global) b	REF	0.987 (global) a	REF	0.283 (global) a	REF	0.283 (global) a
	7	0.48 (0.02–13.29)	0.666	1.19 (0.28–5.10)	0.817	0.61 (0.19–1.92)	0.399	0.61 (0.19–1.92)	0.399
	8	0.22 (0.00–31.73)	0.555	1.19 (0.18–7.84)	0.858	2.14 (0.44–10.39)	0.346	2.14 (0.44–10.39)	0.346
	9	0.05 (0.00–4.42)	0.190	1.02 (0.19–5.29)	0.983	0.38 (0.10–1.44)	0.155	0.38 (0.10–1.44)	0.155
	10	2.97 (0.10–90.37)	0.532	1.48 (0.34–6.48)	0.599	0.83 (0.25–2.72)	0.763	0.83 (0.25–2.72)	0.763
Year	2011	REF	0.788 (global) a	REF	0.922 (global) a	REF	0.354 (global) a	REF	0.354 (global) a
	2012	1.60 (0.38–6.79)	0.524	0.91 (0.23–3.48)	0.886	1.97 (0.65–5.95)	0.230	1.97 (0.65–5.95)	0.230
	2013	1.40 (0.40–4.87)	0.597	0.79 (0.25–2.46)	0.688	0.91 (0.37–2.27)	0.845	0.91 (0.37–2.27)	0.845
		IncFIB(AP001918)		IncI1(alpha)		IncFII		IncY
		OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
Source type	Swine manure pit	REF	0.013 (global) a	REF	0.907 (global) a	REF	0.196 (global) a	REF	0.998 (global) b
	Wildlife	5.14 (1.50–17.57)	0.009	1.19 (0.24–5.99)	0.832	0.18 (0.02–1.60)	0.124	1.37 (1.8e-08–1.04e+08)	0.973
	Soil	3.27 (1.18–9.14)	0.023	0.84 (0.21–3.43)	0.812	0.63 (0.20–2.03)	0.440	1.14 (1.9e-08–6.5e+07)	0.989
Farm	6	REF	0.807 (global) b	REF	0.028 (global) c	REF	0.343 (global) a	REF	0.050 (global) c
	7	1.95 (0.35–10.81)	0.446	3.97 (0.64–43.91)	0.140	4.19 (0.43–40.62)	0.216	0.92 (0.01–75.19)	0.999
	8	2.38 (0.22–25.77)	0.476	0.93 (0.00–12.47)	0.564	9.43 (0.84–105.79)	0.069	2.3* (0.00–89.70)	0.999
	9	1.26 (0.23–7.02)	0.787	2.20 (0.22–29.58)	0.634	4.71 (0.44–49.94)	0.198	6.46 (0.56–348.13)	0.144
	10	0.59 (0.09–3.69)	0.574	0.44 (0–5.80)	0.489	5.18 (0.53–50.65)	0.158	6.60 (0.65–339.59)	0.088
Year	2011	REF	0.512 (global) a	REF	0.227 (global) b	REF	0.865 (global) a	REF	0.518 (global) b
	2012	1.18 (0.39–3.50)	0.770	0.54 (0.08–3.74)	0.529	1.13 (0.24–5.31)	0.878	0.07 (0.00–29.73)	0.395
	2013	0.66 (0.26–1.65)	0.374	0.13 (0.01–1.34)	0.087	1.40 (0.40–4.88)	0.597	0.23 (0.01–4.03)	0.318

REF = referent group, OR = odds ratio.

* Median unbiased estimates obtained with exact logistic regression.

a Ordinary logistic model.

b Multi-level model. A random intercept to account for repeated sampling of animals and swine manure pits retained in the following models: sul2 farm intraclass correlation coefficient (ICC): 74.3% (95%CI: 27.8–95.5%); IncFIB(AP001918) farm ICC: 32.1% (95%CI: 2.9–88.2%); IncI1(alpha) year ICC: 35.2% (95%CI: 2.4–92.1%); IncY source type ICC: 99.8% (95%CI: 98.9–99.9%); IncY year ICC: 99.8% (95%CI: 97.6–99.9%).

c Exact logistic regression model.

** The following contrasts were statistically significant (p<0.05): the odds of IncFIB(AP001918) were lower in swine compared to soil (OR: 0.30, 95%CI: 0.11–0.85); the odds of IncI1(alpha) were lower on farm 10 versus farm 7 (OR: 0.10*, 95%CI: 0–0.70).

Discussion

Using a course-grained epidemiological approach informed by whole-genome sequence data to assess for potential transmission of Salmonella, E. coli and related AMR determinants at the source level provides evidence suggestive of local transmission of certain strains in swine farm environments, or exposure to a common environmental source. We frequently identified findings consistent with soil-raccoon transmission of Salmonella, E. coli and AMR determinants, but the evidence provided by our study suggests that there is limited transmission of Salmonella and associated resistance genes between raccoons and manure pits in the swine farm environment. Salmonella serovars with a broad-host affinity such as S. Typhimurium and S. Infantis [30] were identified in all sampling sources (i.e., raccoons, swine manure pits, soil); on average, these serovars displayed greater diversity in cgMLST profiles (>50 allelic differences) than serovars such as S. Poona and S. Agona, which were also identified in all sampling sources, and tended to be more related (differed at <15 cgMLST loci on average). Additionally, we identified “clusters” of certain Salmonella serovars that were found in multiple sources but restricted to certain years and farms (i.e., S. Poona on farm 8 in 2013; S. Typhimurium ST19 on farm 6 in 2012; S. Thompson on farm 8), albeit these observations are based on small sample sizes (n<20) and differences could not be tested statistically. All sources were found to contain at least one or more internationally important Salmonella sequence types which have been implicated in human illness (e.g., S. Typhimurium ST19, S. Infantis ST32) [27], but, overall, very few isolates exhibited phenotypic AMR (<5%), as previously observed by Bondo et al. [19]. As a result of this low prevalence of AMR among Salmonella, and apart from one gene (fosA7), we were unable to examine patterns in the distribution of resistance genes or assess risk factors statistically. Conversely, our inclusion of untyped E. coli based on demonstrated phenotypic AMR unquestionably resulted in a sampling bias [31], but this approach enabled statistical assessments which contribute to a preliminary understanding of the dynamics and movement of AMR in enteric bacterial populations in these different sources.

Similar to other studies [32, 33], the use of in silico tools for the identification of resistance genes in this study was generally reliable, although we did not assess drug classes for which chromosomal resistance plays an important role (i.e., quinolones). The overall test sensitivity and specificity of genotypic AMR identification in our study (>97% for both Salmonella and E. coli) was comparable to, or greater than, values reported by these two other studies (range: 75–97%) [32, 33]; in some cases, these differences may be attributed–at least in part–to the antimicrobial panel and bioinformatics pipeline used, the population of Salmonella investigated (i.e., the sampling sources and serovars included), and the associated sample size (i.e., a small number of isolates resulting in wide confidence intervals around point estimates of sensitivity or specificity). The specificity for aminoglycoside resistance among E. coli in our study (80%) was lowest of all drug classes, which is also consistent with findings from both of these studies [32, 33], in which the specificity of genotypic identification of streptomycin using WGS data was also the lowest of all drugs evaluated by these studies. The presence of silent (i.e., unexpressed) resistance genes may account for these findings [34] and should be considered in future genotypic AMR evaluations which do not have access to corresponding phenotypic AMR data.

For the purposes of validating phenotypic results and investigating possible transmission of resistance genes between different sources, our in silico AMR data has provided insights about the movement of AMR determinants in a southern Ontario agroecosystem. In some cases, resistant Salmonella with the same phenotypic AMR patterns contained different genes responsible for conferring resistance. Our findings of similar cgMLST profiles (<10 allelic differences) [24], along with the presence of the same resistance genes among isolates that were spatially or temporally linked were suggestive of the dissemination of closely related isolates. Such cases included two S. Hadar ST33 (one soil, one raccoon) from the same farm in the same year, and two S. Typhimurium ST19 DT104 (one raccoon, one swine manure pit) from different farms in the same month and year; these findings highlight the potential occurrence of on-farm as well as between-farm transmission of Salmonella between different sources. Similar or identical cgMLST profiles were also frequently identified among raccoon and soil isolates for a variety of serovars, including, but not limited to, the following: S. Newport, S. Agona, S. Thompson, S. Hartford, S. Paratyphi B var. Java. In conjunction with previous work comparing samples from raccoon paws to their corresponding fecal samples [35], these particular findings suggest that transmission between raccoons and their immediate environment is likely occurring.

Among E. coli isolates, a total of 25 multidrug resistant isolates were identified. One such E. coli isolate identified in a raccoon with phenotypic AMR pattern AMC-AMP-FOX-TIO-CRO-CHL-STR-SOX-TCY-SXT contained the sole blaCMY-2 identified within our study. In general, the types of genotypic resistance identified in these populations of Salmonella and E. coli conferred resistance to aminoglycosides, tetracyclines, and folate pathway inhibitors; other more concerning types of resistance to macrolides, phenicols, and fluoroquinolones were rarely identified, or absent altogether in this study.

Many predicted plasmids found in Salmonella were not associated with presence of resistance genes. Two Inc types were present in ~40% of all Salmonella isolates, the majority of which were pan-susceptible; the abundance of these particular Salmonella genotypes may be related to exposure to disinfectants or other environmental stressors at these sites, and related selection of plasmids containing virulence genes or genes conferring resistance to disinfectants or heavy metals (not evaluated here) [36]. The predicted plasmids appearing in Salmonella were very distinct from those identified in the resistant E. coli population; few Inc types were commonly identified in both organisms. Previous work by Varga et al. [37] demonstrated a lack of association between phenotypic resistance patterns for Salmonella and generic E. coli isolates originating from the same swine manure sample. We made a similar observation based on our examination of a small number of raccoons that carried both resistant Salmonella and resistant E. coli; we identified no similarities among AMR determinants between organisms originating from the same raccoon, manure pit, or soil sample. However, the examination of only one bacterial isolate per fecal sample may not capture or represent important and relevant aspects of the microbiome and related resistome [38].

Major differences in the distribution of AMR genes and predicted plasmids between E. coli and Salmonella were identified. The majority of AMR genes and Inc types in E. coli analyzed statistically were not significantly associated with any of the predictor variables, whereas all of the Inc types and the single gene analyzed in Salmonella (i.e., fosA7) were associated with source type or farm location, or both. The lack of association for most of the AMR determinants in E. coli with source, farm, or year of sampling suggests widespread sharing of E. coli AMR determinants between sources in this region, or common exposure to AMR pollution in environmental sources (e.g., water). However, these findings should be interpreted in light of our selection of a population of resistant E. coli, and future examination of both susceptible and resistant isolates will provide important context for these findings. For those Inc types and genes associated with farm location, the farm with the highest odds of these outcomes was not consistent and varied depending on the particular Inc type or gene under examination. In contrast, Inc types and genes associated with source type were consistently more likely to be identified in raccoons compared to swine manure pit isolates, and, for certain outcomes, they were also more likely to be isolated from soil compared to swine manure pits. These findings suggest that, for certain AMR determinants (particularly in Salmonella), limited exchange between raccoons and soil with swine manure pits is occurring, similar to findings from previous work in this study region examining Campylobacter isolates from raccoons and livestock (swine, dairy, beef) [39].

The identification of sequence types of international importance in raccoons, soil, and manure pits sampled in swine farm environments, in particular S. Typhimurium ST19 (the most prevalent sequence type among S. Typhimurium isolates globally [40]) is suggestive of widespread circulation of these strains in this region of Ontario. To date, there are few studies that have examined subtyping data and gene-level AMR data in raccoons [18, 19, 41, 42]; this study contributes new data to the literature concerning common serovars, sequence types, microbial population structure, resistance genes, and predicted plasmids carried by a rural raccoon population. In wild birds, where genomic investigations are becoming increasingly common, Enterobacteriales containing resistance genes to high-priority antimicrobials (e.g., blaCTX-M-65, blaIMP-4, mcr-1), and international clones have been identified [8, 43–45]. The resistance identified in raccoons and other sources on swine farms in our study mirrors that found in swine in other parts of the world (e.g., sulfonamides, aminoglycosides, tetracyclines) [46-48]. Our findings of widespread tetracycline resistance genes (tetA, tetB) that were not associated with particular sources, locations, or years are plausibly driven by the swine farm environment, since tetracyclines, among other antimicrobials, are among the more commonly used antimicrobials in the Canadian swine industry [49, 50]. A previous study examining AMR in wild small mammals in a variety of environments in the same study region also found that resistance to tetracyclines (conferred by tetA) was significantly more likely to be identified in generic E. coli from animals trapped on swine farms compared to residential areas [51]. Overall, our findings are consistent with recent mounting evidence that the use of antimicrobials in agriculture is a major driver of AMR in intensive farm environments [52-54]. In the future, exploration of enteric bacteria and AMR carried by raccoons in different types of environments, including cities, may contribute valuable information concerning the impact of agricultural and urban environments on the microbiome of these animals.

Limitations

The low overall prevalence of resistance among Salmonella isolates on swine farms presented an obvious challenge for the study of the movement of AMR determinants, and, in many cases, precluded statistical assessments of these data. Our low effective sample sizes did not permit multivariable modeling, or the ability to account for potential confounding by serovar (due to the sheer number of serovars identified, models could not converge). Our univariable analyses do not adequately capture the complexities of AMR in the ecosystem, but they represent an important first step in this process. Similarly, our analysis of Inc types should be interpreted with caution since we did not reconstruct plasmids or characterize mobility, thus, future work would be strengthened with confirmation of these aspects of plasmid biology [55]. Identification of the precise location of resistance genes either on plasmids or within chromosomes in future work will also help to provide further insights about AMR transmission and movement. As previously alluded to, the true relationships between predicted plasmids and the risk factors examined here are potentially obscured by our inclusion of only E. coli demonstrating phenotypic resistance. Moreover, inclusion of only one E. coli isolate per fecal sample limits our understanding of the transmission of AMR determinants within the greater gut microbiome [38], and of the true microbial population structure. The lack of similarities between Salmonella and E. coli obtained from the same animal therefore do not constitute definitive evidence that AMR gene transmission is not commonly occurring in the gastrointestinal tract of raccoons. Finally, the measures of association reported in our analyses do not account for the initial probability of isolating the organism in each sample. To illustrate this point, consider that the prevalence of resistant E. coli was highest in raccoons, and lowest in swine manure samples (35% vs 16%); the odds of certain predicted plasmids were higher in raccoons compared to swine manure isolates, but these odds were always relative to samples that already contained a resistant E. coli isolate.

Conclusions

A diversity of Salmonella serovars were isolated from the raccoon population in this study, some of which have been implicated in human clinical cases in the study region (e.g., S. Thompson, S. Newport). Findings from our preliminary epidemiological investigation are suggestive of local transmission of certain strains of Salmonella, E. coli, and related AMR determinants between raccoons and environmental sources (i.e., soil, swine manure pits) locally on farms, and between farms in the region. Overall, our findings suggest that transmission of certain Salmonella serovars and related genes and plasmids is commonly occurring between soil and raccoons, but is rarely occurring between raccoons and swine manure pits. The highly variable distributions of resistance genes and predicted plasmids among different sources and locations revealed different epidemiological patterns for various AMR determinants in Salmonella and E. coli, highlighting the complexities underlying AMR transmission and maintenance within the ecosystem. The integration of whole-genome sequence data within an epidemiological approach can help to guide and provide focus for future genomic investigations focused on transmission dynamics and phylogenetics. More comprehensive sampling of farm environments, and additional environmental sources, as well as a thorough examination of both susceptible and resistant E. coli isolates, is warranted.

Gene accession numbers for Salmonella and Escherichia coli isolates.

(XLSX)

Click here for additional data file.

Multi-locus sequence types identified using whole-genome sequencing data from antimicrobial resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

(DOCX)

Click here for additional data file.

Serovars identified using whole-genome sequencing data from phenotypically resistant Escherichia coli isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

(DOCX)

Click here for additional data file.

Contrasts from univariable logistic regression modelsa,b (Table 6) assessing the statistically significant associations between farm location and the occurrence of select antimicrobial resistance genes and plasmid incompatibility (Inc) types in Salmonella enterica isolates from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

(DOCX)

Click here for additional data file.

Contrasts from univariable logistic regression modelsa,b (Table 7) assessing the statistically significant associations between farm location, source type, and the occurrence of select antimicrobial resistance genes and plasmid incompatibility (Inc) types in Escherichia coli isolates from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013.

(DOCX)

Click here for additional data file.

15 Oct 2021

PONE-D-21-30211Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, CanadaPLOS ONE

Dear Dr. Vogt,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please address all of the comments of the three reviewers, and particularly, the comments of reviewer #2.  This reviewer suggested comparing the data to a global Salmonella database to observe for broader global linkages, comparing phenotype to genotype calls, comparing the WGS data to the AMRFinderPlus database and uploading as metadata at AMRFinderPlus SRA, providing a list of all NCBI accession numbers for all genomes discussed, modifying Table 1 as suggested, providing detailed information for each unique isolate, providing phylogenetic tree testing, constructing a transmission network using StrainHub, and improving labeling of Figures.

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Reviewer #3: Yes

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Reviewer #1: The manuscript entitled “Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, Canada” presents a study in which whole genomes of Salmonella and antimicrobial resistant E. coli previously isolated from swine manure pits, raccoons, and soil in Ontario were analyzed and compared to assess transmission among these sources. Some overlapping bacterial sequence types, AMR genes, and plasmid types were identified in multiple sources and associated with particular sources or years. The authors did an excellent job presenting results and describing study limitations. This manuscript adds value to the field by both describing whole genome sequencing data of antimicrobial resistant bacteria found in wildlife and the environment and by assessing epidemiological links based on those data. I have a few relatively minor comments discussed below.

Lines 157-159: Please discuss selection of k values here and why different values were selected for Salmonella and E. coli. Also specifically state that these trees were based on cgMLST.

Lines 273-275: What about E. coli sequence types commonly found in humans (e.g. UPEC strains)?

GrapeTree figures. Please include in the figure legend what K means. (i.e. for E. coli, each circle includes isolates differing by no more than 50 alleles).

Line 503: The term “extensively drug resistant” has precise definitions (e.g. Magiorakos et al., 2012, https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext). Please check the use of this term and provide a reference for the definition used here.

Lines 510-521: This is not new information and was already published and discussed in Bondo et al., 2016. Please remove or discuss how WGS data influenced this assessment.

Lines 541-544: Many of the AMR genes found are very common in other environments and hosts. I suggest including an alternative explanation (e.g. widespread presence of these genes in other sources). Additionally, “Or the presence of widespread closely related isolates” is not a very plausible explanation given that you identified many (49) different E. coli sequence types.

Lines 606-608: Are the results suggestive of transmission among those three sources? What about other sources not sampled? Given the high diversity of STs and what it stated in Lines 611-615.

Reviewer #2: Line 101 “For the present study, we included only isolates originating from swine farm environments” Authors should consider wide-spread comparison of global samples by uploading and comparing their data to a global salmonella database like NCBI Pathogen detection where they might see broader global linkages.

Line 129-130, 184, 319 Authors should directly compare phenotype to genotype calls and report any discrepancies. All WGS data should be compared to AMRFinderPlus assessment of AMR gene presence which provides AMR for more then 15 phenotypically determined antibiograms. Additionally, all Antibiograms should be uploaded as metadata at AMRFinderPlus SRA and biosample so that this phenotype to genotype data can be cataloged to improve future AMR predictions. Incongruences were partly discussed line 380 but should be expanded to explicitly discuss all failed predictions as these represent new AMR genes or alleles that are not resistant.

Table 1. Data availability and public release of data. The authors are encouraged to provide a list of all NCBI accession numbers for all genomes discussed in the manuscript. The investigators should provide two additional columns to Table 1. One for strain identification and a second for NCBI accession number of the WGS data per isolate. The authors could also add to the list stress and pathogenicity genes made available at NCBI AMRFinderPlus once the data has been publicly released. They should release the data now to add these results to their manuscript. The authors should consider listing each unique isolate included in their study and include all detailed information for each isolate. Just providing aggregated results is insufficient to reproduce their results.

Line 231 As with the Salmonella, all E. coli Strain identification and NCBI accession numbers should also be added to Table 1 for full transparency for all isolates included in the study.

Line 264, 273. If the authors release all of their data publicly at the NCBI Pathogen Detection web site then they can see if their new WGS data clusters with any known clinical isolate rather than speculating on the possibility they can see if any direct clusters exist in the public release genomes for Salmonella and E. coli. The authors are advised to publicly release their data and make these comparisons and report the results.

Line 172 Using the snippy results the authors are recommended to provide phylogenetic tree testing whether any of these isolates cluster with any others. Independently they can see what clusters at NCBI Pathgen detection.

Line 446-447 To study the pathogen transmission dynamics the authors should consider constructing a transmission network using StrainHub, version 0.2.0. This is a phylogenetic approach to understanding transmission dynamics.

Figures. The investigators have not labeled many of the terminal nodes in their figures with the strain identification so none of these figures is interpretable to the detail of what is claimed in the manuscript. All terminal nodes should be labeled with a unique strain ID.

Table 1. The investigators have not provided NCBI Accession numbers for the genomes described in this study and so none of the Data is currently available publicly released so I am unable to test any of the claims made by these authors. All data must be publicly released for PlosOne to promote openness and transparency to advance science.

Reviewer #3: 1. I did not see the big project numbers, Sequence Read Archive (SRA) numbers or gene accession numbers for whole genome sequencing data. Where were the data deposited?

2. There were 3 Salmonella and 2 E. coli isolates not typerable by MLST. Please provide an explanation for this.

3. Please explain why 15 antibiotics were chosen for this study and why fosfomycin was not included in the panel

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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2 Nov 2021

Response to Reviewers

We thank all reviewers and the editor for their helpful feedback to improve the manuscript. Our responses are below. Line numbers correspond to tracked changes version.

Please address all of the comments of the three reviewers, and particularly, the comments of reviewer #2. This reviewer suggested comparing the data to a global Salmonella database to observe for broader global linkages, comparing phenotype to genotype calls, comparing the WGS data to the AMRFinderPlus database and uploading as metadata at AMRFinderPlus SRA, providing a list of all NCBI accession numbers for all genomes discussed, modifying Table 1 as suggested, providing detailed information for each unique isolate, providing phylogenetic tree testing, constructing a transmission network using StrainHub, and improving labeling of Figures.

We have modified Table 1 as suggested, and also added a supplementary table with a list of all NCBI accession numbers for all genomes included (File S1). We appreciate the suggestions for potential avenues for analyses using a fine-grained genomics approach. However, our goal in using WGS data was to integrate these data into a broader traditional epidemiological approach. Moreover, in the interest of keeping our manuscript streamlined and focused on the broader epidemiological assessment which seeks to characterize population-level patterns and distributions of genes/plasmids among different sources, we have opted to keep our existing analyses as they are. We feel that the addition of further analyses with a different aim would overcomplicate the manuscript and obscure the focus, which is predominantly on source-level (not strain-level) transmission, and on the overarching epidemiological patterns of AMR determinants. The use of WGS within traditional epidemiological frameworks is an emerging field, and there is limited available literature combining these approaches (mostly due to limited sample sizes for statistical analyses), thus, the value of this manuscript lies in the use of WGS-derived outputs within a traditional epidemiological analysis. Overall, we see many of these suggestions as valuable avenues for future work focusing on the strain-level aspects of this population of isolates, and with open data we encourage other researchers to build on this work using a finer-grained genomics-focused approach.

In addition, we are currently working on submitting a related manuscript comparing the Salmonella isolates from this previous wildlife study with clinical human and livestock isolates obtained from the same geographic region and time period. We agree that performing a global assessment and comparison of the farm-level isolates in this manuscript to other external sources would be a valuable assessment, however, inclusion of this type of analysis in the present manuscript would tread on the work performed in our manuscript that is in preparation. In order to make clear our epi-based approach for the reader, we have modified the first line of the discussion to better situate this work in the literature, as primarily a source-level epidemiological assessment. Thus, phylogenetic tree testing to determine evolutionary history and construction of detailed transmission networks are beyond the scope of the current manuscript.

Since the figures were only provided as a means of qualitatively displaying the population structure and overlap of different organisms between different sources, we maintain that it would impede interpretation of the figures if additional strain level information was added (since our sample sizes are considerable---n=159 and n=96 for Salmonella and E. coli, respectively). All comparisons made in text were performed by comparing cgMLST types in R, and were not performed using GrapeTree. We have added text to the methods to convey the purpose of creating minimum spanning trees (lines 161-175).

Our sequence data were submitted to Genbank on July 26, 2021, and were scheduled to be released on September 30, 2021, but I just confirmed with them that, unfortunately, due to a backlog, the data were only recently released, on October 29, 2021. Please see BioProject number PRJNA745182.

Our results can be replicated by linking the sequence data and strain numbers with the epidemiological data previously released by Bondo et al. We hesitate to add in accession numbers into the text for the section in between lines 300-312, since listing up to 29 accession numbers, for example, would severely hinder the communication of these findings in text. These analyses can easily be replicated in R, as we have specified which serovars and sources were examined, and there are a limited number of isolates with those epidemiological attributes.

Reviewer #1: The manuscript entitled “Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, Canada” presents a study in which whole genomes of Salmonella and antimicrobial resistant E. coli previously isolated from swine manure pits, raccoons, and soil in Ontario were analyzed and compared to assess transmission among these sources. Some overlapping bacterial sequence types, AMR genes, and plasmid types were identified in multiple sources and associated with particular sources or years. The authors did an excellent job presenting results and describing study limitations. This manuscript adds value to the field by both describing whole genome sequencing data of antimicrobial resistant bacteria found in wildlife and the environment and by assessing epidemiological links based on those data. I have a few relatively minor comments discussed below.

Lines 157-159: Please discuss selection of k values here and why different values were selected for Salmonella and E. coli. Also specifically state that these trees were based on cgMLST.

Thank you for the suggestion. We have specified our rationale and approach to selection of k values for the different figures, and we have also clarified in text that the trees were based on cgMLST (lines 161-175).

Lines 273-275: What about E. coli sequence types commonly found in humans (e.g. UPEC strains)?

Thank you for the suggestion. A statement has been added in text (lines 293-295) and a footnote to Table S1.

GrapeTree figures. Please include in the figure legend what K means. (i.e. for E. coli, each circle includes isolates differing by no more than 50 alleles).

All figure headings have been modified as suggested.

Line 503: The term “extensively drug resistant” has precise definitions (e.g. Magiorakos et al., 2012, https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext). Please check the use of this term and provide a reference for the definition used here.

Thank you for catching this error. We have corrected the text and used the term MDR instead (line 531).

Lines 510-521: This is not new information and was already published and discussed in Bondo et al., 2016. Please remove or discuss how WGS data influenced this assessment.

Thank you for catching the redundancy. These lines have been removed.

Lines 541-544: Many of the AMR genes found are very common in other environments and hosts. I suggest including an alternative explanation (e.g. widespread presence of these genes in other sources). Additionally, “Or the presence of widespread closely related isolates” is not a very plausible explanation given that you identified many (49) different E. coli sequence types.

We agree this wasn't the most plausible explanation. The text has been changed to reflect that the most likely alternative explanation was "common exposure to AMR pollution in environmental sources". (lines 573-574).

Lines 606-608: Are the results suggestive of transmission among those three sources? What about other sources not sampled? Given the high diversity of STs and what it stated in Lines 611-615.

Thank you for catching the apparent contradiction in our conclusion. We have removed the text that was contradictory and substituted a summary statement conveying that the epidemiological pattern of each AMR determinant was variable (lines 639-651). We maintain that there does appear to be evidence suggestive of transmission for certain strains and serovars, but not all (line 637).

Reviewer #2: Line 101 “For the present study, we included only isolates originating from swine farm environments” Authors should consider wide-spread comparison of global samples by uploading and comparing their data to a global salmonella database like NCBI Pathogen detection where they might see broader global linkages.

See above comments. We agree that a comparison of these isolates to a global database would be useful and interesting, however, the focus of this manuscript was to use an epi-based approach with WGS-derived data originating from swine farm environments specifically. Our related manuscript that is currently in preparation addresses Salmonella and related AMR transmission between these swine farm isolates with human clinical cases and livestock isolates from the same geographic region and time period. We are cautious about keeping the objectives of the two manuscripts distinct.

Line 129-130, 184, 319 Authors should directly compare phenotype to genotype calls and report any discrepancies. All WGS data should be compared to AMRFinderPlus assessment of AMR gene presence which provides AMR for more then 15 phenotypically determined antibiograms. Additionally, all Antibiograms should be uploaded as metadata at AMRFinderPlus SRA and biosample so that this phenotype to genotype data can be cataloged to improve future AMR predictions. Incongruences were partly discussed line 380 but should be expanded to explicitly discuss all failed predictions as these represent new AMR genes or alleles that are not resistant.

Since we have so many isolates (n=255), our objective in performing a class-level (not gene-level) assessment of genotype to phenotype was to provide a course-grained, succinct assessment to validate our epidemiological analyses. In line with our objectives, isolates with too many failed predictions were excluded from statistical analyses to remove potentially unreliable information from our analyses. As such, we prioritized population-level analyses (in line with our epidemiological approach), rather than delving into missed predictions of individual isolates and discovery of new AMR genes. Resfinder and ABricate use a subset of the AMR genes in the AMRFinderPlus database, and cover all of the antimicrobials (and more!) that were previously tested phenotypically for in previous work by Bondo et al. Thus, we consider the use of Resfinder to be more than adequate for the scope of our paper.

Although we would not be opposed to depositing our sequence data to an additional repository to promote scientific advancements, this process represents a considerable time and financial burden to the primary author (i.e., unpaid work requiring access to high-speed internet, currently unavailable in the primary author's residence, and no access to university facilities). This request also goes above and beyond what is considered "Open Science" (and beyond what is required by Plos One for publishing). The phenotypic data are available from Bondo et al.'s previous release of data, and our sequence data are available from Genbank. We have provided all data needed for another researcher to perform gene-level comparisons of phenotype to genotype, if desired. Finally, the inclusion of redundant analyses (i.e., both class-level and gene-level comparisons) has the potential to overcomplicate the paper, since we already have three figures, seven tables, and four supplementary tables.

Table 1. Data availability and public release of data. The authors are encouraged to provide a list of all NCBI accession numbers for all genomes discussed in the manuscript.

A supplementary table has been added with these accession numbers (S1 File).

The investigators should provide two additional columns to Table 1. One for strain identification and a second for NCBI accession number of the WGS data per isolate.

Thank you for the suggestions, these have been added to the table 1.

The authors could also add to the list stress and pathogenicity genes made available at NCBI AMRFinderPlus once the data has been publicly released. They should release the data now to add these results to their manuscript.

Thank you for the suggestion, however, we feel these approaches are outside the scope of our epidemiologically-focused investigation looking at source-level transmission (see newly added lines 470-473). As of October 29, 2021 our data is available in Genbank if other researchers wish to pursue this research angle.

The authors should consider listing each unique isolate included in their study and include all detailed information for each isolate. Just providing aggregated results is insufficient to reproduce their results.

We have a total of 255 isolates included in this study, thus we feel that including a table with 255 lines is not very interpretable for the reader, given that we already have three figures, seven tables, and four supplementary tables. Using a population-level (epidemiological) approach necessitates presentation of aggregate results; this is particularly true of all of the statistical analyses. Traditional epidemiological analyses rarely present raw data, since the goal of the analysis is to provide an assessment of a large number of samples/isolates that cannot be assessed via qualitative examination alone. Providing our sequence data, associated epidemiological data, and methodological approaches is sufficient to reproduce our results.

Line 231 As with the Salmonella, all E. coli Strain identification and NCBI accession numbers should also be added to Table 1 for full transparency for all isolates included in the study.

The purpose of Table 1 was to present the antimicrobial resistant Salmonella isolates within the population, not the E. coli, since all 96 of those isolates were selected based on the presence of their phenotypic resistance.

Line 264, 273. If the authors release all of their data publicly at the NCBI Pathogen Detection web site then they can see if their new WGS data clusters with any known clinical isolate rather than speculating on the possibility they can see if any direct clusters exist in the public release genomes for Salmonella and E. coli. The authors are advised to publicly release their data and make these comparisons and report the results.

As previously mentioned, the objective of our epidemiologically-focused paper was to assess for transmission on swine farms, and not compare to a larger database, or other sources---this is the focus of our related manuscript that is currently in preparation. We insist on keeping the focus of these two papers distinct.

Line 172 Using the snippy results the authors are recommended to provide phylogenetic tree testing whether any of these isolates cluster with any others. Independently they can see what clusters at NCBI Pathgen detection.

Unfortunately, we were limited by computational power, thus the use of cgMLST was preferred, and we consider it sufficient for our epidemiological approach (additionally, this typing method is widely used by PulseNet for foodborne illness outbreak investigations, see our reference in the manuscript, Tolar et al., 2019). We were only able to perform a SNP-based approach to provide higher resolution for a small subset of specific isolates of interest. As we highlighted in the comments above, our approach was predominantly focused on source-level transmission using an epidemiological approach, rather than a specific genomics-based approach focused on evolutionary history and overlap with sources outside of the swine farm environment. As the GrapeTree documentation highlights, "It is difficult to infer clusters from classical phylograms when showing large numbers of isolates", as we have here. Furthermore, the primary author is currently limited by computational power to use SNIPPY to assess 159 and 96 isolates, respectively. A preliminary SNP-based assessment using Roary and FastTree was attempted in 2020, but the risk to the computer's hardware was too high to reattempt (so much power was needed that the computer died even though it was plugged into the wall outlet), and to consider correcting for potential confounding due to recombination (essential for any epidemiological analyses using WGS data) using alternative software to FastTree would require even further computational power.

Line 446-447 To study the pathogen transmission dynamics the authors should consider constructing a transmission network using StrainHub, version 0.2.0. This is a phylogenetic approach to understanding transmission dynamics.

We maintain that keeping our approach simple, and as a preliminary "scan" of the data using epidemiologically-informed statistical modeling, will help to lay the groundwork for future research using more complex genomic approaches, and allow researchers to focus-in on subsets of the isolates meriting further investigation. As such, these types of phylogenetic analyses were outside the scope of this paper, but will certainly be important in future papers with a different aim.

Figures. The investigators have not labeled many of the terminal nodes in their figures with the strain identification so none of these figures is interpretable to the detail of what is claimed in the manuscript. All terminal nodes should be labeled with a unique strain ID.

See above comment: since the figures were only provided as a means of qualitatively displaying the population structure and overlap of different organisms between different sources, we maintain that there is no need to clutter the figures with additional strain level information (since our sample sizes are considerable---n=159 and n=96 for Salmonella and E. coli, respectively). All comparisons made in text were performed by comparing cgMLST types of similar isolates and were not performed using GrapeTree. We have added text to the methods to convey the purposes of creating minimum spanning trees (lines 161-175).

Table 1. The investigators have not provided NCBI Accession numbers for the genomes described in this study and so none of the Data is currently available publicly released so I am unable to test any of the claims made by these authors. All data must be publicly released for PlosOne to promote openness and transparency to advance science.

The data were submitted to Genbank on July 26, 2021, and were to be released on September 30, 2021, but I just confirmed with them that due to a backlog, the data were not released until October 29, 2021. They are available now. See project number PRJNA745182 in Genbank. We have added a supplementary file (File S1) with information about all isolates included in the study, with the following attributes: the bioproject number, accession ids, biosample numbers, sample ids, sequencing ids, sampling sources, collection date, and bacterial species.

Reviewer #3: 1. I did not see the big project numbers, Sequence Read Archive (SRA) numbers or gene accession numbers for whole genome sequencing data. Where were the data deposited?

Apologies that the data were not available at the time of submission. The WGS data were supposed to be publicly released on September 30, 2021 under Bioproject # PRJNA745182, but I've contacted Genbank and due to a backlog the data weren't released until October 29, 2021. We have included a list of accession numbers for the isolates included in our work in S1 File, along with other attributes (see comment above).

2. There were 3 Salmonella and 2 E. coli isolates not typeable by MLST. Please provide an explanation for this.

The isolates that were not typeable were assigned a "—" by the MLST program. None of these isolates were assigned a MLST type because at least one allele was missing, or there was only a partial match. We have added text to clarify this (lines 259-262; 290-291).

3. Please explain why 15 antibiotics were chosen for this study and why fosfomycin was not included in the panel

The phenotypic assessment performed was part of previous work using the standard CIPARS or NARMS panel of antimicrobials, which, unfortunately, does not include fosfomycin. Unfortunately, our funding did not permit us to revisit phenotypic microbial assessments from previous work based on our WGS genotypic AMR findings. We have added the clarification in text that this work was previously performed (lines 117, 118, and 127).

Submitted filename: Response to Reviewers.docx

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5 Nov 2021

Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, Canada

PONE-D-21-30211R1

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9 Nov 2021

PONE-D-21-30211R1

Using whole-genome sequence data to examine the epidemiology of Salmonella, Escherichia coli and associated antimicrobial resistance in raccoons (Procyon lotor), swine manure pits, and soil samples on swine farms in southern Ontario, Canada

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