Virulome and genome analyses identify associations between antimicrobial resistance genes and virulence factors in highly drug-resistant Escherichia coli isolated from veal calves

Bradd J Haley; Seon Woo Kim; Serajus Salaheen; Ernest Hovingh; Jo Ann S Van Kessel

doi:10.1371/journal.pone.0265445

. 2022 Mar 17;17(3):e0265445. doi: 10.1371/journal.pone.0265445

Virulome and genome analyses identify associations between antimicrobial resistance genes and virulence factors in highly drug-resistant Escherichia coli isolated from veal calves

Bradd J Haley ^1,^*, Seon Woo Kim ¹, Serajus Salaheen ¹, Ernest Hovingh ², Jo Ann S Van Kessel ¹

Editor: Feng Gao³

PMCID: PMC8929554 PMID: 35298535

Abstract

Food animals are known reservoirs of multidrug-resistant (MDR) Escherichia coli, but information regarding the factors influencing colonization by these organisms is lacking. Here we report the genomic analysis of 66 MDR E. coli isolates from non-redundant veal calf fecal samples. Genes conferring resistance to aminoglycosides, β-lactams, sulfonamides, and tetracyclines were the most frequent antimicrobial resistance genes (ARGs) detected and included those that confer resistance to clinically significant antibiotics (bla_CMY-2, bla_CTX-M, mph(A), erm(B), aac(6’)Ib-cr, and qnrS1). Co-occurrence analyses indicated that multiple ARGs significantly co-occurred with each other, and with metal and biocide resistance genes (MRGs and BRGs). Genomic analysis also indicated that the MDR E. coli isolated from veal calves were highly diverse. The most frequently detected genotype was phylogroup A-ST Cplx 10. A high percentage of isolates (50%) were identified as sequence types that are the causative agents of extra-intestinal infections (ExPECs), such as ST69, ST410, ST117, ST88, ST617, ST648, ST10, ST58, and ST167, and an appreciable number of these isolates encoded virulence factors involved in the colonization and infection of the human urinary tract. There was a significant difference in the presence of multiple accessory virulence factors (VFs) between MDR and susceptible strains. VFs associated with enterohemorrhagic infections, such as stx, tir, and eae, were more likely to be harbored by antimicrobial-susceptible strains, while factors associated with extraintestinal infections such as the sit system, aerobactin, and pap fimbriae genes were more likely to be encoded in resistant strains. A comparative analysis of SNPs between strains indicated that several closely related strains were recovered from animals on different farms indicating the potential for resistant strains to circulate among farms. These results indicate that veal calves are a reservoir for a diverse group of MDR E. coli that harbor various resistance genes and virulence factors associated with human infections. Evidence of co-occurrence of ARGs with MRGs, BRGs, and iron-scavenging genes (sit and aerobactin) may lead to management strategies for reducing colonization of resistant bacteria in the calf gut.

Introduction

Escherichia coli are Gram-negative facultative anaerobes that are commensal members of the bovine gut as well as frequent members of environmental (non-animal) communities. Most E. coli are non-pathogenic, but a small subset has been linked to a range of mild to severe human diseases. These typically include self-limiting gastrointestinal (GI) infections as well as extra-intestinal infections such as bladder/urinary tract infections (UTIs), prostatitis, wound infections, pneumonia, sepsis, and meningitis in newborn babies. Infections are primarily caused by strains that carry various suites of virulence factors (VFs), but opportunistic infections can be caused by any strain, even those lacking major VFs. E. coli causes a significant number of GI infections annually in the United States and is responsible for 80% of UTIs [1–3]. Treatments for non-Shiga-toxigenic infections typically involve antimicrobial therapy, but pathogenic and non-pathogenic E. coli can be resistant to these drugs; some are multidrug-resistant (MDR) and can cause difficult-to-treat infections in humans and animals. Further, the E. coli population can serve as a reservoir of resistance genes that can transfer from commensal to pathogenic strains, or to other pathogenic organisms, such as Salmonella enterica [4–6].

Antimicrobial-resistant infections are an on-going human and animal health threat on a global scale, causing an extremely high, but not well-quantified, number of medical complications and fatalities each year [7–9]. Like antimicrobial-susceptible organisms, infections caused by resistant organisms can be nosocomial, community-acquired, waterborne, or foodborne. Foodborne and waterborne antimicrobial-resistant E. coli infections typically occur from fecal contamination of produce, meat, milk, eggs, and surface and drinking waters; community-acquired resistant E. coli infections, although transmitted differently than foodborne and waterborne infections, can be caused by strains that have a natural food animal host reservoir, such as poultry and cattle.

Beef cattle, dairy cows, and dairy calves are well documented reservoirs of antimicrobial-resistant bacteria and pathogens, but antimicrobial resistance carriage in veal calves remains understudied [10–13]. Calves raised for veal are usually the male calves from dairy herds. In the United States, milk is a major component of their diet until they are 16 to 18 weeks of age. About 15% of marketed veal calves are “bob veal” which are sold from birth up to three weeks of age. Recent studies have shown that dairy calf feces are a significant source of resistant bacteria and typically harbor a different suite of antimicrobial resistance genes (ARGs) and a greater concentration of ARGs than older lactating and dry cows [10–14]. However, the genetic mechanisms or management practices responsible for these age-related differences in resistance carriage remain unknown. Further, the characteristics of resistant bacteria shed by veal calves, which are raised under significantly different management practices than replacement dairy calves, have not been adequately studied. The aim of this study was to comprehensively evaluate the genomic characteristics, virulence profiles, and ARGs in MDR E. coli collected from veal calf feces, as well as the genomic features that co-occur with these genes and may influence resistance carriage in the veal calf gut. We further compared the genomic distance between isolates to investigate the relatedness of isolates collected from animals on different farms.

Materials and methods

In total, 66 confirmed MDR E. coli isolates collected from veal calves during a previous study [15] were selected for whole genome sequencing (WGS). Simultaneously, a subset of 19 pansusceptible isolates from the same study were selected as comparators for the MDR genome analyses. E. coli isolates were recovered from feces collected directly from individual calves on 12 farms at two time points; once immediately after arriving at the farm (typically ~ 1 week of age) and then again immediately prior to slaughter (at or around 24 weeks of age) [15]. In order to reduce within-individual animal bias and prevent the selection of within-sample enrichment of clonal isolates, only one isolate per resistance group (MDR or susceptible) was selected for each animal from each time point. Antibiotic-susceptible strains were randomly selected from unique animals, and MDR isolates that were resistant to the most classes of antibiotics were selected to evaluate highly drug-resistant isolates. Multidrug-resistant isolates were considered those that were resistant to at least three classes of antibiotics. Isolates were not selected from all animals due to feasibility. Veal calf management protocols were not made available. Sampling procedures were approved by the Pennsylvania State University institutional animal care and use committee (IACUC, protocol number 42381–1).

E. coli isolates were grown overnight at 37°C in Luria-Bertani broth (BD Diagnostics, Sparks, MD), and DNA was extracted using the QIAmp DNA Mini kit (Qiagen, Hilden, Germany). For shotgun genome sequencing, DNA libraries were constructed using the Nextera XT chemistry (Illumina, La Jolla, CA) and these were sequenced on the NextSeq 500 platform (2 x 151 bp paired-end reads) (Illumina). Sequencing reads were subsequently trimmed and curated for quality, length and contaminants using Trimmomatic (LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:36) [16] and DeconSeq to remove phiX reads (NCBI accession: NC_001422) [17]. Reads were assembled using SPAdes V. 3.14.1 with the–careful option to reduce the number of mismatches and indels [18]. Genome sequencing data and metadata have been deposited at NCBI under BioProject ID PRJNA664052.

Assembled genomes were evaluated for Sequence Type (ST) assignment [19], plasmid replicons [20], and antimicrobial resistance genes [21] under default settings using the Center for Genomic Epidemiology webserver (http://www.genomicepidemiology.org/). Phylogroups were determined using the ClermonTyping and EZClermont schemes [22, 23]. Virulence factors were identified using standalone BLASTN with a minimum 80% nucleotide sequence similarity across a minimum of 80% of the length of the reference VF gene selected from a database of known E. coli virulence genes [24]. Metal and biocide resistance genes (MRGs and BRGs) were similarly identified with BLASTN using Enterobacteriaceae reference nucleotide sequences from the BacMet database [25].

To visualize the differences in the virulomes (the set of virulence factors that are involved in virulence of a bacterium) of MDR and susceptible genomes, a nonmetric multidimensional scaling (NMDS) analysis using the Jaccard distance metric was inferred using the vegan package, followed by an analysis of similarities (ANOSIM) of the virulomes of these two groups using vegan in R [26]. The enrichment of VFs in MDR or susceptible isolates was evaluated using a two-tailed Fisher’s exact test (fisher.test command in R) followed by a correction for multiple comparisons [27] using the command p.adjust in the stats package (method = “fdr”) and the qvalue command (default commands) in the qvalue package in R. These corrected P-values are written as q-values. For this analysis, q < 0.05 (false discovery rate = 5%) was considered statistically significant. Analyses to determine the co-occurrence between VFs and ARG/MRG/BRGs, and plasmid replicons and ARG/MRG/BRGs were conducted with the cooccur command with the parameter thresh = TRUE to remove cooccurrences that are not expected to occur more than once, in the cooccur package in R [28]. The presence of ARGs and VFs in the isolates was visualized using ForceAtlas2 algorithm on an interactive network inference, Gephi version 0.9.1 (scaling 10, edge weight influence 1) [29, 30]. ForceAtlas2 is a force-directed algorithm used for network spatialization where nodes repulse each other like charged particles while edges attract their nodes [30]. These parameters were chosen for clarity of the nodes in the network. The edges (curves) in the network links a gene to the host isolate.

Core genome single nucleotide polymorphisms (SNPs) were identified by aligning the 85 E. coli genomes used in this study with 118 publicly available Escherichia genomes representing the eight major phylogenetic groups (A, B1, B2, C, D, E, F, and G), E. coli Cryptic Clades, and the near neighbors E. fergussoni and E. albertii using the Harvest package [31]. ParSNP within the Harvest package was run with the following parameters, -c (to force inclusion of all genomes in the analysis) and -x (to enable recombination filtering), and the complete chromosome of E. coli K-12 substr. MG1655 (NCBI accession: NC_000913.3) as the reference genome (-r). These SNPs were then used to infer a maximum likelihood tree with 1000 bootstrap replicates under default settings using the Randomized Axelerated Maximum Likelihood program (RAxML) [32]. To interrogate the relatedness of isolates collected from different farms, high quality SNPs (hqSNPs) were identified by aligning the cleaned and curated reads of presumptive related genomes to the E. coli K-12 MG1655 genome and retaining those SNPs that have a minimum of 10X sequencing read coverage by using the program Lyve-SET [33].

Results

Among the MDR isolates, phylogroups A, B1, D, C, F, G, E were identified 29, 9, 9, 8, 4, 4, and 2 times, respectively, with a single isolate identified as a member of Clade I (Fig 1; Table 1). None of the isolates were identified as phylogroup B2. In total, 33 STs were identified, and the most common were ST10 (7 isolates, 10.6% of isolates), ST69 (6, 9.1%), ST744 (5, 7.6%), and ST410 (5, 7.6%) (Table 1). There was a total of 12 ST complexes (ST Cplx), which include closely related STs differing by only a few alleles. The most frequently detected ST complexes were the ST10 Cplx (22 isolates, 33%), ST23 Cplx (9 isolates, 13.6%), ST69 Cplx (6 isolates, 9.1%), and ST648 Cplx (3 isolates, 4.5%). There were 16 isolates (24.2%) that were not assigned to any ST Cplx (Table 1).

Fig 1 — Brackets on right show phylogroups. The two inner brackets show the SNP differences between closely related strains. The bar on the bottom left shows substitutions per site.

Table 1. Characteristics of MDR and susceptible strains.

Columns show farm from which the isolate was collected, MLST, ST Cplx, antibiotic resistance, metal resistance, and biocide resistance genes.

Isolate	Farm	MLST	ST Cplx	Phylogroup	Antibiotic Resistance Genes (ARG)	Antibiotic Resistance-Conferring Point Mutations	Metal Resistance Genes (MRG)	Biocide Resistance Genes (BRG)	Plasmid Replicons
ARS-CC11286	K	10	10	A	aadA5, bla, blaTEM-1B, dfrA17, erm(B), floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B)	gyrA (p.S83L), gyrA (p.D87N)	merA	qacEΔ1, sugE	IncFII, IncFIB(pB171), IncX1
ARS-CC11297	D	10	10	A	aadA1, aadA2, aph(3’)-Ic, blaTEM-1C, cmlA1, dfrA12, floR, aph(3’’)-Ib, aph(6)-Id, sul2, sul3, tet(A), tet(M)			sugE	IncFII, IncN, IncX1, p0111, IncQ1, ColRNAI
ARS-CC11299	D	10	10	A	aadA5, blaCTX-M-15, blaTEM-1A, dfrA17, erm(B), floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B)	gyrA (p.S83L), gyrA (p.D87N)	silABC	qacEΔ1, sugE	IncFIB(pB171), IncFII, IncI1, IncFIB(K), ColRNAI
ARS-CC11321	A	10	10	A	aac(3)-VIa, aadA1, aadA5, aph(3’)-Ia, blaTEM-1B, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A)				IncFIB(K), IncFIB(pB171), IncX1, IncA/C2
ARS-CC11322	D	10	10	A	aadA1, aph(3’)-Ic, blaCMY-2, blaTEM-1B, dfrA1, floR, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(M)				IncN, IncFII(pCoo), IncY, IncA/C2
ARS-CC11333	K	10	10	A	aadA2, blaCMY-2, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)			qacEΔ1, sugE, sugE1	IncA/C2, ColpVC, ColRNAI
ARS-CC11351	M	10	10	A	blaCMY-2, dfrA17, floR, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(M)			qacEΔ1, sugE, sugE1	p0111, IncA/C2
ARS-CC11279	E	44	10	A	aadA5, blaCTX-M-15, dfrA17, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFII, IncI1, IncFIB(AP001918), IncFIA, Col(MGD2), Col(MG828), ColRNAI
ARS-CC11280	F	44	10	A	aadA5, blaCTX-M-15, dfrA17, mph(A), sul1, tet(B)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFIA, IncI1, IncFIB(AP001918), IncFII, Col(MGD2), Col(MG828), IncB/O/K/Z, ColRNAI
ARS-CC11281	E	44	10	A	aadA5, blaCTX-M-15, dfrA17, mph(A), sul1, tet(B)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFII(pRSB107), IncFII, IncI1, IncFIB(AP001918), IncFIA, Col(MGD2), ColRNAI, IncB/O/K/Z, Col(MG828)
ARS-CC11334	L	48	10	A	aadA1, aadA2, aph(3’)-Ia, blaCMY-2, blaTEM-1B, cmlA1, dfrA12, floR, lnu(F), sul2, sul3, tet(A)			sugE, sugE1	IncFIC(FII), IncI1, IncFIB(AP001918), IncX1, ColRNAI
ARS-CC11290	J	167	10	A	aac(6’)Ib-cr, aadA1, aadA5, blaCTX-M-15, blaOXA-1, catB3, dfrA17, lnu(F), mph(A), sul1, tet(A)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncFIA, ColRNAI
ARS-CC11287	G	617	10	A	aadA1, aadA2, aph(3’)-Ia, blaCTX-M-55, blaTEM-1B, dfrA12, floR, lnu(F), sul3, tet(A), tet(M)	gyrA (p.S83L), gyrA (p.D87N)	merA, silAB, pcoABCDRSE	sugE	IncFIB(AP001918), IncFII, IncFIC(FII), IncX1
ARS-CC9542	B	617	10	A	aac(3)-IIa, aac(6’)Ib-cr, aadA5, blaCTX-M-1, blaOXA-1, catB3, dfrA17, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncFIA, IncN, IncX1, ColRNAI, Col(MG828)
ARS-CC9579	B	617	10	A	aac(6’)Ib-cr, aadA1, aadA5, aph(3’)-Ic, blaCTX-M-15, blaOXA-1, catA1, catB3, dfrA17, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncFII, IncFIB(Mar), IncFIB(AP001918), IncFIA, IncHI1B, IncA/C2, ColRNAI
ARS-CC11288	G	744	10	A	aadA5, blaCTX-M-55, blaTEM-1B, catA1, dfrA17, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)	parC (p.A56T), gyrA (p.S83L), gyrA (p.D87N)	merA	qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncQ1
ARS-CC11317	J	744	10	A	aac(3)-VIa, aadA1, aadA2, aph(3’)-Ia, blaCMY-2, blaTEM-1B, catA1, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B), tet(M)	parC (p.A56T), gyrA (p.S83L), gyrA (p.D87N)			IncHI2, IncHI2A, IncFII, IncI1, TrfA, IncQ1, IncA/C2
ARS-CC11327	B	744	10	A	aadA5, blaCMY-2, blaTEM-1B, catA1, dfrA17, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)	parC (p.A56T), gyrA (p.S83L), gyrA (p.D87N)	merA	qacEΔ1, sugE, sugE1	IncI1, IncQ1
ARS-CC11328	B	744	10	A	aadA5, blaCTX-M-55, blaTEM-1B, catA1, dfrA17, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)	gyrA (p.S83L), parC (p.A56T)	merA	qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncQ1
ARS-CC11329	I	744	10	A	aadA1, aadA2, aph(3’)-Ia, blaCTX-M-15, blaTEM-1B, cmlA1, dfrA12, mef(B), mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, sul3, tet(B), tet(M)	gyrA (p.S83L), parC (p.A56T)	merA	qacEΔ1, sugE	IncFIC(FII), IncFII, IncFIB(AP001918), IncQ1
ARS-CC11311	H	993	10	A	aadA2, aph(3’)-Ia, blaTEM-1B, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B)				IncFIC(FII), IncFIB(AP001918), IncX1, ColRNAI, IncA/C2, Col(MG828)
ARS-CC11323	F	1721	10	A	aadA2, aph(3’)-Ia, blaTEM-1B, catA1, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)		merA, silABC,arsRDABC, pcoABCDRSE	qacEΔ1, sugE	IncFIA(HI1), IncFII, IncFIB(K), IncFII(Yp), IncX1, IncA/C2, ColRNAI
ARS-CC11302	F	1114	165	A	aadA2, aph(3’)-Ia, blaCMY-2, catA1, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)	gyrA (p.S83L), gyrA (p.D87N)			IncFII, p0111, ColRNAI, Col(MG828)
ARS-CC11289	I	2325	467	A	aadA1, aadA2, aph(3’)-Ia, blaCTX-M-27, blaTEM-1B, cmlA1, dfrA12, erm(B), floR, lnu(F), sul2, sul3, tet(A), tet(M)	gyrA (p.S83L), gyrA (p.D87N)		sugE	IncFIC(FII), IncI1, IncFIB(AP001918), IncX1, ColRNAI, Col(MG828)
ARS-CC11330	I	2325	467	A	aadA1, aadA2, aph(3’)-Ia, blaCTX-M-27, blaTEM-1B, cmlA1, dfrA12, erm(B), floR, lnu(F), sul2, sul3, tet(A), tet(M)	gyrA (p.S83L), gyrA (p.D87N)		sugE	IncFIC(FII), IncI1, IncFIB(AP001918), IncX1, ColRNAI, Col(MG828)
ARS-CC11316	J	540		A	aadA1, aph(3’)-Ic, blaTEM-1B, dfrA1, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)	gyrA (p.S83L)			IncFII, IncFIA, Col(MG828), ColRNAI, IncA/C2, IncX1, Col8282, IncQ1
ARS-CC11318	K	540		A	aadA5, aph(3’)-Ia, blaCMY-2, blaTEM-1B, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A)				p0111, IncA/C2
ARS-CC9586	B	1112		A	aph(3’)-Ia, blaCMY-2, blaTEM-1B, floR, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(B), tet(C), tet(M)			sugE, sugE1	IncFII(pSE11), IncI1, IncFIB(AP001918), IncA/C2
ARS-CC11320	M	1564		A	aadA5, aph(3’)-Ia, blaCMY-2, dfrA17, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)				IncFIB(K), p0111, IncA/C2
ARS-CC11331	J	345	23	B1	aadA12, aadA2, blaTEM-1B, dfrA23, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)		merA	qacEΔ1, sugE	IncFIB(AP001918), IncY, IncQ1
ARS-CC9632	C	58	155	B1	aph(3’)-Ic, aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)			sugE	IncFII(pRSB107), IncFIA, IncFIB(AP001918), IncFII(pCoo), IncX1, IncI2, IncX4, ColRNAI
ARS-CC11285	I	448	448	B1	aph(3’)-Ia, blaCTX-M-27, blaTEM-1B, dfrA7, rmtE, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(M)	gyrA (p.S83L), gyrA (p.D87N)		qacEΔ1, sugE	IncHI2, IncFII(pCoo), IncHI2A, IncFII, IncFIB(AP001918), IncFIA, TrfA, ColRNAI, Col(MG828), Col156, IncQ1, IncB/O/K/Z
ARS-CC11326	B	448	448	B1	blaCMY-2, dfrA8, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(M)			sugE	IncFII, IncI1, IncQ1
ARS-CC9596	C	947	469	B1	aac(3)-IId, aadA5, aph(3’)-Ic, blaCMY-2, blaTEM-1B, dfrA17, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)			qacEΔ1, sugE, sugE1	IncFIA, IncFIB(AP001918), IncFII(pCoo),
ARS-CC11319	K	224		B1	aadA1, aadA2, blaCMY-2, blaTEM-1B, cmlA1, dfrA12, floR, lnu(F), mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, sul3, tet(A), tet(B), tet(M)	gyrA (p.S83L), gyrA (p.D87N)			IncI1, IncX1, IncY, IncA/C2
ARS-CC11298	D	2436		B1	aac(3)-VIa, aadA1, aadA2, aph(3’)-Ia, blaTEM-1B, dfrA12, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)		merA	qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncQ1, ColRNAI
ARS-CC11307	G	2522		B1	aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)
ARS-CC11332	K	6345		B1	aadA2, blaCMY-2, dfrA12, floR, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A)			qacEΔ1, sugE, sugE1	IncFII
ARS-CC11300	E	88	23	C	aadA2, aph(3’)-Ia, blaCMY-2, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(M)	ampC-promoter (g.-42C>T)			IncA/C2
ARS-CC9615	B	88	23	C	aadA1, blaTEM-1B, dfrA1, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A)		merA	qacEΔ1, sugE	IncFII, IncFIB(AP001918), Col(MGD2), IncQ1, Col156, IncB/O/K/Z, ColRNAI
ARS-CC9650	C	88	23	C	aadA1, aadA2, aph(3’)-Ic, blaCMY-2, blaTEM-1B, dfrA1, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A), tet(B), tet(M)	ampC-promoter (g.-42C>T)		qacEΔ1, sugE, sugE1	IncFIA, IncFIB(AP001918), IncFII(pCoo), IncA/C2
ARS-CC11275	D	410	23	C	aac(3)-IIa, aadA5, blaCTX-M-15, dfrA17, mph(A), sul1, tet(B)	gyrA (p.S83L), gyrA (p.D87N)	silABC, pcoABCDRSE	qacEΔ1, sugE	IncFIA, IncFIB(AP001918), IncFII, IncQ1
ARS-CC11276	D	410	23	C	aac(3)-IIa, aadA5, blaCTX-M-15, dfrA17, mph(A), sul1, tet(B)	gyrA (p.S83L), gyrA (p.D87N)	silABC, pcoABCDRSE	qacEΔ1, sugE	IncFII(pRSB107), IncFII, IncFIB(AP001918), IncFIA, IncQ1
ARS-CC11278	E	410	23	C	aadA1, aadA5, aadB, blaCTX-M-15, blaTEM-1C, cmlA1, dfrA17, mph(A), sul1, tet(A)	gyrA (p.S83L), gyrA (p.D87N)	silABC, pcoABCDRSE	qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncFIA, IncX1, ColRNAI
ARS-CC11291	H	410	23	C	aadA1, aadA5, aadB, blaCTX-M-15, blaTEM-1C, cmlA1, dfrA17, mph(A), sul1, tet(A)	gyrA (p.S83L), gyrA (p.D87N)	silABC, pcoABCDRSE	qacEΔ1, sugE	IncFIA, IncFIB(AP001918), IncFII,
ARS-CC11313	H	410	23	C	aadA1, aadA5, aadB, blaCTX-M-15, blaTEM-1C, cmlA1, dfrA17, mph(A), sul1, tet(A)	gyrA (p.S83L), gyrA (p.D87N)			IncFII, IncFIB(AP001918), IncFIA,
ARS-CC9646	B	925	31	D	aadA1, aph(3’)-Ic, blaCMY-2, blaTEM-1D, dfrA1, sul1, tet(A)		merA	qacEΔ1, sugE, sugE1	IncI1, p0111, ColRNAI
ARS-CC9581	B	69	69	D	blaCTX-M-15, blaTEM-1B, qnrS1, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A)			sugE	IncFIB(AP001918), IncFIA, IncI1, IncFIC(FII), IncY, Col156
ARS-CC9594	C	69	69	D	aph(3’)-Ic, blaCMY-2, blaCTX-M-27, blaTEM-1B, aph(3’’)-Ib, aph(6)-Id, tet(B)			sugE, sugE1	IncFIA, IncI1, IncFIB(AP001918), IncFII, IncFII(pCoo),
ARS-CC9607	C	69	69	D	aadA1, aph(3’)-Ic, blaCMY-2, blaTEM-1B, catA1, dfrA1, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)		merA	qacEΔ1, sugE, sugE1	IncFIA, IncFIB(AP001918), IncFII(pHN7A8), IncFII(pCoo), p0111
ARS-CC9610	B	69	69	D	aph(3’)-Ic, blaTEM-1B, aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)			sugE	IncFIA, IncI1, IncFIB(AP001918), IncFII(pCoo), IncY, ColRNAI, Col(MG828)
ARS-CC9628	C	69	69	D	aadA1, aph(3’)-Ic, dfrA1, floR, aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)			sugE	IncFIA, IncFIB(AP001918), IncFII(pCoo), ColRNAI
ARS-CC9653	C	69	69	D	aph(3’)-Ic, blaCMY-2, aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)			sugE	IncFIA, IncFIB(AP001918), IncFII(pCoo), IncY, IncB/O/K/Z, ColRNAI
ARS-CC9601	C	349	349	D	aac(3)-IId, aadA1, aadA5, aph(3’)-Ic, blaCMY-2, blaCTX-M-27, blaOXA-1, blaTEM-1B, catA1, dfrA17, aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)			qacEΔ1, sugE, sugE1	IncFIA, IncI1, IncFIB(AP001918), IncFII, IncFII(pCoo),
ARS-CC9625	B	362		D	aph(3’)-Ia, blaCTX-M-15, blaTEM-1B, catA1, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A)		merA	sugE	IncQ1
ARS-CC9590	C	57	350	E	aac(3)-VIa, aadA24, aph(3’)-Ia, blaCMY-2, dfrA1, floR, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(B)	ampC-promoter (g.-32T>A), gyrA (p.S83L), gyrA (p.D87N)	merA	qacEΔ1, sugE, sugE1	IncFIA, IncFIB(AP001918), IncFII(pHN7A8), IncFII(pCoo), IncI2, ColRNAI, Col(MG828), IncA/C2, p0111, IncB/O/K/Z
ARS-CC9616	B	219		E	aph(3’)-Ic, blaCMY-2, blaTEM-1B, aph(3’’)-Ib, aph(6)-Id, tet(B)			sugE, sugE1	IncFIA, IncFIB(AP001918), IncFII, IncFII(pCoo), IncB/O/K/Z
ARS-CC9543	K	648	648	F	aadA2, blaCTX-M-124, blaTEM-1B, catA1, dfrA12, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(B)			qacEΔ1, sugE	IncQ1
ARS-CC9544	M	648	648	F	aadA2, aph(3’)-Ia, blaCTX-M-124, blaTEM-1B, catA1, dfrA12, floR, mph(A), aph(3’’)-Ib, aph(6)-Id, sul1, sul2, tet(A)		merA	qacEΔ1, sugE	IncFII, IncFIB(AP001918), IncFIA, IncX1, IncQ1
ARS-CC9589	B	648	648	F	aph(3’)-Ic, blaTEM-1B, aph(3’’)-Ib, aph(6)-Id, sul2, tet(B)	ampC-promoter (g.-42C>T)		sugE	IncFII(pRSB107), IncFIA, IncFIB(AP001918), IncFII(pCoo),
ARS-CC11293	K	457		F	aadA22, aph(3’)-Ia, blaCMY-2, blaCTX-M-55, blaTEM-1B, catA2, dfrA14, floR, lnu(F), aph(3’’)-Ib, aph(6)-Id, sul2, sul3, tet(A)	gyrA (p.S83L), gyrA (p.D87Y)		sugE, sugE1	IncFIC(FII), IncI1, IncFIB(AP001918), IncY
ARS-CC11348	I	117		G	aadA1, blaCMY-2, sul1		merA	sugE, sugE1	IncFII(pRSB107), IncFII, IncI1, IncFIB(AP001918), ColRNAI, Col156, Col(MG828)
ARS-CC9597	C	117		G	aadA1, aph(3’)-Ic, blaCMY-2, sul1, tet(A)		merA	qacEΔ1, sugE, sugE1	IncFII(29), IncI1, IncFIB(AP001918), ColRNAI, Col156, Col(MG828)
ARS-CC9602	C	117		G	aac(3)-VIa, aadA1, aph(3’)-Ic, blaCMY-2, blaTEM-1C, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(B)			sugE, sugE1	IncHI2, IncHI2A, IncFIC(FII), IncI1, IncFIB(AP001918), p0111, ColRNAI, Col156, Col(MG828)
ARS-CC9635	C	117		G	aadA5, blaTEM-1B, dfrA17, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A)			sugE	IncFII, IncI1, ColRNAI, Col(MG828)
ARS-CC11349	M	3042		Clade I	blaCMY-2, dfrA8, aph(3’’)-Ib, aph(6)-Id, sul2, tet(A), tet(M)	parE:p.I355T		sugE	IncFII, IncQ1
ARS-CC11304	G	10	10	A
ARS-CC11308	G	10	10	A					IncX4, ColRNAI
ARS-CC11342	G	10	10	A			silABC, pcoABCDRSE	sugE
ARS-CC11343	G	10	10	A			silABC, pcoABCDRSE	sugE
ARS-CC11324	F	730		A				sugE	IncFIB(AP001918), IncFII(pSE11), IncFIC(FII), ColRNAI
ARS-CC11294	B	1718		A				sugE
ARS-CC11295	B	21	29	B1				sugE	IncFIB(AP001918), IncY, IncB/O/K/Z
ARS-CC11306	G	21	29	B1					IncFIB(AP001918), p0111, ColRNAI, IncB/O/K/Z
ARS-CC9609	B	29	29	B1				sugE	IncFII, IncFIB(AP001918),
ARS-CC11325	F	765	29	B1				sugE	IncFII(pSE11),
ARS-CC11296	B	162	469	B1				sugE	IncFIA(HI1), IncFIB(pB171), IncFII(pCoo), IncI2
ARS-CC9621	B	173		B1				sugE	IncFIB(AP001918), ColRNAI, Col156, IncB/O/K/Z
ARS-CC11305	G	300		B1					IncFIB(AP001918),
ARS-CC9636	C	300		B1				sugE	IncFIB(AP001918), ColRNAI
ARS-CC9631	C	937		B1				sugE	IncFII, IncFIB(AP001918), IncY, Col156, ColRNAI
ARS-CC9613	B	4038		B1				sugE	IncFIA, IncFIB(AP001918), IncFII(29), IncX1
ARS-CC11303	E	2570		D
ARS-CC11314	I	4197		E
ARS-CC9623	B	657		G				sugE	IncFIB(AP001918), IncB/O/K/Z, ColRNAI

Open in a new tab

In total, there were 673 antimicrobial resistance genes (ARGs) detected, including 52 unique ARGs, among the 66 MDR isolates (Table 1). These ARGs conferred resistance to aminoglycosides (227 genes, 33% of detected ARGs), ß-lactams (102, 15%), sulfonamides (102, 15%), tetracyclines (97, 14%), phenicols (55, 8%), trimethoprim (51, 7%), macrolides (31, 4%), lincosamide (7, 1%), and quinolones (1, 0.1%). ARGs conferring resistance to colistin, fosfomycin, fusidic acid, glycopeptides, nitroimidazole, oxazolidinone, and rifampicin were not detected in any of the genomes. Among the isolates, the five most frequently detected ARGs were sul2 (sulfonamide resistant dihydropteroate synthase), aph(3’’)-Ib (aminoglycoside phosphotransferase), aph(6)-Id (aminoglycoside phosphotransferase), sul1 (sulfonamide resistant dihydropteroate synthase), and tet(A) (tetracycline efflux pump), and they were detected in 78%, 77%, 77%, 63%, and 63% of MDR isolates, respectively. Sulfonamide-resistant dihydropteroate synthases sul2 and sul1 represented 51% and 41% of the three sulfonamide ARGs detected. Aminoglycoside phosphotransferases aph(3’’)-Ib and aph(6)-Id each made up 23% of the 15 aminoglycoside ARG types detected. ß-lactamases bla_TEM-1B, bla_CMY-2, and bla_CTX-M-15 represented 35%, 27%, and 15% of the 11 detected ß-lactamases. Tetracycline efflux pumps tet(A) and tet(B) comprised 43% and 33% of the four detected tetracycline ARGs. Macrolide 2’-phosphotransferase I mph(A) represented 84% of the detected macrolide ARGs. The genes aac(6’)Ib-cr and qnrS1 were the only detected fluoroquinolone/quinolone ARGs. Multiple genes conferring resistance to antibiotics of human health significance were detected, including ß-lactamases bla_CMY-2 and bla_CTX-M (variants 1, 15, 27, 55, 124), macrolide resistance genes mph(A) and erm(B), fluoroquinolone/aminoglycoside resistance gene aac(6’)Ib-cr, and quinolone resistance gene qnrS1.

Metal and biocide resistance genes were detected in many isolates (Table 1). Among the MDR genomes, 25% encoded merA which confers resistance to mercury, 10% encoded silABC silver transport system, 9% encoded the pcoABCDRSE copper detoxification system, and one isolate encoded arsRDABC conferring resistance to arsenite. BRGs were also identified in a higher percentage of MDR isolates than were MRGs. In total, 50% of the MDR genomes encoded quaternary ammonium (QAC) disinfectant resistance gene qacEΔ1, while 80% encoded sugE and 27% encoded sugE1. Biocide resistance genes oqxA or oqxB were not detected in any of the genomes.

Statistically significant co-occurrence of resistance genes was detected as 160 positive co-occurrences (Fig 2). Several ARGs had positive co-occurrence with multiple resistance genes; mph(A), sul1, dfrA17, aadA5, and bla_CTX-M-15 frequently co-occurred. Silver (sil) and copper resistance genes (pco) had a positive cooccurrence with mph(A), dfrA17, aadA5, and bla_CTX-M-15, but negative cooccurrences with sul2, aph(3’’)-Ib, and aph(6)-Id. BRG qacEΔ1 had a positive cooccurrence with mph(A), sul1, dfrA17, aadA5, bla_CTX-M-15, pcoABCDRSE, merA, and sugE1.

VFs were detected in every isolate, although the number of VFs per genome was highly variable and ranged between 1 and 48 (median = 10, mean = 15.5) (Fig 3). None of the MDR genomes encoded intimin (eae), translocated intimin receptor (tir), heat labile toxin (eltAB), heat stable toxin (estlA), the bundle forming pilus (bfp), or Shiga-toxins (stx). A single MDR isolate encoded a sequence that aligned with approximately 30% of stx2A at 100% similarity but was not identified as stx2A-positive by the VirulenceFinder tool. In total, very few VFs associated with enteroaggregative E. coli (EAEC), diffusely adherent E. coli (DAEC), enterohemorrhagic E. coli (EHEC), enteroinvasive E. coli (EIEC), enteropathogenic E. coli (EPEC), or enterotoxigenic E. coli (ETEC) were identified among the MDR isolates (Fig 3). However, VFs such as the EAEC heat-stable enterotoxin 1 (EAST1/astA), Shigella enterotoxin 1 (ShET1 or setAB), and cdt (cytolethal distending toxin) were detected in some isolates (Fig 3). There were multiple genomes encoding virulence factors known to be involved in extra-intestinal pathogenic E. coli (ExPEC) infections. These include pap (P fimbriae), iha (iron‐regulated‐gene‐homologue adhesion), iuc-iutA (aerobactin synthase and receptor), irp (iron repressible protein), fyuA (yersiniabactin receptor), iroN (salmochelin), chu (heme binding protein), sit (iron transport), kps (K1 capsule), omp (outer membrane protein), iss (increased serum survival), pic (serine protease autotransporter), sat (secreted autotransporter toxin), and vat (vacuolating autotransporter toxin) (Fig 3).

Among the MDR isolates, 35 unique plasmid replicons were detected (Table 1). IncFII and IncFIB (both often carried on the same plasmid) were the two most frequently detected replicons, followed by ColRNAI, IncFIA, IncI1, IncQ1, Col(MG828), and IncA/C2. Since the sequencing chemistry used in this study could not result in the assembly of completely closed plasmids, a co-occurrence analysis between resistance genes, VFs, and plasmid replicons was conducted to identify which plasmids may be potential carriers of certain ARGs, MRGs, BRGs and VFs (Table 2). The IncFIB replicon was positively associated with the IncFII, IncFIA, and IncB/O/K/Z replicons, as well as four ARGs, the sit system (sitABCD), and the aerobactin (iucABC-iutA) operon. The IncFII replicon was similarly positively associated with the sit system (sitABCD), and aerobactin (iucABC-iutA) operon, as well as seven ARGs. IncFIA replicons were associated with IncFIB and IncFII replicons as well as the sit system, aerobactin operon, 12 ARGs, and the copper resistance operon. IncA/C2 replicons were associated with 11 ARGs. BRG qacEΔ1 was associated with IncFIA, IncFII, and IncFIC replicons, while sugE1 was associated with IncI1, IncQ, and IncX1 replicons.

Table 2. ARGs, BRGs, MRGs, and VFs that co-occur with plasmid replicons identified in the study isolates.

Plasmid Replicon	ARG/BRG/MRG/VF ^A	ARG/BRG/MRG/VF Incidence	Observed Co-occurences	Probability of Co-occurrence	Expected Co-occurences	P-value ^B
IncA/C2	floR	30	16	0.066	5.6	< 0.0001
IncA/C2	tet(M)	22	11	0.049	4.1	< 0.0001
IncA/C2	dfrA1	8	4	0.018	1.5	< 0.05
IncA/C2	aph(3’)-Ia	20	9	0.044	3.8	< 0.01
IncA/C2	bla _CMY2	28	11	0.062	5.3	< 0.01
IncA/C2	tet(A)	42	16	0.093	7.9	< 0.0001
IncA/C2	aadA2	19	7	0.042	3.6	< 0.05
IncA/C2	aph(3")-Ib	51	16	0.113	9.6	< 0.0001
IncA/C2	aph(6)-Id	51	16	0.113	9.6	< 0.0001
IncA/C2	sul2	52	16	0.115	9.8	< 0.001
IncA/C2	sul1	42	12	0.093	7.9	< 0.05
IncFIA	bla _OXA-1	4	4	0.017	1.4	< 0.05
IncFIA	IncI2	3	3	0.012	1.1	< 0.05
IncFIA	aac(3)-IIa	3	3	0.012	1.1	< 0.05
IncFIA	aac(6’)-Ib-cr	3	3	0.012	1.1	< 0.05
IncFIA	aadB	3	3	0.012	1.1	< 0.05
IncFIA	catB3	3	3	0.012	1.1	< 0.05
IncFIA	iutA	30	22	0.125	10.6	< 0.0001
IncFIA	iucC	30	22	0.125	10.6	< 0.0001
IncFIA	iucA	30	22	0.125	10.6	< 0.0001
IncFIA	bla _CTX-M-15	15	11	0.062	5.3	< 0.005
IncFIA	sitD	33	24	0.137	11.6	< 0.0001
IncFIA	sitC	33	24	0.137	11.6	< 0.0001
IncFIA	sitB	33	24	0.137	11.6	< 0.0001
IncFIA	sitA	33	24	0.137	11.6	< 0.0001
IncFIA	iucB	29	21	0.12	10.2	< 0.0001
IncFIA	aph(3")-Ic	18	13	0.075	6.4	< 0.01
IncFIA	pcoE	7	5	0.029	2.5	< 0.05
IncFIA	pcoA	7	5	0.029	2.5	< 0.05
IncFIA	pcoB	7	5	0.029	2.5	< 0.05
IncFIA	pcoC	7	5	0.029	2.5	< 0.05
IncFIA	pcoD	7	5	0.029	2.5	< 0.05
IncFIA	pcoR	7	5	0.029	2.5	< 0.05
IncFIA	pcoS	7	5	0.029	2.5	< 0.05
IncFIA	mcmA	16	10	0.066	5.6	< 0.05
IncFIA	eilA	16	10	0.066	5.6	< 0.05
IncFIA	dfrA17	21	13	0.087	7.4	< 0.01
IncFIA	tet(B)	32	19	0.133	11.3	< 0.01
IncFIA	aadA5	22	13	0.091	7.8	< 0.01
IncFIA	IncFII	53	29	0.22	18.7	< 0.0001
IncFIA	qacEΔ1	33	18	0.137	11.6	< 0.01
IncFIA	fyuA	23	12	0.096	8.1	< 0.05
IncFIA	IncFIB	59	29	0.245	20.8	< 0.0001
IncFIA	sul1	42	19	0.174	14.8	< 0.05
IncFIA	sugE	67	28	0.278	23.6	< 0.05
IncFIB	IncB/O/K/Z	11	11	0.09	7.6	< 0.05
IncFIB	IncFIC(FII)	10	10	0.082	6.9	< 0.05
IncFIB	Col156	9	9	0.073	6.2	< 0.05
IncFIB	sitD	33	32	0.269	22.9	< 0.0001
IncFIB	sitC	33	32	0.269	22.9	< 0.0001
IncFIB	sitB	33	32	0.269	22.9	< 0.0001
IncFIB	sitA	33	32	0.269	22.9	< 0.0001
IncFIB	IncFIA	30	29	0.245	20.8	< 0.0001
IncFIB	iutA	30	29	0.245	20.8	< 0.0001
IncFIB	iucC	30	29	0.245	20.8	< 0.0001
IncFIB	iucA	30	29	0.245	20.8	< 0.0001
IncFIB	iucB	29	28	0.237	20.1	< 0.0001
IncFIB	bla _CTX-M-15	15	14	0.122	10.4	< 0.05
IncFIB	espP	21	19	0.171	14.6	< 0.05
IncFIB	aadA5	22	19	0.18	15.3	< 0.05
IncFIB	tet(B)	32	27	0.261	22.2	< 0.05
IncFIB	qacEΔ1	33	27	0.269	22.9	< 0.05
IncFIB	ColRNAI	38	31	0.31	26.4	< 0.05
IncFIB	IncFII	53	43	0.433	36.8	< 0.01
IncFIB	sugE	67	52	0.547	46.5	< 0.01
IncFIC(FII)	cmlA1	9	4	0.012	1.1	< 0.01
IncFIC(FII)	dfrA12	17	6	0.024	2	< 0.01
IncFIC(FII)	aph(3")-Ia	20	7	0.028	2.4	< 0.01
IncFIC(FII)	aadA2	19	6	0.026	2.2	< 0.01
IncFIC(FII)	IncX1	16	5	0.022	1.9	< 0.05
IncFIC(FII)	IncI1	22	6	0.03	2.6	< 0.05
IncFIC(FII)	aadA1	26	6	0.036	3.1	< 0.05
IncFIC(FII)	bla _TEM-1B	36	8	0.05	4.2	< 0.05
IncFIC(FII)	tet(A)	42	8	0.058	4.9	< 0.05
IncFIC(FII)	aslA	51	9	0.071	6	< 0.05
IncFIC(FII)	IncFIB	59	10	0.082	6.9	< 0.05
IncFII	IncFIA	30	29	0.22	18.7	< 0.0001
IncFII	traJ	15	14	0.11	9.4	< 0.01
IncFII	aph(3")-Ic	18	16	0.132	11.2	< 0.01
IncFII	sitD	33	29	0.242	20.6	< 0.0001
IncFII	sitC	33	29	0.242	20.6	< 0.0001
IncFII	sitB	33	29	0.242	20.6	< 0.0001
IncFII	sitA	33	29	0.242	20.6	< 0.0001
IncFII	iutA	30	26	0.22	18.7	< 0.001
IncFII	iucC	30	26	0.22	18.7	< 0.001
IncFII	iucA	30	26	0.22	18.7	< 0.001
IncFII	bla _CTX-M-15	15	13	0.11	9.4	< 0.05
IncFII	iucB	29	25	0.213	18.1	< 0.001
IncFII	dfrA17	21	18	0.154	13.1	< 0.01
IncFII	qacEΔ1	33	27	0.242	20.6	< 0.01
IncFII	aadA5	22	18	0.161	13.7	< 0.05
IncFII	tet(B)	32	26	0.235	20	< 0.01
IncFII	sul1	42	31	0.308	26.2	< 0.05
IncFII	IncFIB	59	43	0.433	36.8	< 0.01
IncFII	sugE	67	47	0.491	41.8	< 0.01
IncI1	set1B	4	4	0.012	1	< 0.01
IncI1	set1A	4	4	0.012	1	< 0.01
IncI1	pic	4	4	0.012	1	< 0.01
IncI1	vat	5	4	0.015	1.3	< 0.05
IncI1	tsh	5	4	0.015	1.3	< 0.05
IncI1	bla _CTX-M-27	5	4	0.015	1.3	< 0.05
IncI1	lnuF	7	5	0.021	1.8	< 0.05
IncI1	ireA	10	7	0.03	2.6	< 0.01
IncI1	sul3	8	5	0.024	2.1	< 0.05
IncI1	IncFIC(FII)	10	6	0.03	2.6	< 0.05
IncI1	ColMG828	17	10	0.052	4.4	< 0.01
IncI1	sugE1	18	10	0.055	4.7	< 0.01
IncI1	sfaC	16	8	0.049	4.1	< 0.05
IncI1	papK	16	8	0.049	4.1	< 0.05
IncI1	papJ	16	8	0.049	4.1	< 0.05
IncI1	papI	16	8	0.049	4.1	< 0.05
IncI1	papH	16	8	0.049	4.1	< 0.05
IncI1	papF	16	8	0.049	4.1	< 0.05
IncI1	papD	16	8	0.049	4.1	< 0.05
IncI1	papC	16	8	0.049	4.1	< 0.05
IncI1	papB	16	8	0.049	4.1	< 0.05
IncI1	sfaX	14	7	0.043	3.6	< 0.05
IncI1	bla _CMY-2	28	13	0.085	7.2	< 0.01
IncI1	chuY	23	10	0.07	6	< 0.05
IncI1	chuX	23	10	0.07	6	< 0.05
IncI1	chuW	23	10	0.07	6	< 0.05
IncI1	chuV	23	10	0.07	6	< 0.05
IncI1	chuU	23	10	0.07	6	< 0.05
IncI1	chuT	23	10	0.07	6	< 0.05
IncI1	chuS	23	10	0.07	6	< 0.05
IncI1	chuA	23	10	0.07	6	< 0.05
IncI1	shUY	23	10	0.07	6	< 0.05
IncI1	shUX	23	10	0.07	6	< 0.05
IncI1	shUV	23	10	0.07	6	< 0.05
IncI1	shUT	23	10	0.07	6	< 0.05
IncI1	shUS	23	10	0.07	6	< 0.05
IncI1	shUA	23	10	0.07	6	< 0.05
IncI1	aslA	51	17	0.155	13.2	< 0.05
IncQ1	mer(A)	17	10	0.042	3.6	< 0.001
IncQ1	catA1	12	7	0.03	2.5	< 0.05
IncQ1	tet(M)	22	9	0.055	4.7	< 0.05
IncQ1	qacEΔ1	33	12	0.082	7	< 0.01
IncQ1	bla _TEM-1B	36	13	0.09	7.6	< 0.01
IncQ1	mph(A)	26	9	0.065	5.5	< 0.05
IncQ1	sul1	42	14	0.105	8.9	< 0.01
IncQ1	aph(3")-Ib	51	16	0.127	10.8	< 0.01
IncQ1	aph(6)-Id	51	16	0.127	10.8	< 0.01
IncQ1	sul2	52	16	0.13	11	< 0.01

Open in a new tab

^A: ARG = antibiotic resistance gene, BRG = biocide resistance gene, MRG = metal resistance gene, VF = virulence factor

^B: Observed co-occurrences greater than what is expected by chance

Although the focus of this study was on highly drug-resistant E. coli isolates, we randomly selected a smaller subset of susceptible isolates as comparators to better evaluate the characteristics of the MDR isolates. Similar to the MDR isolates, the most common ST among the susceptible isolates was ST10. However, the most common phylogroup among these isolates was B1 (Table 1). The most frequently detected plasmid replicons were IncFIB, ColRNAI, and IncFII, which were detected in 11, 7, and 6 genomes, respectively. IncI1, IncQ1, and IncA/C2 replicons were not detected among the susceptible isolate genomes. EHEC and EPEC-associated VFs absent in MDR genomes were identified in susceptible isolates. These include efa-1/lifA (EHEC factor for adherence/lymphostatin), eae, paa (porcine attaching-effacing associated protein), ler (LEE encoded regulator), hlyABCD (hemolysin), stx, cif (cycle-inhibiting factor), nleACD (non-LEE-encoded effectors), and plasmid-encoded regulator (per) (Fig 3).

Based on a nonmetric multidimensional scaling (NMDS) analysis, the virulomes of MDR isolates were somewhat different than those of susceptible isolates (ANOSIM R = 0.1698, P = 0.001) (Fig 4). Since some VFs are integral to within-host survival, we assessed if there was an association between VFs, which may confer a selective advantage in the host gut, and the MDR phenotype. We analyzed the relative abundances of these accessory genes and compared these abundances in the MDR genomes to the susceptible genomes. In total, there were 40 VFs that were differentially abundant between the two groups (Fisher’s exact test, two-tailed, q < 0.05). Of these, 22 were more abundant in susceptible genomes and 18 were more abundant in MDR genomes (Fig 3). The VFs more abundant in susceptible isolates included stx1AB, stx2AB, ler, tir, eae, cif, paa, per, hlyABCE, ehxA and nleABCD (q < 0.05). The VFs more abundant in MDR genomes were iron acquisition genes sitABCD and iucABC-iutA (aerobactin), and pap P fimbriae (q < 0.05).

The presence of all resistance genes and VFs in all strains (MDR and susceptible) were visualized in a network interface (Fig 5). In this study, most of the susceptible isolates were grouped into a separate cluster from the MDR isolates in the network structure indicating variation in the resistance genes and VFs repertoires in susceptible versus MDR isolates, which is congruent with the results of the NMDS analysis (Fig 5).

Several clonal strains with high levels of genomic similarity, based on the core genome SNPs and ARGs, were isolated from different veal operations (Fig 1). Isolates from farms E and H (ARS-CC11278 and ARS-CC11291) collected 122 days apart differed by 20 SNPs and two isolates from farms B and G (ARS-CC11328 and ARS-CC11288) collected over 7 days differed by 17 SNPs.

Discussion

Antimicrobial resistance has been well-documented in dairy and beef cattle and recent studies have demonstrated that younger calves harbor a greater abundance of resistant bacteria than older animals [10–14]. However, resistance in veal calves, which are considered a separate production class from dairy and beef calves during the Food and Drug Administration drug approval process, remains under-studied. Dairy calves raised as replacements for lactating cows and veal calves are managed differently and fed different diets. Replacement dairy calves are initially fed a diet of milk or milk replacer, followed by gradual introduction of hay and a solid calf starter. Once weaned, typically at 8–9 weeks of age, they are fed an exclusively solid feed. This phased transition to solid food assists in the development of a functional rumen. The diets of veal calves, on the other hand, typically include milk or milk replacer (made from whey and whey protein) until marketed at 16–18 weeks. Bob veal calves are fed either colostrum, waste milk, and/or milk replacer for approximately three weeks when they are sold. Although several studies have evaluated the prevalence of antimicrobial resistance on veal farms [34], none to-date have evaluated the non-ARG genetic features that co-occur with ARGs and may be associated with persistence or selection of resistance in the calf gut. Further, the public health risk posed by these bacteria remains unknown. Here we analyzed 66 MDR isolates from non-redundant veal calf fecal samples and compared these to a smaller subset of susceptible isolates with the aim of further understanding the diversity of MDR strains shed by these animals, as well as the genomic features that may be responsible for their persistence in young calves.

High genotypic diversity with an observed predominant phylogroup and genotype

Results of this analysis demonstrate that there is a high level of diversity among MDR E. coli isolated from veal calf feces, but the group was dominated by phylogroup A-ST Cplx 10 strains (33% of all isolates). These data suggest that the MDR and susceptible strains from veal calf feces, in general, are associated with different lineages of E. coli. It appears that MDR E. coli shed in veal calf feces are more likely to be phylogroup A, while susceptible isolates are more likely to be B1. Currently it is unknown if certain phylogroups are more likely than others to acquire transferrable resistance, but previous studies have described this phenomenon, albeit some identified similar trends as observed in this study (group A strains having a high level of resistance), while others showed that different groups are more likely to be resistant than phylogroup A [35–40]. These studies characterized E. coli from a variety of non-bovine matrices and isolates from studies in which E. coli was recovered from feces were similar in phylogroup distribution to those presented here [41, 42]. Studies focused on bovine feces indicate that randomly selected generic E. coli are predominantly group B1 [43–46], while extended spectrum β-lactamase (ESBL)-producing E. coli were more likely to be phylogroup A [47], the latter being consistent with our results.

ExPEC-associated sequence types repeatedly identified among MDR isolates

The predominant ST among the MDR and susceptible isolates was A-ST10, which is a “globally distributed” ST that is commonly isolated from a wide diversity of hosts, environments, and regions [48]. It is therefore not surprising that A-ST10 is common among E. coli from veal calf feces. More than half of the MDR isolates were identified as STs that are frequently isolated from human infections, including gastrointestinal and extra-intestinal infections. The pandemic ST131, which is the current leading cause of ExPEC infections globally, was not detected among any of the isolates, but ST69, ST410, ST117, ST88, ST617, ST648, ST10, ST58, and ST167, which are among the leading causes of non-ST131 ExPEC infections globally were all identified repeatedly, except for ST58 and ST167, which were identified once each [49–52]. A significant number of the isolates encoded VFs involved in ExPEC infections, such as fyuA (yersiniabactin), sit operon (Sit system), iucABC-iutA (aerobactin), chuA (heme binding protein), and pap operon (P fimbriae), which were particularly abundant in ST69, ST117, ST410, and ST648 genomes. Of particular interest, of the 33 isolates identified as ExPEC-associated STs, 27 were bla_CTX-M-encoding strains, and 26 encoded the azithromycin resistance gene mph(A), indicating that these potential ExPEC strains encoded resistance to antibiotics of human clinical significance.

Based on these data, there is an appreciable prevalence of ExPEC-associated STs among the MDR fecal E. coli isolated from veal calves. Previous studies have identified poultry as a significant reservoir of ExPEC isolates causing human bladder infections [50, 53–55], and results of this study indicate that veal calves may harbor similar strains. However, this study only takes into account the STs and VFs of these isolates and does not definitively identify them as pathogens. Further, more research needs to be conducted to evaluate the abundance of potential ExPEC strains in relation to the total E. coli population in the veal calf gut.

ARGs, MRGs, BRGs, and their co-occurrence

The most frequently observed antimicrobial class to which ARGs were identified were aminoglycosides, β-lactams, sulfonamides, and tetracyclines, in decreasing order of frequency. Antimicrobial usage data on these operations were not available and national data on antimicrobial usage on veal operations is lacking, unlike dairy and beef calves for which these data have been periodically tabulated. Veal calves are considered a separate production class from dairy and beef steer calves during the FDA drug approval process, so antimicrobial usage in these animals cannot be accurately extrapolated to usage in veal calves. Currently, aminoglycosides (streptomycin), β-lactams (ampicillin and amoxicillin), sulfonamides (sulfabromomethazine, sulfamethazine, sulfaethoxypyridazine, and sulfamethazine), bacitracin, and tetracyclines are approved for oral administration in veal calves in the United States [56, 57].

Oxytetracycline and chlortetracycline have been included in scour (diarrhea) medication and supplemented in milk replacers fed to calves and may, in part, select for bacteria encoding resistance to these antibiotics. β-lactams, specifically ampicillin and amoxicillin, can be administered orally and intramuscularly for the treatment of bacterial enteritis and bovine respiratory disease (BRD), a significant cause of morbidity and mortality in calves and leads to considerable economic losses. Tulathromycin (macrolide) (subcutaneous administration), and ceftiofur (β-lactam, veterinary cephalosporin) (subcutaneous or intramuscular administration) can also be used for the treatment of BRD. Intramuscular administration of ceftiofur and florfenicol in dairy calves has been associated with a transient increase in resistant fecal E. coli [58]. Neomycin, an aminoglycoside that has been used to prevent scours in dairy calves, is not approved for oral administration in veal calves. What is not known is how historical use of antimicrobials within the birth herd (where neomycin is approved for use in replacement calves) can influence the presence and types of resistance carried by in the veal calves after they are moved from the source herd to the veal farm. These neonatal exposures should be considered a potential source of resistance that may remain within the veal calf gut after transitioning to a veal farm.

Some of the most common resistance genes among the MDR isolates confer resistance to antimicrobials approved for use in veal calves [59]. All but one MDR isolate encoded tetracycline resistance genes. β-lactamases, sulfonamide resistance genes, and aminoglycoside resistance genes were detected in most MDR isolates, and the macrolide resistance gene mph(A) was detected in a considerable number of isolates.

In addition to direct treatment with antimicrobials, calves can be exposed to antimicrobial residues in colostrum from cows treated with intramammary antibiotics at the time of dry-off (initiation of break from lactation) for mastitis treatment and prevention. Dairy operations often treat mastitis with first and third generation cephalosporins (β-lactams) and calves are sometimes fed unsaleable waste milk containing antimicrobial residues from treated lactating cows [60]. Since resistance genes are often co-located on mobile elements, exposure to one antimicrobial may select for multiple resistance genes [61]. For example, based on the genetic co-occurrence data from isolates in this study, exposure to neomycin or oxytetracycline could potentially select for trimethoprim (dfrA12), phenicol (floR), and/or sulfonamide (sul2 and sul3) resistance genes.

The co-occurrence of metal and biocide resistance genes with antibiotic resistance genes is notable and has been identified previously [10, 62–65]. Our isolate genomics results confirm the metagenomic analysis of Liu et al. [10], which showed a similar relationship in the metagenomes of dairy calf feces. Our analysis confirmed that MRGs and BRGs are associated with some ARGs, but there is also evidence of a negative cooccurrence between all metal resistance genes and the most frequently identified antibiotic resistance genes (sul2, aph(3’’)-Ib, and aph(6)-Id). Silver (sil genes) and copper (pco genes) resistance had the most frequent positive cooccurrence with ARGs, with some of these conferring resistance to antibiotics of human health significance such as bla_CTX-M-15 (ESBL) and mphA (azithromycin resistance). Associations between silver and ARGs have been noted previously, although some of these are not consistent with our findings [66, 67]. Congruent with our results, silver resistance was found to be positively associated with bla_CTX-M in E. coli by Sütterlin et al. [68]. However, this was only observed with CTX-M-15 in our analysis. Copper is present in animal feeds, including colostrum, milk, milk replacer, and calf starter, and the positive co-occurrence between pco genes and bla_CTX-M-15 and mphA indicates a potential selection for ARGs due to this dietary component. A positive co-occurrence between quaternary ammonium compound (QAC) resistance gene qacEΔ and bla_CTX-M-15 and mphA was also observed, as well as between QAC-resistance gene sugE1 and the ESBL gene bla_CMY-2. QACs are among some of the antiseptics and have been used on farms for cleaning surfaces and equipment. It is unknown if QACs were used on these veal operations, but exposure of the dams to QACs prior to calving could potentially result in exposure of the calves to these compounds, or transmission of QAC-resistant bacteria from dam to calf.

Among these E. coli isolates there was a notable occurrence of genes conferring resistance to antibiotics of public health significance such as CTX-M, as well as quinolone resistance gene qnrS1, aminoglycoside and fluoroquinolone resistance gene aac(6’)Ib-cr, and azithromycin resistance gene mph(A). Extended-spectrum β-lactamases (ESBL) are the most common proteins responsible for β-lactam resistance, and E. coli that harbor these genes are typically resistant to extended spectrum cephalosporins and monobactams. From a public health perspective, this is significant since β-lactams are among the most frequently prescribed antimicrobials globally, and ESBL-producing Enterobacteriaceae are considered a serious public health threat by the Centers for Disease Control (CDC) [https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf]. The ESBLs identified in these genomes (bla_CTX-M-and and bla_CMY) are particularly notable since they are known to confer resistance to the 3^rd generation cephalosporin, ceftazidime, ceftriaxone, cefotaxime and the 4^th generation extended spectrum penicillin/β-lactamase inhibitor piperacillin/tazobactam [World Health Organization Essential Medicine Watch Group Antimicrobials, ttps://apps.who.int/iris/rest/bitstreams/1237479/retrieve]. Resistance to β-lactams has been increasing worldwide and CTX-M lactamases are among the most prevalent ESBLs in human infections. bla_CTX-M-1 and bla_CTX-M-15 are globally distributed. bla_CTX-M-15 is notable because it is associated with pandemic ST131, but in these veal isolates it is mainly associated with A-ST10 Cplx strains. CTX-M genes were identified in strains with ExPEC VFs that were also STs associated with ExPEC infections (ST69, ST410, ST617, ST648, and ST167). The presence of plasmid-mediated quinolone resistance (PMQR) genes aac(6’)Ib-cr and qnrS1 is significant as fluoroquinolones comprise a group of broad spectrum antibiotics of critical importance in animal and human health. Both of these genes increase the quinolone minimum inhibitory concentration (MIC) which gives these strains a competitive advantage in the presence of a fluoroquinolone challenge [69]. These genes were identified in ST69, ST617, and ST167 isolates that also encoded ExPEC VFs. Azithromycin has been historically used to treat Gram-positive infections but has shown promise as an alternative to treat infections with Enterobacteriaceae that may be resistant to other commonly used therapeutics [70]. Therefore, resistance to this antibiotic is potentially an emerging public health threat’ and should be closely monitored.

Virulome differences and VFs associated with MDR and susceptible genotypes

On average, the virulomes of MDR strains and susceptible strains were also somewhat different according to the NMDS ordination analysis and the analysis of similarities test. Although it is clear that the virulence profiles of some MDR isolates are more similar to those of some susceptible isolates, it should be noted that the presence of these VFs does not confirm that the isolates are human pathogenic strains, but only indicates the potential for these strains to cause disease in humans. The health statuses of the animals were not reported and for some of these VFs their role in the pathogenesis in calves is not well-defined or are not known to cause disease in these animals. Some of these VFs are also known, or presumed, to enhance colonization of the mammalian gut, and therefore act as fitness factors that may confer a competitive advantage in the calf gut, regardless of disease outcome for the animal. Interestingly, our analysis indicated that stx1AB and stx2AB were enriched in the susceptible isolates when compared with their presence in the MDR isolates. Similarly, eae and tir, both located on the locus of enterocyte effacement (LEE) and involved in adherence to the human small intestine wall in EPEC and EHEC, were enriched in the susceptible strains and absent in the MDR strains. Their presence in susceptible strains is not surprising, but their absence in the MDR isolates is noteworthy. MDR STEC are occasionally shed by cows and calves [71–73], but among the animals sampled in this study they represent an undetectable minority based on the number of isolates collected and/or sequenced. Future work should investigate any potential interplay between carriage of stx and LEE and the presence of ARGs, or if there are veal management practices that select against MDR STEC.

Two accessory plasmid-borne iron acquisitions systems, sitABCD (Sit system) and iucABC-iutA (aerobactin), were significantly more abundant in the MDR isolates. Iron is a common limiting factor of bacterial growth and replication and is vital for many bacterial processes [74]. These two systems are involved in scavenging extracellular iron within the host environment, most notably the human gastrointestinal system and urinary tract. In E. coli, these two systems are primarily found on IncFIB plasmids, which are known to also encode multiple ARGs. IncFIB plasmid replicons were detected in all but one MDR isolate encoding sitABCD and/or iucABC-iutA genes and has previously been shown to encode both ARGs and iron acquisition systems [75, 76].

Milk has a low iron content and milk-fed calves are at a high risk of anemia [77, 78]. We hypothesize that the low input of iron into the calf gut may, in part, select for bacterial strains that encode accessory iron scavenging systems thereby allowing these organisms to outcompete strains lacking these systems. Similar to the phenomenon of antibiotic administration selecting for bacteria encoding complementary resistance genes, low iron environments potentially select for strains with genes encoding iron acquisition systems. Since these systems are co-located on resistance gene-encoding plasmids, the low iron input to the calf gut may coincidentally select for MDR strains in the absence of antibiotic administration and may synergistically act with resistance genes as simultaneous selection pressures to select for these strains. Similarly, P fimbriae genes (pap), involved in binding to glycolipids of the human urinary tract epithelial cells, were more abundant in MDR than susceptible isolates, but their role in binding to young bovine intestinal cells has not been evaluated. The differential enrichment of VFs in susceptible versus resistant strains has been previously identified in human isolates, but the selection pressures driving these trends are currently unknown [79, 80]. We suggest that such differential enrichment of accessory genes in MDR isolates confers an advantage upon these strains in the calf gut.

Based on genomic comparisons, closely related strains were isolated from different veal farms. Animals on these premises were primarily acquired from auction houses or buying stations, where animals from many different farms are typically commingled, and therefore exposed to a large suite of bacteria. This could include MDR E. coli, which they could then transmit to other animals in their cohort, either by direct contact during transport to the farm or at the farm, or through intermediary means such as farm workers or fomites. There is also the potential that different calves shedding highly similar strains were born at the same dairy farms on which they were exposed to the same microbial communities, and therefore could potentially be colonized by clonal copies of MDR E. coli that are endemic in their birth herd. Individual veal calves could not be traced back to their herd of origin to investigate this possibility. The repeated isolation of highly similar strains from different sources within a highly diverse E. coli population [43] suggests they haven an enhanced ability to persist within the veal farm environment. We have previously demonstrated that clonal MDR E. coli can be isolated from different animals (and animals of different ages) on the same farm, suggesting that transmission of E. coli occurs between animals and that some MDR strains may be selected for, or persist, in the bovine gut [15]. Results of this analysis suggest that MDR E. coli have the potential to spread between animals at auction houses and on dairy farms; transmission of these bacteria can spread between farms when co-colonized calves are sold to different veal farm operations.

This study demonstrates that MDR E. coli in veal operations are highly diverse but dominated by phylogroup A/ST Cplx 10 strains. Further, a significant proportion of these MDR strains are similar to ExPEC isolates known to cause infections and many encode VFs involved in colonization and virulence outside of the human intestine, particularly in the urinary tract. The encoded VFs include iron-scavenging systems, most likely co-located with resistance genes on plasmids, that may enhance the colonization of the low-iron, milk-fed calf gut environment. This analysis also demonstrated that ARGs of human health significance and MDR E. coli strains are circulating among veal calves in the same and different farms. Although this work focused on veal calves, it has relevance outside of this production system and future work focused on antimicrobial resistance in other systems or environments should evaluate the multiplicity of factors that may influence, or be associated with, the carriage of resistant bacteria. Research aimed towards mitigating the carriage of resistance in food animal production should consider the role of management practices, not just limited to antimicrobial administration, in the carriage and maintenance of resistant organisms.

Supporting information

S1 Table. Genome sequencing statistics for E. coli strains utilized in this study.

(XLSX)

Click here for additional data file.^{(17.9KB, xlsx)}

Acknowledgments

We gratefully acknowledge the assistance of Laura Del Collo, Jakeitha Sonnier, and Huilin Cao. The mention of a trade name, proprietary product, or specific equipment does not constitute guarantee or warranty by the USDA and does not imply approval to the exclusion of other products that might be suitable.

Data Availability

Genome sequencing data is available under BioProject PRJNA664052 in NCBI.

Funding Statement

This project was supported by internal USDA/ARS research funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Scallan E, Hoekstra RM, Angulo FJ, et al. Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis. 2011;17(1):7–15. doi: 10.3201/eid1701.p11101 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Silverman JA, Schreiber HL 4th, Hooton TM, Hultgren SJ. From physiology to pharmacy: developments in the pathogenesis and treatment of recurrent urinary tract infections. Curr Urol Rep. 2013;14(5):448–456. doi: 10.1007/s11934-013-0354-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Brannon JR, Dunigan TL, Beebout CJ, et al. Invasion of vaginal epithelial cells by uropathogenic Escherichia coli. Nat Commun. 2020;11(1):2803. Published 2020 Jun 4. doi: 10.1038/s41467-020-16627-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Oladeinde A, Cook K, Lakin SM, et al. Horizontal Gene Transfer and Acquired Antibiotic Resistance in Salmonella enterica Serovar Heidelberg following In Vitro Incubation in Broiler Ceca. Appl Environ Microbiol. 2019;85(22):e01903–19. Published 2019 Oct 30. doi: 10.1128/AEM.01903-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Devanga Ragupathi NK, Muthuirulandi Sethuvel DP, Gajendran R, Anandan S, Walia K, Veeraraghavan B. Horizontal Transfer of Antimicrobial Resistance Determinants Among Enteric Pathogens Through Bacterial Conjugation. Curr Microbiol. 2019. Jun;76(6):666–672. doi: 10.1007/s00284-019-01676-x [DOI] [PubMed] [Google Scholar]
6.Saliu EM, Eitinger M, Zentek J, Vahjen W. Nutrition Related Stress Factors Reduce the Transfer of Extended-Spectrum Beta-Lactamase Resistance Genes between an Escherichia coli Donor and a Salmonella Typhimurium Recipient In Vitro. Biomolecules. 2019. Jul 31;9(8):324. doi: 10.3390/biom9080324 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS, et al. Burden of AMR Collaborative Group. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis 2019;19(1):56–66. doi: 10.1016/S1473-3099(18)30605-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Centers for Disease Control and Prevention (CDC). Antibiotic Resistance Threats in the United States, 2019. CDC, Atlanta, GA (2019) (https://www.cdc.gov/drugresistance/biggest-threats.html)
9.O’Neill, J. Review on AMR, Antimicrobial resistance: Tackling a crisis for the health and wealth of nations, 2014. Accessed on 2 August 2021. https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf
10.Liu J, Taft DH, Maldonado-Gomez MX, et al. The fecal resistome of dairy cattle is associated with diet during nursing. Nat Commun. 2019;10(1):4406. Published 2019 Sep 27. doi: 10.1038/s41467-019-12111-x [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Liu J, Zhao Z, Avillan JJ, et al. Dairy farm soil presents distinct microbiota and varied prevalence of antibiotic resistance across housing areas. Environ Pollut. 2019;254(Pt B):113058. doi: 10.1016/j.envpol.2019.113058 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Cao H, Pradhan AK, Karns JS, et al. Age-Associated Distribution of Antimicrobial-Resistant Salmonella enterica and Escherichia coli Isolated from Dairy Herds in Pennsylvania, 2013–2015. Foodborne Pathog Dis. 2019;16(1):60–67. doi: 10.1089/fpd.2018.2519 [DOI] [PubMed] [Google Scholar]
13.Springer HR, Denagamage TN, Fenton GD, Haley BJ, Van Kessel JAS, Hovingh EP. Antimicrobial Resistance in Fecal Escherichia coli and Salmonella enterica from Dairy Calves: A Systematic Review. Foodborne Pathog Dis. 2019;16(1):23–34. doi: 10.1089/fpd.2018.2529 [DOI] [PubMed] [Google Scholar]
14.Haley BJ, Kim SW, Salaheen S, Hovingh E, Van Kessel JAS. Differences in the Microbial Community and Resistome Structures of Feces from Preweaned Calves and Lactating Dairy Cows in Commercial Dairy Herds [published online ahead of print, 2020 Mar 13]. Foodborne Pathog Dis. 2020;10.1089/fpd.2019.2768. doi: 10.1089/fpd.2019.2768 [DOI] [PubMed] [Google Scholar]
15.Salaheen S, Cao H, Sonnier JL, et al. Diversity of Extended-Spectrum Cephalosporin-Resistant Escherichia coli in Feces from Calves and Cows on Pennsylvania Dairy Farms. Foodborne Pathog Dis. 2019;16(5):368–370. doi: 10.1089/fpd.2018.2579 [DOI] [PubMed] [Google Scholar]
16.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi: 10.1093/bioinformatics/btu170 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One. 2011;6(3):e17288. Published 2011 Mar 9. doi: 10.1371/journal.pone.0017288 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bankevich A, Nurk S, Antipov D, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–477. doi: 10.1089/cmb.2012.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Achtman M, Wain J, Weill FX, et al. Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLoS Pathog. 2012;8(6):e1002776. doi: 10.1371/journal.ppat.1002776 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Carattoli A, Zankari E, García-Fernández A, et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58(7):3895–3903. doi: 10.1128/AAC.02412-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. 2012. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 67(11):2640–4. doi: 10.1093/jac/dks261 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Beghain J, Bridier-Nahmias A, Le Nagard H, Denamur E, Clermont O. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb Genom. 2018;4(7):e000192. doi: 10.1099/mgen.0.000192 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Waters N, Abram F, Brennan F, Holmes A, Pritchard L. 2020. Easy phylotyping of Escherichia coli via the EzClermont web app and command-line tool. Access Microbiology. doi: 10.1099/acmi.0.000143 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Liu B, Zheng DD, Jin Q, Chen LH and Yang J, 2019. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 47(D1):D687–D692. doi: 10.1093/nar/gky1080 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Pal C., Bengtsson-Palme J., Rensing C., Kristiansson E., Larsson DGJ. (2014) BacMet: antibacterial biocide and metal resistance genes database, Nucleic Acids Res., 42, D737–D743. doi: 10.1093/nar/gkt1252 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Oksanen J., Blanchet F. G., Friendly M., Kindt R., Legendre P., McGlinn D., et al. (2019). vegan: Community Ecology Package. R package version 2.5–5. Available Online at: https://CRAN.R-project.org/package=vegan [Google Scholar]
27.Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003; 100(16):9440–5. doi: 10.1073/pnas.1530509100 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Griffith D., Veech J. & Marsh C. cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. Code Snippets 69, 1–17 (2016). [Google Scholar]
29.Bastian M, Heymann S, Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. AAI Publications. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, San Jose, CA, USA.
30.Jacomy M, Venturini T, Heymann S, Bastian M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one. 2014. Jun 10;9(6):e98679. doi: 10.1371/journal.pone.0098679 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol. 2014;15(11):524. doi: 10.1186/s13059-014-0524-x [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–1313. doi: 10.1093/bioinformatics/btu033 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Katz LS, Griswold T, Williams-Newkirk AJ, et al. A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens. Front Microbiol. 2017;8:375. Published 2017 Mar 13. doi: 10.3389/fmicb.2017.00375 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Salaheen S, Kim SW, Hovingh E, Van Kessel JAS, Haley BJ. Metagenomic Analysis of the Microbial Communities and Resistomes of Veal Calf Feces. Front Microbiol. 2021. Feb 9;11:609950. doi: 10.3389/fmicb.2020.609950 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Capita R, Cordero J, Molina-González D, Igrejas G, Poeta P, Alonso-Calleja C. Phylogenetic Diversity, Antimicrobial Susceptibility and Virulence Characteristics of Escherichia coli Isolates from Pigeon Meat. Antibiotics (Basel). 2019;8(4):259. Published 2019 Dec 10. doi: 10.3390/antibiotics8040259 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Munkhdelger Y, Gunregjav N, Dorjpurev A, Juniichiro N, Sarantuya J. Detection of virulence genes, phylogenetic group and antibiotic resistance of uropathogenic Escherichia coli in Mongolia. J Infect Dev Ctries. 2017;11(1):51–57. Published 2017 Jan 30. doi: 10.3855/jidc.7903 [DOI] [PubMed] [Google Scholar]
37.Iranpour D, Hassanpour M, Ansari H, Tajbakhsh S, Khamisipour G, Najafi A. Phylogenetic groups of Escherichia coli strains from patients with urinary tract infection in Iran based on the new Clermont phylotyping method. Biomed Res Int. 2015;2015:846219. doi: 10.1155/2015/846219 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Mukherjee M, Koley S, Mukherjee S, Basu S, Ghosh B, Chakraborty S. Phylogenetic background of E. coli isolated from asymptomatic pregnant women from Kolkata, India. J Infect Dev Ctries. 2015;9(7):720–724. Published 2015 Jul 30. doi: 10.3855/jidc.5771 [DOI] [PubMed] [Google Scholar]
39.Aslantaş Ö. Antimicrobial Resistance among Commensal Escherichia coli from Broilers in Turkey. Israel Journal of Veterinary Medicine Vol. 73 (3) 2018 [Google Scholar]
40.Ferreira JC, Penha Filho RAC, Kuaye APY, Andrade LN, Chang YF, Darini ALC. Virulence potential of commensal multidrug resistant Escherichia coli isolated from poultry in Brazil. Infect Genet Evol. 2018;65:251–256. doi: 10.1016/j.meegid.2018.07.037 [DOI] [PubMed] [Google Scholar]
41.Purohit MR, Lindahl LF, Diwan V, Marrone G, Lundborg CS. High levels of drug resistance in commensal E. coli in a cohort of children from rural central India. Sci Rep. 2019;9(1):6682. Published 2019 Apr 30. doi: 10.1038/s41598-019-43227-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Johnson TJ, Logue CM, Johnson JR, et al. Associations between multidrug resistance, plasmid content, and virulence potential among extraintestinal pathogenic and commensal Escherichia coli from humans and poultry. Foodborne Pathog Dis. 2012;9(1):37–46. doi: 10.1089/fpd.2011.0961 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Son I, Van Kessel JA, Karns JS. 2009. Genotypic diversity of Escherichia coli in a dairy farm. Foodborne Pathog. Dis. 6:837–847 doi: 10.1089/fpd.2008.0201 [DOI] [PubMed] [Google Scholar]
44.Tenaillon O, Skurnik D, Picard B, Denamur E. 2010. The population genetics of commensal Escherichia coli. Nat. Rev. Microbiol. 8:207–217. doi: 10.1038/nrmicro2298 [DOI] [PubMed] [Google Scholar]
45.Coura FM, Diniz Sde A, Silva MX, et al. Phylogenetic Group Determination of Escherichia coli Isolated from Animals Samples. ScientificWorldJournal. 2015;2015:258424. doi: 10.1155/2015/258424 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Madoshi BP, Kudirkiene E, Mtambo MM, Muhairwa AP, Lupindu AM, Olsen JE. Characterisation of Commensal Escherichia coli Isolated from Apparently Healthy Cattle and Their Attendants in Tanzania. PLoS One. 2016;11(12):e0168160. Published 2016 Dec 15. doi: 10.1371/journal.pone.0168160 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Valat C, Auvray F, Forest K, et al. Phylogenetic grouping and virulence potential of extended-spectrum-β-lactamase-producing Escherichia coli strains in cattle. Appl Environ Microbiol. 2012;78(13):4677–4682. doi: 10.1128/AEM.00351-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Manges A.R., Harel J., Masson L., Edens T.J., Portt A., Reid-Smith R.J., et al. Multilocus sequence typing and virulence gene profiles associated with Escherichia coli from human and animal sources. Foodborne Pathog Dis, 12 (2015), pp. 302–310. doi: 10.1089/fpd.2014.1860 [DOI] [PubMed] [Google Scholar]
49.Riley LW. Pandemic lineages of extraintestinal pathogenic Escherichia coli. Clin Microbiol Infect. 2014;20(5):380–390. doi: 10.1111/1469-0691.12646 [DOI] [PubMed] [Google Scholar]
50.Schaufler K, Semmler T, Wieler LH, et al. Clonal spread and interspecies transmission of clinically relevant ESBL-producing Escherichia coli of ST410—another successful pandemic clone?. FEMS Microbiol Ecol. 2016;92(1):fiv155. doi: 10.1093/femsec/fiv155 [DOI] [PubMed] [Google Scholar]
51.Roer L, Overballe-Petersen S, Hansen F, et al. Escherichia coli Sequence Type 410 Is Causing New International High-Risk Clones. mSphere. 2018;3(4):e00337–18. Published 2018 Jul 18. doi: 10.1128/mSphere.00337-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Manges AR, Geum HM, Guo A, Edens TJ, Fibke CD, Pitout JDD. Global Extraintestinal Pathogenic Escherichia coli (ExPEC) Lineages. Clin Microbiol Rev. 2019;32(3):e00135–18. Published 2019 Jun 12. doi: 10.1128/CMR.00135-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Johnson TJ, Logue CM, Wannemuehler Y, et al. Examination of the source and extended virulence genotypes of Escherichia coli contaminating retail poultry meat. Foodborne Pathog Dis. 2009;6(6):657–667. doi: 10.1089/fpd.2009.0266 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Maluta RP, Logue CM, Casas MR, et al. Overlapped sequence types (STs) and serogroups of avian pathogenic (APEC) and human extra-intestinal pathogenic (ExPEC) Escherichia coli isolated in Brazil. PLoS One. 2014;9(8):e105016. Published 2014 Aug 12. doi: 10.1371/journal.pone.0105016 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Manges AR. Escherichia coli and urinary tract infections: the role of poultry-meat. Clin Microbiol Infect. 2016;22(2):122–129. doi: 10.1016/j.cmi.2015.11.010 [DOI] [PubMed] [Google Scholar]
56.Veterinary feed directive. United States Food and Drug Administration (FDA). https://www.fda.gov/animal-veterinary/development-approval-process/veterinary-feed-directive-vfd
57.Food Animal Residue Avoidance Databank (FARAD). www.farad.org/vetgram/cattle_veal.asp accessed on 20 December 2020.
58.Liu J, Zhao Z, Orfe L, Subbiah M, Call DR. Soil-borne reservoirs of antibiotic-resistant bacteria are established following therapeutic treatment of dairy calves. Environ Microbiol. 2016. Feb;18(2):557–64. doi: 10.1111/1462-2920.13097 [DOI] [PubMed] [Google Scholar]
59.Food Animal Residue Avoidance Databank; VetGRAM (Veterinarian’s Guide to Residue Avoidance Management). http://www.farad.org/vetgram/calves.asp accessed on 08/01/2021
60.U.S. Department of Agriculture (USDA). Animal and Plant Health Inspection Service (APHIS) (2018). Dairy 2014b: Milk Quality, Milking Procedures, and Mastitis on U.S. Dairies, 2014. https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms/NAHMS_Dairy_Studies (Accessed June 30, 2020)
61.Bischoff KM, White DG, Hume ME, Poole TL, Nisbet DJ. The chloramphenicol resistance gene cmlA is disseminated on transferable plasmids that confer multiple-drug resistance in swine Escherichia coli. FEMS Microbiol Lett. 2005;243(1):285–291. doi: 10.1016/j.femsle.2004.12.017 [DOI] [PubMed] [Google Scholar]
62.Li Y, McCrory DF, Powell JM, Saam H, Jackson-Smith D. A survey of selected heavy metal concentrations in Wisconsin dairy feeds. J Dairy Sci. 2005;88(8):2911–2922. doi: 10.3168/jds.S0022-0302(05)72972-6 [DOI] [PubMed] [Google Scholar]
63.Zhu Y. G. et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc. Natl Acad. Sci. USA 110, 3435–3440 (2013). doi: 10.1073/pnas.1222743110 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Seiler C. & Berendonk T. U. Heavy metal driven co-selection of antibiotic resistance in soil and water bodies impacted by agriculture and aquaculture. Front. Microbiol. 3, 399 (2012). doi: 10.3389/fmicb.2012.00399 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Baker-Austin C., Wright M. S., Stepanauskas R. & McArthur J. V. Co-selection of antibiotic and metal resistance. Trends Microbiol. 14, 176–182 (2006). doi: 10.1016/j.tim.2006.02.006 [DOI] [PubMed] [Google Scholar]
66.Yuan L, Li ZH, Zhang MQ, Shao W, Fan YY, Sheng GP. Mercury/silver resistance genes and their association with antibiotic resistance genes and microbial community in a municipal wastewater treatment plant. Sci Total Environ. 2019;657:1014–1022. doi: 10.1016/j.scitotenv.2018.12.088 [DOI] [PubMed] [Google Scholar]
67.Loh JV, Percival SL, Woods EJ, Williams NJ, Cochrane CA. Silver resistance in MRSA isolated from wound and nasal sources in humans and animals. Int Wound J. 2009;6(1):32–38. doi: 10.1111/j.1742-481X.2008.00563.x [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Sütterlin S, Edquist P, Sandegren L, et al. Silver resistance genes are overrepresented among Escherichia coli isolates with CTX-M production. Appl Environ Microbiol. 2014;80(22):6863–6869. doi: 10.1128/AEM.01803-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Machuca J, Ortiz M, Recacha E, et al. Impact of AAC(6’)-Ib-cr in combination with chromosomal-mediated mechanisms on clinical quinolone resistance in Escherichia coli. J Antimicrob Chemother. 2016;71(11):3066–3071. doi: 10.1093/jac/dkw258 [DOI] [PubMed] [Google Scholar]
70.Gomes C, Ruiz-Roldán L, Mateu J, Ochoa TJ, Ruiz J. Azithromycin resistance levels and mechanisms in Escherichia coli. Sci Rep. 2019;9(1):6089. Published 2019 Apr 15. doi: 10.1038/s41598-019-42423-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Cho S, Fossler CP, Diez-Gonzalez F, et al. Antimicrobial susceptibility of Shiga toxin-producing Escherichia coli isolated from organic dairy farms, conventional dairy farms, and county fairs in Minnesota. Foodborne Pathog Dis. 2007;4(2):178–186. doi: 10.1089/fpd.2006.0074 [DOI] [PubMed] [Google Scholar]
72.Kang E, Hwang SY, Kwon KH, Kim KY, Kim JH, Park YH. Prevalence and characteristics of Shiga toxin-producing Escherichia coli (STEC) from cattle in Korea between 2010 and 2011. J Vet Sci. 2014;15(3):369–379. doi: 10.4142/jvs.2014.15.3.369 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Kennedy CA, Fanning S, Karczmarczyk M, et al. Characterizing the Multidrug Resistance of non-O157 Shiga Toxin-Producing Escherichia coli Isolates from Cattle Farms and Abattoirs. Microb Drug Resist. 2017;23(6):781–790. doi: 10.1089/mdr.2016.0082 [DOI] [PubMed] [Google Scholar]
74.Ratledge C, Dover LG. Iron Metabolism in Pathogenic Bacteria Annual Review of Microbiology 2000. 54:1, 881–941 [DOI] [PubMed] [Google Scholar]
75.Fricke WF, McDermott PF, Mammel MK, et al. Antimicrobial resistance-conferring plasmids with similarity to virulence plasmids from avian pathogenic Escherichia coli strains in Salmonella enterica serovar Kentucky isolates from poultry. Appl Environ Microbiol. 2009;75(18):5963–5971. doi: 10.1128/AEM.00786-09 [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Khajanchi BK, Hasan NA, Choi SY, et al. Comparative genomic analysis and characterization of incompatibility group FIB plasmid encoded virulence factors of Salmonella enterica isolated from food sources. BMC Genomics. 2017;18(1):570. Published 2017 Aug 2. doi: 10.1186/s12864-017-3954-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Miltenburg GA, Wensing T, van de Broek J, Mevius DJ, Breukink HJ. Effects of different iron contents in the milk replacer on the development of iron deficiency anaemia in veal calves. Vet Q. 1992;14(1):18–21. doi: 10.1080/01652176.1992.9694320 [DOI] [PubMed] [Google Scholar]
78.Allan J, Plate P, Van Winden S. The Effect of Iron Dextran Injection on Daily Weight Gain and Haemoglobin Values in Whole Milk Fed Calves. Animals (Basel). 2020;10(5):853. Published 2020 May 14. doi: 10.3390/ani10050853 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Harwalkar A, Gupta S, Rao A, Srinivasa H. Lower prevalence of hlyD, papC and cnf-1 genes in ciprofloxacin-resistant uropathogenic Escherichia coli than their susceptible counterparts isolated from southern India. J Infect Public Health. 2014;7(5):413–419. doi: 10.1016/j.jiph.2014.04.002 [DOI] [PubMed] [Google Scholar]
80.Piatti G, Mannini A, Balistreri M, Schito AM. Virulence factors in urinary Escherichia coli strains: phylogenetic background and quinolone and fluoroquinolone resistance. J Clin Microbiol. 2008;46(2):480–487. doi: 10.1128/JCM.01488-07 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Genome sequencing statistics for E. coli strains utilized in this study.

(XLSX)

Click here for additional data file.^{(17.9KB, xlsx)}

Data Availability Statement

Genome sequencing data is available under BioProject PRJNA664052 in NCBI.

[pone.0265445.ref001] 1.Scallan E, Hoekstra RM, Angulo FJ, et al. Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis. 2011;17(1):7–15. doi: 10.3201/eid1701.p11101 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref002] 2.Silverman JA, Schreiber HL 4th, Hooton TM, Hultgren SJ. From physiology to pharmacy: developments in the pathogenesis and treatment of recurrent urinary tract infections. Curr Urol Rep. 2013;14(5):448–456. doi: 10.1007/s11934-013-0354-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref003] 3.Brannon JR, Dunigan TL, Beebout CJ, et al. Invasion of vaginal epithelial cells by uropathogenic Escherichia coli. Nat Commun. 2020;11(1):2803. Published 2020 Jun 4. doi: 10.1038/s41467-020-16627-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref004] 4.Oladeinde A, Cook K, Lakin SM, et al. Horizontal Gene Transfer and Acquired Antibiotic Resistance in Salmonella enterica Serovar Heidelberg following In Vitro Incubation in Broiler Ceca. Appl Environ Microbiol. 2019;85(22):e01903–19. Published 2019 Oct 30. doi: 10.1128/AEM.01903-19 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref005] 5.Devanga Ragupathi NK, Muthuirulandi Sethuvel DP, Gajendran R, Anandan S, Walia K, Veeraraghavan B. Horizontal Transfer of Antimicrobial Resistance Determinants Among Enteric Pathogens Through Bacterial Conjugation. Curr Microbiol. 2019. Jun;76(6):666–672. doi: 10.1007/s00284-019-01676-x [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref006] 6.Saliu EM, Eitinger M, Zentek J, Vahjen W. Nutrition Related Stress Factors Reduce the Transfer of Extended-Spectrum Beta-Lactamase Resistance Genes between an Escherichia coli Donor and a Salmonella Typhimurium Recipient In Vitro. Biomolecules. 2019. Jul 31;9(8):324. doi: 10.3390/biom9080324 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref007] 7.Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS, et al. Burden of AMR Collaborative Group. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis 2019;19(1):56–66. doi: 10.1016/S1473-3099(18)30605-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref008] 8.Centers for Disease Control and Prevention (CDC). Antibiotic Resistance Threats in the United States, 2019. CDC, Atlanta, GA (2019) (https://www.cdc.gov/drugresistance/biggest-threats.html)

[pone.0265445.ref009] 9.O’Neill, J. Review on AMR, Antimicrobial resistance: Tackling a crisis for the health and wealth of nations, 2014. Accessed on 2 August 2021. https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf

[pone.0265445.ref010] 10.Liu J, Taft DH, Maldonado-Gomez MX, et al. The fecal resistome of dairy cattle is associated with diet during nursing. Nat Commun. 2019;10(1):4406. Published 2019 Sep 27. doi: 10.1038/s41467-019-12111-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref011] 11.Liu J, Zhao Z, Avillan JJ, et al. Dairy farm soil presents distinct microbiota and varied prevalence of antibiotic resistance across housing areas. Environ Pollut. 2019;254(Pt B):113058. doi: 10.1016/j.envpol.2019.113058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref012] 12.Cao H, Pradhan AK, Karns JS, et al. Age-Associated Distribution of Antimicrobial-Resistant Salmonella enterica and Escherichia coli Isolated from Dairy Herds in Pennsylvania, 2013–2015. Foodborne Pathog Dis. 2019;16(1):60–67. doi: 10.1089/fpd.2018.2519 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref013] 13.Springer HR, Denagamage TN, Fenton GD, Haley BJ, Van Kessel JAS, Hovingh EP. Antimicrobial Resistance in Fecal Escherichia coli and Salmonella enterica from Dairy Calves: A Systematic Review. Foodborne Pathog Dis. 2019;16(1):23–34. doi: 10.1089/fpd.2018.2529 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref014] 14.Haley BJ, Kim SW, Salaheen S, Hovingh E, Van Kessel JAS. Differences in the Microbial Community and Resistome Structures of Feces from Preweaned Calves and Lactating Dairy Cows in Commercial Dairy Herds [published online ahead of print, 2020 Mar 13]. Foodborne Pathog Dis. 2020;10.1089/fpd.2019.2768. doi: 10.1089/fpd.2019.2768 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref015] 15.Salaheen S, Cao H, Sonnier JL, et al. Diversity of Extended-Spectrum Cephalosporin-Resistant Escherichia coli in Feces from Calves and Cows on Pennsylvania Dairy Farms. Foodborne Pathog Dis. 2019;16(5):368–370. doi: 10.1089/fpd.2018.2579 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref016] 16.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi: 10.1093/bioinformatics/btu170 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref017] 17.Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One. 2011;6(3):e17288. Published 2011 Mar 9. doi: 10.1371/journal.pone.0017288 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref018] 18.Bankevich A, Nurk S, Antipov D, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–477. doi: 10.1089/cmb.2012.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref019] 19.Achtman M, Wain J, Weill FX, et al. Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLoS Pathog. 2012;8(6):e1002776. doi: 10.1371/journal.ppat.1002776 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref020] 20.Carattoli A, Zankari E, García-Fernández A, et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58(7):3895–3903. doi: 10.1128/AAC.02412-14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref021] 21.Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. 2012. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 67(11):2640–4. doi: 10.1093/jac/dks261 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref022] 22.Beghain J, Bridier-Nahmias A, Le Nagard H, Denamur E, Clermont O. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb Genom. 2018;4(7):e000192. doi: 10.1099/mgen.0.000192 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref023] 23.Waters N, Abram F, Brennan F, Holmes A, Pritchard L. 2020. Easy phylotyping of Escherichia coli via the EzClermont web app and command-line tool. Access Microbiology. doi: 10.1099/acmi.0.000143 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref024] 24.Liu B, Zheng DD, Jin Q, Chen LH and Yang J, 2019. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 47(D1):D687–D692. doi: 10.1093/nar/gky1080 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref025] 25.Pal C., Bengtsson-Palme J., Rensing C., Kristiansson E., Larsson DGJ. (2014) BacMet: antibacterial biocide and metal resistance genes database, Nucleic Acids Res., 42, D737–D743. doi: 10.1093/nar/gkt1252 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref026] 26.Oksanen J., Blanchet F. G., Friendly M., Kindt R., Legendre P., McGlinn D., et al. (2019). vegan: Community Ecology Package. R package version 2.5–5. Available Online at: https://CRAN.R-project.org/package=vegan [Google Scholar]

[pone.0265445.ref027] 27.Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003; 100(16):9440–5. doi: 10.1073/pnas.1530509100 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref028] 28.Griffith D., Veech J. & Marsh C. cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. Code Snippets 69, 1–17 (2016). [Google Scholar]

[pone.0265445.ref029] 29.Bastian M, Heymann S, Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. AAI Publications. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, San Jose, CA, USA.

[pone.0265445.ref030] 30.Jacomy M, Venturini T, Heymann S, Bastian M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one. 2014. Jun 10;9(6):e98679. doi: 10.1371/journal.pone.0098679 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref031] 31.Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol. 2014;15(11):524. doi: 10.1186/s13059-014-0524-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref032] 32.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–1313. doi: 10.1093/bioinformatics/btu033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref033] 33.Katz LS, Griswold T, Williams-Newkirk AJ, et al. A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens. Front Microbiol. 2017;8:375. Published 2017 Mar 13. doi: 10.3389/fmicb.2017.00375 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref034] 34.Salaheen S, Kim SW, Hovingh E, Van Kessel JAS, Haley BJ. Metagenomic Analysis of the Microbial Communities and Resistomes of Veal Calf Feces. Front Microbiol. 2021. Feb 9;11:609950. doi: 10.3389/fmicb.2020.609950 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref035] 35.Capita R, Cordero J, Molina-González D, Igrejas G, Poeta P, Alonso-Calleja C. Phylogenetic Diversity, Antimicrobial Susceptibility and Virulence Characteristics of Escherichia coli Isolates from Pigeon Meat. Antibiotics (Basel). 2019;8(4):259. Published 2019 Dec 10. doi: 10.3390/antibiotics8040259 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref036] 36.Munkhdelger Y, Gunregjav N, Dorjpurev A, Juniichiro N, Sarantuya J. Detection of virulence genes, phylogenetic group and antibiotic resistance of uropathogenic Escherichia coli in Mongolia. J Infect Dev Ctries. 2017;11(1):51–57. Published 2017 Jan 30. doi: 10.3855/jidc.7903 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref037] 37.Iranpour D, Hassanpour M, Ansari H, Tajbakhsh S, Khamisipour G, Najafi A. Phylogenetic groups of Escherichia coli strains from patients with urinary tract infection in Iran based on the new Clermont phylotyping method. Biomed Res Int. 2015;2015:846219. doi: 10.1155/2015/846219 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref038] 38.Mukherjee M, Koley S, Mukherjee S, Basu S, Ghosh B, Chakraborty S. Phylogenetic background of E. coli isolated from asymptomatic pregnant women from Kolkata, India. J Infect Dev Ctries. 2015;9(7):720–724. Published 2015 Jul 30. doi: 10.3855/jidc.5771 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref039] 39.Aslantaş Ö. Antimicrobial Resistance among Commensal Escherichia coli from Broilers in Turkey. Israel Journal of Veterinary Medicine Vol. 73 (3) 2018 [Google Scholar]

[pone.0265445.ref040] 40.Ferreira JC, Penha Filho RAC, Kuaye APY, Andrade LN, Chang YF, Darini ALC. Virulence potential of commensal multidrug resistant Escherichia coli isolated from poultry in Brazil. Infect Genet Evol. 2018;65:251–256. doi: 10.1016/j.meegid.2018.07.037 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref041] 41.Purohit MR, Lindahl LF, Diwan V, Marrone G, Lundborg CS. High levels of drug resistance in commensal E. coli in a cohort of children from rural central India. Sci Rep. 2019;9(1):6682. Published 2019 Apr 30. doi: 10.1038/s41598-019-43227-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref042] 42.Johnson TJ, Logue CM, Johnson JR, et al. Associations between multidrug resistance, plasmid content, and virulence potential among extraintestinal pathogenic and commensal Escherichia coli from humans and poultry. Foodborne Pathog Dis. 2012;9(1):37–46. doi: 10.1089/fpd.2011.0961 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref043] 43.Son I, Van Kessel JA, Karns JS. 2009. Genotypic diversity of Escherichia coli in a dairy farm. Foodborne Pathog. Dis. 6:837–847 doi: 10.1089/fpd.2008.0201 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref044] 44.Tenaillon O, Skurnik D, Picard B, Denamur E. 2010. The population genetics of commensal Escherichia coli. Nat. Rev. Microbiol. 8:207–217. doi: 10.1038/nrmicro2298 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref045] 45.Coura FM, Diniz Sde A, Silva MX, et al. Phylogenetic Group Determination of Escherichia coli Isolated from Animals Samples. ScientificWorldJournal. 2015;2015:258424. doi: 10.1155/2015/258424 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref046] 46.Madoshi BP, Kudirkiene E, Mtambo MM, Muhairwa AP, Lupindu AM, Olsen JE. Characterisation of Commensal Escherichia coli Isolated from Apparently Healthy Cattle and Their Attendants in Tanzania. PLoS One. 2016;11(12):e0168160. Published 2016 Dec 15. doi: 10.1371/journal.pone.0168160 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref047] 47.Valat C, Auvray F, Forest K, et al. Phylogenetic grouping and virulence potential of extended-spectrum-β-lactamase-producing Escherichia coli strains in cattle. Appl Environ Microbiol. 2012;78(13):4677–4682. doi: 10.1128/AEM.00351-12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref048] 48.Manges A.R., Harel J., Masson L., Edens T.J., Portt A., Reid-Smith R.J., et al. Multilocus sequence typing and virulence gene profiles associated with Escherichia coli from human and animal sources. Foodborne Pathog Dis, 12 (2015), pp. 302–310. doi: 10.1089/fpd.2014.1860 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref049] 49.Riley LW. Pandemic lineages of extraintestinal pathogenic Escherichia coli. Clin Microbiol Infect. 2014;20(5):380–390. doi: 10.1111/1469-0691.12646 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref050] 50.Schaufler K, Semmler T, Wieler LH, et al. Clonal spread and interspecies transmission of clinically relevant ESBL-producing Escherichia coli of ST410—another successful pandemic clone?. FEMS Microbiol Ecol. 2016;92(1):fiv155. doi: 10.1093/femsec/fiv155 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref051] 51.Roer L, Overballe-Petersen S, Hansen F, et al. Escherichia coli Sequence Type 410 Is Causing New International High-Risk Clones. mSphere. 2018;3(4):e00337–18. Published 2018 Jul 18. doi: 10.1128/mSphere.00337-18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref052] 52.Manges AR, Geum HM, Guo A, Edens TJ, Fibke CD, Pitout JDD. Global Extraintestinal Pathogenic Escherichia coli (ExPEC) Lineages. Clin Microbiol Rev. 2019;32(3):e00135–18. Published 2019 Jun 12. doi: 10.1128/CMR.00135-18 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref053] 53.Johnson TJ, Logue CM, Wannemuehler Y, et al. Examination of the source and extended virulence genotypes of Escherichia coli contaminating retail poultry meat. Foodborne Pathog Dis. 2009;6(6):657–667. doi: 10.1089/fpd.2009.0266 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref054] 54.Maluta RP, Logue CM, Casas MR, et al. Overlapped sequence types (STs) and serogroups of avian pathogenic (APEC) and human extra-intestinal pathogenic (ExPEC) Escherichia coli isolated in Brazil. PLoS One. 2014;9(8):e105016. Published 2014 Aug 12. doi: 10.1371/journal.pone.0105016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref055] 55.Manges AR. Escherichia coli and urinary tract infections: the role of poultry-meat. Clin Microbiol Infect. 2016;22(2):122–129. doi: 10.1016/j.cmi.2015.11.010 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref056] 56.Veterinary feed directive. United States Food and Drug Administration (FDA). https://www.fda.gov/animal-veterinary/development-approval-process/veterinary-feed-directive-vfd

[pone.0265445.ref057] 57.Food Animal Residue Avoidance Databank (FARAD). www.farad.org/vetgram/cattle_veal.asp accessed on 20 December 2020.

[pone.0265445.ref058] 58.Liu J, Zhao Z, Orfe L, Subbiah M, Call DR. Soil-borne reservoirs of antibiotic-resistant bacteria are established following therapeutic treatment of dairy calves. Environ Microbiol. 2016. Feb;18(2):557–64. doi: 10.1111/1462-2920.13097 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref059] 59.Food Animal Residue Avoidance Databank; VetGRAM (Veterinarian’s Guide to Residue Avoidance Management). http://www.farad.org/vetgram/calves.asp accessed on 08/01/2021

[pone.0265445.ref060] 60.U.S. Department of Agriculture (USDA). Animal and Plant Health Inspection Service (APHIS) (2018). Dairy 2014b: Milk Quality, Milking Procedures, and Mastitis on U.S. Dairies, 2014. https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms/NAHMS_Dairy_Studies (Accessed June 30, 2020)

[pone.0265445.ref061] 61.Bischoff KM, White DG, Hume ME, Poole TL, Nisbet DJ. The chloramphenicol resistance gene cmlA is disseminated on transferable plasmids that confer multiple-drug resistance in swine Escherichia coli. FEMS Microbiol Lett. 2005;243(1):285–291. doi: 10.1016/j.femsle.2004.12.017 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref062] 62.Li Y, McCrory DF, Powell JM, Saam H, Jackson-Smith D. A survey of selected heavy metal concentrations in Wisconsin dairy feeds. J Dairy Sci. 2005;88(8):2911–2922. doi: 10.3168/jds.S0022-0302(05)72972-6 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref063] 63.Zhu Y. G. et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc. Natl Acad. Sci. USA 110, 3435–3440 (2013). doi: 10.1073/pnas.1222743110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref064] 64.Seiler C. & Berendonk T. U. Heavy metal driven co-selection of antibiotic resistance in soil and water bodies impacted by agriculture and aquaculture. Front. Microbiol. 3, 399 (2012). doi: 10.3389/fmicb.2012.00399 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref065] 65.Baker-Austin C., Wright M. S., Stepanauskas R. & McArthur J. V. Co-selection of antibiotic and metal resistance. Trends Microbiol. 14, 176–182 (2006). doi: 10.1016/j.tim.2006.02.006 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref066] 66.Yuan L, Li ZH, Zhang MQ, Shao W, Fan YY, Sheng GP. Mercury/silver resistance genes and their association with antibiotic resistance genes and microbial community in a municipal wastewater treatment plant. Sci Total Environ. 2019;657:1014–1022. doi: 10.1016/j.scitotenv.2018.12.088 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref067] 67.Loh JV, Percival SL, Woods EJ, Williams NJ, Cochrane CA. Silver resistance in MRSA isolated from wound and nasal sources in humans and animals. Int Wound J. 2009;6(1):32–38. doi: 10.1111/j.1742-481X.2008.00563.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref068] 68.Sütterlin S, Edquist P, Sandegren L, et al. Silver resistance genes are overrepresented among Escherichia coli isolates with CTX-M production. Appl Environ Microbiol. 2014;80(22):6863–6869. doi: 10.1128/AEM.01803-14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref069] 69.Machuca J, Ortiz M, Recacha E, et al. Impact of AAC(6’)-Ib-cr in combination with chromosomal-mediated mechanisms on clinical quinolone resistance in Escherichia coli. J Antimicrob Chemother. 2016;71(11):3066–3071. doi: 10.1093/jac/dkw258 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref070] 70.Gomes C, Ruiz-Roldán L, Mateu J, Ochoa TJ, Ruiz J. Azithromycin resistance levels and mechanisms in Escherichia coli. Sci Rep. 2019;9(1):6089. Published 2019 Apr 15. doi: 10.1038/s41598-019-42423-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref071] 71.Cho S, Fossler CP, Diez-Gonzalez F, et al. Antimicrobial susceptibility of Shiga toxin-producing Escherichia coli isolated from organic dairy farms, conventional dairy farms, and county fairs in Minnesota. Foodborne Pathog Dis. 2007;4(2):178–186. doi: 10.1089/fpd.2006.0074 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref072] 72.Kang E, Hwang SY, Kwon KH, Kim KY, Kim JH, Park YH. Prevalence and characteristics of Shiga toxin-producing Escherichia coli (STEC) from cattle in Korea between 2010 and 2011. J Vet Sci. 2014;15(3):369–379. doi: 10.4142/jvs.2014.15.3.369 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref073] 73.Kennedy CA, Fanning S, Karczmarczyk M, et al. Characterizing the Multidrug Resistance of non-O157 Shiga Toxin-Producing Escherichia coli Isolates from Cattle Farms and Abattoirs. Microb Drug Resist. 2017;23(6):781–790. doi: 10.1089/mdr.2016.0082 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref074] 74.Ratledge C, Dover LG. Iron Metabolism in Pathogenic Bacteria Annual Review of Microbiology 2000. 54:1, 881–941 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref075] 75.Fricke WF, McDermott PF, Mammel MK, et al. Antimicrobial resistance-conferring plasmids with similarity to virulence plasmids from avian pathogenic Escherichia coli strains in Salmonella enterica serovar Kentucky isolates from poultry. Appl Environ Microbiol. 2009;75(18):5963–5971. doi: 10.1128/AEM.00786-09 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref076] 76.Khajanchi BK, Hasan NA, Choi SY, et al. Comparative genomic analysis and characterization of incompatibility group FIB plasmid encoded virulence factors of Salmonella enterica isolated from food sources. BMC Genomics. 2017;18(1):570. Published 2017 Aug 2. doi: 10.1186/s12864-017-3954-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref077] 77.Miltenburg GA, Wensing T, van de Broek J, Mevius DJ, Breukink HJ. Effects of different iron contents in the milk replacer on the development of iron deficiency anaemia in veal calves. Vet Q. 1992;14(1):18–21. doi: 10.1080/01652176.1992.9694320 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref078] 78.Allan J, Plate P, Van Winden S. The Effect of Iron Dextran Injection on Daily Weight Gain and Haemoglobin Values in Whole Milk Fed Calves. Animals (Basel). 2020;10(5):853. Published 2020 May 14. doi: 10.3390/ani10050853 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0265445.ref079] 79.Harwalkar A, Gupta S, Rao A, Srinivasa H. Lower prevalence of hlyD, papC and cnf-1 genes in ciprofloxacin-resistant uropathogenic Escherichia coli than their susceptible counterparts isolated from southern India. J Infect Public Health. 2014;7(5):413–419. doi: 10.1016/j.jiph.2014.04.002 [DOI] [PubMed] [Google Scholar]

[pone.0265445.ref080] 80.Piatti G, Mannini A, Balistreri M, Schito AM. Virulence factors in urinary Escherichia coli strains: phylogenetic background and quinolone and fluoroquinolone resistance. J Clin Microbiol. 2008;46(2):480–487. doi: 10.1128/JCM.01488-07 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Virulome and genome analyses identify associations between antimicrobial resistance genes and virulence factors in highly drug-resistant Escherichia coli isolated from veal calves

Bradd J Haley

Seon Woo Kim

Serajus Salaheen

Ernest Hovingh

Jo Ann S Van Kessel

Roles

Abstract

Introduction

Materials and methods

Results

Fig 1. Maximum likelihood tree showing the inferred phylogeny of strains analyzed in this study (in bold with “ARS-CC” prefix) along with previously characterized strains from each phylogroup, cryptic clade, and E. albertii, and E. fergusonii, with 1000 bootstrap replicates.

Table 1. Characteristics of MDR and susceptible strains.

Fig 2. Co-occurrence matrix of antimicrobial, metal, and biocide resistance genes that show a negative, random, and positive cooccurrence with each other.

Fig 3. Virulence factors detected in MDR and susceptible isolates.

Table 2. ARGs, BRGs, MRGs, and VFs that co-occur with plasmid replicons identified in the study isolates.

Fig 4. Nonmetric multidimensional scaling (NMDS) analysis of virulence gene presence/absence (Jaccard distance) with isolates grouped based on resistance gene presence (stress = 0.19) (ANOSIM R = 0.1689, P = 0.001).

Fig 5. A network analysis showing the presence of resistance genes and virulence factors in all the isolates (both MDR and susceptible).

Discussion

High genotypic diversity with an observed predominant phylogroup and genotype

ExPEC-associated sequence types repeatedly identified among MDR isolates

ARGs, MRGs, BRGs, and their co-occurrence

Virulome differences and VFs associated with MDR and susceptible genotypes

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Virulome and genome analyses identify associations between antimicrobial resistance genes and virulence factors in highly drug-resistant Escherichia coli isolated from veal calves

Bradd J Haley

Seon Woo Kim

Serajus Salaheen

Ernest Hovingh

Jo Ann S Van Kessel

Roles

Abstract

Introduction

Materials and methods

Results

Fig 1. Maximum likelihood tree showing the inferred phylogeny of strains analyzed in this study (in bold with “ARS-CC” prefix) along with previously characterized strains from each phylogroup, cryptic clade, and E. albertii, and E. fergusonii, with 1000 bootstrap replicates.

Table 1. Characteristics of MDR and susceptible strains.

Fig 2. Co-occurrence matrix of antimicrobial, metal, and biocide resistance genes that show a negative, random, and positive cooccurrence with each other.

Fig 3. Virulence factors detected in MDR and susceptible isolates.

Table 2. ARGs, BRGs, MRGs, and VFs that co-occur with plasmid replicons identified in the study isolates.

Fig 4. Nonmetric multidimensional scaling (NMDS) analysis of virulence gene presence/absence (Jaccard distance) with isolates grouped based on resistance gene presence (stress = 0.19) (ANOSIM R = 0.1689, P = 0.001).

Fig 5. A network analysis showing the presence of resistance genes and virulence factors in all the isolates (both MDR and susceptible).

Discussion

High genotypic diversity with an observed predominant phylogroup and genotype

ExPEC-associated sequence types repeatedly identified among MDR isolates

ARGs, MRGs, BRGs, and their co-occurrence

Virulome differences and VFs associated with MDR and susceptible genotypes

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases