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Pathogens 2024, 13, 300. https://doi.org/10.3390/pathogens13040300 www.mdpi.com/journal/pathogens
Article
Antimicrobial Resistance Genes in Respiratory Bacteria from
Weaned Dairy Heifers
Sarah Depenbrock 1,*, Cory Schlesener 2, Sharif Aly 3, Deniece Williams 3, Wagdy ElAshmawy 3,4,
Gary McArthur 5, Kristin Clothier 6, John Wenz 7, Heather Fri 6, Munashe Chigerwe 1 and Bart Weimer 2,*
1 Department of Veterinary Medicine and Epidemiology, School of Veterinary Medicine,
University of California Davis, Davis, CA 95616, USA
2 Department of Population Health and Reproduction, 100K Pathogen Genome Project, School of Veterinary
Medicine, University of California Davis, Davis, CA 95616, USA; cschlesener@ucdavis.edu
3 Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California
Davis, Tulare, CA 93274, USA
4 Department of Internal Medicine and Infectious Diseases, Faculty of Veterinary Medicine, Cairo University,
Giza 12613, Egypt
5 Swinging Udders Veterinarian Services, Galt, CA 95632, USA
6 California Animal Health and Food Safety Laboratory, School of Veterinary Medicine,
University of California Davis, Davis, CA 95616, USA
7 Field Disease Investigation Unit, Department of Veterinary Clinical Sciences, College of Veterinary
Medicine, Washington State University, Pullman, WA 99163, USA
* Correspondence: smdepenbrock@ucdavis.edu (S.D.); bcweimer@ucdavis.edu (B.W.)
Abstract: Bovine respiratory disease (BRD) is the leading cause of mortality and antimicrobial drug
(AMD) use in weaned dairy heifers. Limited information is available regarding antimicrobial
resistance (AMR) in respiratory bacteria in this population. This study determined AMR gene
presence in 326 respiratory isolates (Pasteurella multocida, Mannheimia haemolytica, and Histophilus
somni) from weaned dairy heifers using whole genome sequencing. Concordance between AMR
genotype and phenotype was determined. Twenty-six AMR genes for 8 broad classes of AMD were
identified. The most prevalent, medically important AMD classes used in calf rearing, to which
these genes predict AMR among study isolates were tetracycline (95%), aminoglycoside (94%),
sulfonamide (94%), beta-lactam (77%), phenicol (50%), and macrolide (44%). The co-occurrence of
AMR genes within an isolate was common; the largest cluster of gene co-occurrence encodes AMR
to phenicol, macrolide, elfamycin, β-lactam (cephalosporin, penam cephamycin), aminoglycoside,
tetracycline, and sulfonamide class AMD. Concordance between genotype and phenotype varied
(Mahew’s Correlation Coefficient ranged from −0.57 to 1) by bacterial species, gene, and AMD
tested, and was particularly poor for fluoroquinolones (no AMR genes detected) and ceftiofur (no
phenotypic AMR classified while AMR genes present). These findings suggest a high genetic
potential for AMR in weaned dairy heifers; preventing BRD and decreasing AMD reliance may be
important in this population.
Keywords: Mannheimia; Pasteurella; Histophilus; bovine; airway; fluoroquinolone; ceftiofur;
macrolide; tetracycline
1. Introduction
Dairy heifer rearing is a significant part of California’s USD 10 billion dairy industry
[1]. Bovine respiratory disease (BRD) is the most common cause of mortality and
indication for antimicrobial drug (AMD) use in weaned dairy heifers [2]. Antimicrobial
resistance is highly prevalent in respiratory bacteria from weaned dairy heifers in
California [3]; however, the genomic basis for AMR in respiratory isolates specifically
from weaned dairy heifers has not been described. Weaned dairy heifers represent a
Citation: Depenbrock, S.;
Schlesener, C.; Aly, S.; Williams, D.;
ElAshmawy, W.; McArthur, G.;
Clothier, K.; Wenz, J.; Fri, H.;
Chigerwe, M.; et al. Antimicrobial
Resistance Genes in Respiratory
Bacteria from Weaned Dairy Heifers.
Pathogens 2024, 13, 300. hps://
doi.org/10.3390/pathogens13040300
Academic Editor: Jörg Jores
Received: 1 March 2024
Revised: 22 March 2024
Accepted: 27 March 2024
Published: 3 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Swierland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Aribution (CC BY) license
(hps://creativecommons.org/license
s/by/4.0/).
Pathogens 2024, 13, 300 2 of 21
unique production group, compared to pre-weaned calves or adult cale, in which to
study BRD and AMR. This population has unique management conditions that may
influence BRD and AMR; they have recently been weaned off milk feeding, which is
stressful [4,5], and many have received prior treatment with AMD during the pre-weaned
period for BRD or diarrhea [3,6–10], and have been moved to group housing where they
may be comingled with calves from other farms, which represents stress and a risk for
pathogen and AMR spread [3,11]. Antimicrobial resistance (AMR) in food animals is an
animal health, welfare, and One Health concern [12–15].
Although the respiratory pasteurellaceae of cale are not generally considered
zoonotic pathogens in humans, AMR in respiratory bacteria of cale represents many One
Health risks. The use of AMD for the treatment of BRD affects other bacterial populations
in the animal, including enteric zoonotic pathogens. The transfer of genetic elements that
encode AMR between respiratory and enteric bacteria has been documented in cale [16].
The CDC reports that more than 2.8 million AMR infections occur in the United States
annually and that more than 35,000 people die as a result; multiple pathogens listed as
AMR threats by the CDC are enteric pathogens, some of which are zoonotic such as
Enterobacteriaceae and Salmonella spp. [17]. Studies around the globe have documented
the transfer of AMR bacteria between cale or other livestock species and many different
human populations [18–22]. Transfer of AMR between livestock operations and the
environment, via multiple different modes of transfer, has likewise been documented [23–
25]. Additionally, the spread of AMR in respiratory pathogens between domestic and wild
ruminants has likewise been documented [26].
The genomic basis of AMR is clinically important because it provides information
about resistance mechanisms across groups of organisms, for many classes of AMD, in a
single analysis. Whole genome sequencing (WGS) is one method by which AMR genes
can be identified and can be used to determine total genetic diversity and molecular
epidemiology and evaluate mechanistic insights into AMR. The advantage of whole
genome sequencing compared to traditional in vitro susceptibility testing methods that
determine AMR phenotype is that WGS provides data rapidly and examines all known
AMR resistance genes in a single test. In contrast, culture and susceptibility testing
methods are applied to a specific panel of AMD and assess a narrow range of pre-
determined AMD dilutions. The increasing availability of WGS makes it a clinically useful
and relevant methodology for sequence-based assessment of AMR. Workflows that
provide reports for clinical consideration can be constructed [27–29], and this approach is
becoming routine for AMR analyses in environmental samples [30]. The use of AMR gene
presence to predict resistance phenotype has not been widely validated in multiple
bacteria and within a genus [28]. Genotype predictions of phenotype are emerging with
variable results; even within a single organism, the concordance of AMR genotype with
phenotypic is variable [31–33]. Studies in Salmonella spp. and Staphylococcus aureus
reported some agreement between AMR genotypic and phenotype [31,34,35]. Substantial
discrepancies were identified between AMR phenotype and genotype in Mannheimia
haemolytica respiratory isolates from beef cale, depending on what drug was being
analyzed [36]. Similarly, respiratory isolates M. haemolytica, Pasteurella multocida,
Histophilus somni, and Mycoplasma bovis from pre-weaned dairy calves and feedlot cale
demonstrated discordance between genotype and phenotype based on the drug tested,
bacterial species, and time point of sampling [37]. The variation among genetic AMR
determinants in respiratory isolates between different aspects of the cale industry, and
differences in WGS within species of these organisms, suggest detailed information is
needed to validate the use of WGS for predicting AMR phenotype in bacteria associated
with BRD.
We hypothesized that AMR genes are highly prevalent and that AMR genotype and
phenotype are well correlated in a study population of respiratory bacterial isolates from
weaned dairy heifers in California. The objectives of this study were to (1) determine the
AMR gene presence in respiratory bacterial isolates using WGS, and (2) determine the
Pathogens 2024, 13, 300 3 of 21
concordance between AMR genotype and AMR phenotype (using broth microdilution
antimicrobial susceptibility testing) in the same isolates.
2. Materials and Methods
2.1. Study Design
A cross-sectional study was performed between June 2019 and February 2020 on a
convenience sample of 6 dairy calf-rearing facilities across California’s Central Valley. A
total of 360 heifers were sampled by deep nasopharyngeal swab (DNPS), as previously
described [3,38]. Bacterial samples were submied to the California Animal Health and
Food Safety laboratory for culture and susceptibility testing of P. multocida, M. haemolytica,
and H. somni isolates. The culture and susceptibility results were previously published in
a study reporting AMR prevalence in the study population [3]. Banked isolates were then
evaluated by WGS for this study and the results of WGS of P. multocida, M. haemolytica,
and H. somni isolates were compared to the culture and susceptibility results.
2.2. Animals and Sample Sources
Original sample collection for this study population has previously been described
[3]. Briefly, 360 heifers from 6 calf-rearing facilities were enrolled for sampling. In total, 3
of 6 calf-rearing facilities were multisource (ranging from 8 to 45 farm sources), 2 dairies
raised their calves on site, and 1 dairy sent calves 1–7 d of age to a multisource calf-rearing
facility (number of sources not reported) until weaning and weaned heifers returned to
the source farm where they were sampled. This study was approved by the UC Davis
Institutional Animal Care and Use Commiee (protocol #20114); informed consent from
herd management was obtained verbally prior to commencing study activities. Selection
criteria included weaned dairy heifers in group pens (>3 months old) comingled for at
least 2 weeks prior to sampling and less than 6 months old. Equal numbers of heifers with
and without clinical signs of BRD based on a validated BRD Scoring system for weaned
calves [39] were included. Samples were collected over two seasonal time points to include
warmer (June–early October) and cooler (January–February) seasons. Respiratory
bacterial isolates were collected by DNPS (double-guarded culture swabs, Reproduction
Provisions LLC, Walworth, WI, USA), as previously described [38]. The DNPS were
placed in Amies with charcoal transport media (CultureSwab Plus, BD BBL™, COPAN
Italia 114 SpA, Brescia, Italy). Samples were stored in a cooler with wet ice during
sampling and refrigerated at ~4 °C up to 48 h before submission. Selective culture and
antimicrobial susceptibility testing for P. multocida, M. haemolytica, and H. somni was
performed. A total of 361 isolates, consisting of 145, 119, and 97 P. multocida, M.
haemolytica, and H. somni isolates, respectively, were collected. A loopful of pure growth
was transferred from the MIC purity plate to the cryovial with treated beads and
cryopreservative solution according to manufacturer instructions (Pro-Lab Diagnostics
Microbank Cryovial Storage Systems) and stored at −70–80 °C (Panasonic VIP Plus) until
WGS analysis.
2.3. Bacterial Culture and Susceptibility Testing
The methods for culture and susceptibility testing in this study population have
previously been described [3], and are included in Supplementary Materials S1. Briefly,
selective isolation was performed for P. multocida, M. haemolytica, and H. somni, and
bacterial identification was confirmed by matrix-assisted laser desorption–ionization–
time-of-flight (MALDI-TOF) mass spectrometry [40]. Antimicrobial susceptibility testing
was performed using broth microdilution in BO-PO plates. Antimicrobial drugs selected
for analysis in this study were based on the availability of the 2018 Clinical Laboratory
Standard Institute (CLSI) breakpoints [41] that could be meaningfully interpreted with the
AMD solution concentrations used for the respiratory isolates in the study and included
tetracycline, tilmicosin, tildipirosin, gamithromycin, enrofloxacin, danofloxacin,
Pathogens 2024, 13, 300 4 of 21
florfenicol, spectinomycin, tulathromycin, penicillin, and ceftiofur. The AMD for which a
MIC was obtained through broth microdilution but no applicable breakpoint was
available included ampicillin, clindamycin, neomycin, sulfadimethoxine, tiamulin,
trimethoprim–sulfamethoxazole, and tylosin; these AMD were analyzed separately. For
the purposes of AMD class comparisons and MDR determination, when an isolate was
classified as susceptible to all AMD tested in a class, the isolate was categorized as
susceptible to that class; when an isolate was classified as resistant or intermediate to any
drug in the class, the isolate was classified as resistant.
2.4. Whole Genome Sequencing (WGS)
A single bead from banked bacterial isolates was aseptically transferred to a 5% sheep
blood agar plate (Hardy Diagnostics, Santa Maria, CA, USA). The bead was streaked
across the primary quadrant and then removed. The plate was struck for single colony
isolation with subsequent incubation aerobically at 37 °C (+2 °C) with 5% CO2 for 18–24 h.
A loopful of pure growth (~10 µL) was transferred to Meuller–Hinton broth and incubated
aerobically overnight at 37 °C for 18–24 h. Approximately 1 mL of suspension was
transferred to a microcentrifuge tube in duplicate and the bacteria were pelleted by
centrifugation at 16,000× g for 5 min. The supernatant was aspirated off and pellets were
frozen at −80 °C until DNA was extracted, as described previously [42–45]. Briefly, the cell
pellet was lysed with a hydrolytic enzyme cocktail for 30 min with DNA isolation
completed using the Wizard Genomic DNA Purification kit (Promega, Madison, WI,
USA), according to instructions for gram-negative bacteria. Duplicate bacterial pellets
were combined with 600 µL Nuclei Lysis Solution and processed as a single sample. The
final DNA pellet was rehydrated in 100 µL of 10 mM Tris-HCl pH 8.0. Genomic DNA
quality was evaluated as described previously [46]. Briefly, purity for protein and organic
contamination was conducted using a Nanodrop One UV-Vis Spectrophotometer
(ThermoScientific, Waltham, MA, USA) using the A260/280 and the A260/230 of >1.5.
Genomic DNA integrity was evaluated by genomic DNA TapeStation (Agilent 4200, Santa
Clara, CA, USA) [46,47]. The DNA was stored at −20 °C until used for WGS.
The 361 bacterial isolates were prepared for WGS with the Illumina 2500 (San Diego,
CA, USA), using the paired-end 150 method as previously described [27,42,48–51]. Briefly,
high-quality gDNA was used to construct sequencing libraries with 400–550 bp inserts,
followed by size selection to an average of 450 bp and sequenced to 50× depth per genome.
Raw sequence information is available at the 100K Pathogen Genome Bioproject
(PRJNA203445).
2.5. Informatic Tool Usage
Program packages and their dependencies were managed with Conda (Miniconda
Software Distribution, Anaconda Inc., available at
hps://docs.conda.io/projects/miniconda, versions 4.8 to 23.10). The programs listed in
this publication include the repository, version number, and package ID that are used on
anaconda.org. Data organization was carried out with Python3 using Pandas data frames
(version: conda-forge pandas=2.0.1=py311hab14417_1). Specific use of each tool is listed
by functional need.
2.6. Genome Assembly and Quality Control
Genomic sequence data was processed with Trimmomatic {Ref. [52] Method1}
(version: bioconda trimmomatic=0.39=1), using seings “trimmomatic PE {input} {output}
ILLUMINACLIP:{adapters}:2:40:15 LEADING:2 TRAILING:2 SLIDINGWINDOW:4:15
MINLEN:50”, to remove low-quality sequence and sequencing adapters. Sequence data
quality was reviewed with FastQC {Ref. [53] Method2} (version: bioconda
fastqc=0.11.9=0). Genome assemblies were constructed with Shovill {Ref. [54]: Method3}
(version: bioconda shovill=1.0.4=0) using the default options with the SPAdes assembler.
Pathogens 2024, 13, 300 5 of 21
Genome assembly quality was reviewed with CheckM {Ref. [55]: Method4} (version:
bioconda checkm-genome=1.1.2=py_1), using the “lineage_wf” workflow. Each
assembly’s depth of coverage was measured with Mosdepth {Ref. [56]: Method5} (version:
bioconda mosdepth=0.3.1=h4dc83_1) using “fast-mode”. Quality control cutoffs for
inclusion in the analyses were CheckM: >95% estimated completeness, <5% estimated
contamination, within range 2–2.75 Mbases for assembly size, <300 contigs; Mosdepth:
>20× mean coverage.
2.7. Investigating Contamination
Genomes were explored for contamination to identify species of the contaminants
[57,58]. Trimmed sequence reads were assigned taxonomic identities with Kraken2 {Ref.
[59]: Method6} (version: bioconda kraken2=2.0.8_beta=pl526hc9558a2_2), using standard
seings (k-mer size = 35). Taxonomic assignment used standard Kraken2 database build
of NCBI RefSeq genomes, following the Kraken2 manual protocol
(hps://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown,
accessed on 5 May 2021), incorporating the categories archaea, bacteria, viral, fungi,
protozoa, and UniVec Core (built/downloaded 5 May 2021). Taxonomically assigned
reads were statistically proportioned to the respective taxa at the species level with
Bracken {Ref. [60]: Method7} (version: bioconda bracken=2.6.1=py38hed8969a_0). Braken
species database was made from the Kraken2 database by standard protocol, using the
parameters of k-mer size = 35 and read size = 150. Resulting assignments were organized
into percentage abundance of sequence reads for each identified species per sample.
2.8. Identifying AMR Genes
Genome assemblies were scanned for AMR genes using the Resistance Gene
Identifier (RGI) software (version: bioconda rgi=5.1.1=py_0) with the Comprehensive
Antibiotic Resistance Database (CARD) (version: 3.1.1 released 29 January 2021) {Ref. [61]:
Method8}. From CARD, identified genes’ information regarding types/classes of drug
resistance conferred and mechanism of action were obtained.
2.9. AMR Gene Distribution Graphs
The prevalence of AMR genes was plotted, by species, in a heat map generated by
Python script using the heat map function of the seaborn package (version: conda-forge
seaborn=0.12.2=hd8ed1ab_0), and category labels were created with the “UpSet” function of
the upsetplot package (version: conda-forge upsetplot=0.8.0=pyhd8ed1ab_0). Gene
distribution by species was also plotted as a Venn diagram using the “venn3” function of
the matplotlib Venn package (version: conda-forge matplotlib-venn=0.11.9=pyhd8ed1ab_0).
Gene pairwise co-occurrence in genomes was presented as a proportion of samples with
both genes relative to total samples with either or both genes (a Jaccard index). The pairwise
gene co-occurrence matrix was clustered by the “squareform” and “linkage” function
(method=‘single’) of the scipy.spatial.distance and scipy.cluster.hierarchy packages,
respectively (version: conda-forge scipy=1.10.1=py311h939689b_1) and graphed with the
clustermap function of the seaborn package.
2.10. Isolate Phenotype and AMR Genotype Classification
Standardized drug class categories were set for comparisons (Table S1). The class of
beta-lactams was split into the sub-classes cephalosporin and penicillin for some
comparisons where differentiation is relevant for clinical interpretation. The class
macrolide+ combines the different chemical drug classes macrolide, lincosamide, and
streptogramin as they are affected by many of the same resistance mechanisms. The AMR
phenotype was determined by classification of MIC values as resistant or sensitive using
the 2018 version of the CLSI breakpoints that were clinically applicable to cale
respiratory isolates for the respective drugs [3]. Briefly, isolates were classified as having
Pathogens 2024, 13, 300 6 of 21
a phenotype of ‘sensitive’ if their MIC value was within the CLSI range determined as
susceptible, and classified as having a phenotype of resistant if within the CLSI range for
resistant or intermediate. For drugs without a clinically applicable breakpoint, a similar
binary phenotype interpretation was made as ‘low’ or ‘high’. For drugs tested over a range
of concentrations, MIC value distributions were assessed to find distribution breakpoints
of ‘low’ and ‘high’ by Jenks natural breaks classification [62]. For AMD that were only
tested at one concentration, the phenotype ‘low’ was assigned if the isolate did not grow
at the test drug concentration, or ‘high’ if it grew.
Isolates were classified as genotype resistant to each class of AMD for which AMR genes
were identified by CARD in the isolate, and sensitive when no AMR genes were identified.
2.11. Statistical Analysis
Concordance between AMR phenotype and genotype was assessed. When
comparing genotype predictive to phenotype, results were generated in the following
categories: false positive [genotype(+) & phenotype(−)]; true positive [genotype(+) &
phenotype(+)]; true negative [genotype(−) & phenotype(−)]; and false negative
[genotype(−) & phenotype(+)]. The ability of AMR genes to predict AMR phenotypes was
assessed from the confusion matrix style framework using sensitivity and specificity.
Comparisons were made individually for each drug tested against each gene present
for the respective drug class. Comparisons were also made collectively by drug class,
comparing resistance to any drug in the class to the presence of any gene that confers AMR
to that class. Comparisons were made for bacterial species as well as all bacteria combined.
Statistical comparisons with a Python script used Panda’s data frames to sort and
organize respective comparisons. Mahew’s correlation coefficient (MCC) was used to
estimate the correlation between AMR genotype and phenotype. The MCC values range
from −1 to 1, where positive correlation estimates represent increasingly agreeing
genotype and phenotype AMR status classification, with +1 being perfectly in agreement,
negative correlation estimates represent increasingly opposing genotype and phenotype
AMR status classification, with −1 being perfectly opposing, while 0 implies perfect
random genotype and phenotype pair classification. To test whether a specific MCC was
statistically significant, McNemar’s test was used to account for the dependency between
genotype and phenotype status observed from the same isolate. A McNemar’s exact test
was used if the discordant cell counts (sum of false positives and false negatives) of a
specific genotype–phenotype combination were sparse (n < 24). A 5% level of significance
was observed to estimate significant correlation coefficients. Analyses were conducted
using the SciPy package (version: conda-forge scipy=1.10.1=py311h939689b_1) to
determine the significance of the associations between susceptibility testing and AMR
gene ID from WGS. Additional statistical values were calculated by Python script with the
standard formulas. McNemar’s standard and exact tests were conducted using the
“contingency_tables.mcnemar()” function of the Statsmodels package (version: conda-
forge statsmodels=0.14.1=py311hc9a392d_0). Additional statistical values were calculated
by Python script with the standard formulas as listed in the column headings of Table S2.
Comparison of multidrug resistance between phenotype and genotype was carried
out at a broad scale; the comparison was restricted to the 6 AMD classes that contain drugs
tested with clinically relevant MIC breakpoints and were assessable from RGI/CARD
genome scans. The set of classes assessed includes aminoglycosides, beta-lactams,
fluoroquinolones, macrolides, phenicols, and tetracyclines. The cumulative count of drug-
resistant classes by AMR genes increased if a gene was present for a respective class, and
likewise for the cumulative count by MIC phenotype, increasing if an isolate was
determined resistant for any drug in the respective class. Graphical comparisons were
produced using the “histplot” function of the Seaborn package.
Pathogens 2024, 13, 300 7 of 21
3. Results
A total of 326 genomes and corresponding assemblies passed quality control metrics
and were used for analysis, including 130 P. multocida, 106 M. haemolytica, and 90 H. somni.
Thirty-five samples were removed for quality control including some isolates where the
bacterial identification (ID) provided by MALDI-TOF was in disagreement with the
bacterial IDs determined using WGS; in some cases, multiple organisms were identified
in a single sample (Tables S3 and S4).
There were 26 AMR genes, encoding resistance to 8 broad classes of AMD, identified
from 326 isolates (Figure 1). The top nine most prevalent individual AMR genes across all three
bacterial species, the percent of samples containing the gene, and the AMD class to which they
predict AMR conference were as follows: tetH (95%, tetracycline), sul2 (94%, sulfonamide),
aph3′-Ia (94%, aminoglycoside), aph3”-Ib (94%, aminoglycoside), aph6-Id (84%,
aminoglycoside), PBP3(ftsI) (72%, beta-lactam), EF-Tu(tuf (72%, elfamycin), floR (45%,
phenicol), and erm42 (44%, macrolide/lincosamide/streptogramin). When the identified AMR
genes are sorted by class of AMD to which they predict conference of AMR, the most prevalent
classes to which AMR would be conferred were as follows: tetracycline (95%), aminoglycoside
(94%), sulfonamide (94%), beta-lactam (77%), elfamycin (72%), phenicol (50%),
macrolide/lincosamide/streptogramin (44%), and diaminopyrimidine (10%) (Figure 1).
Figure 1. Prevalence of AMR genes identified by WGS of 326 respiratory isolates of P. multocida, M.
haemolytica, and H. somni from weaned dairy heifers. The AMR gene is on the Y axis, and the
prevalence of each gene is listed by bacterial species on the X axis; heat map with Likert scale
demonstrates relative prevalence of each gene where darker blue corresponds to greater prevalence.
Pathogens 2024, 13, 300 8 of 21
The corresponding dot plot demonstrates which classes of AMD each gene is predicted to confer
AMR (right dot) and the line links to the associated mechanism by which AMR is conferred (left
dot). N = 130, 106, 90 P. multocida, M. haemoltyica, H. somni isolates, respectively.
Multiple AMR genes co-occurred within isolates from each genus (Figures 2 and 3).
The AMR genes tetH, aph3”-Ib, aph3′-Ia, and sul2 occur together in most isolates (Figure
2). The largest cluster of gene co-occurrence included floR, erm42, EF-Tu(tuf), PBP3(ftsI),
aph6-Id, aph3”-Ib, aph3”-Ia, tetH, and sul2 (Figure 2). These genes predict AMR to
phenicol, macrolide, elfamycin, β-lactam (cephalosporin, penam cephamycin),
aminoglycoside (all three aph genes listed), tetracycline, and sulfonamide class AMD,
respectively.
Figure 2. Jaccard similarity index heat map of co-occurrence of 26 AMR genes identified across
respiratory bacterial isolates P. multocida, M. haemoltyica, and H. somni from weaned dairy heifers.
Darker blue on the Likert scale indicates more frequent co-occurrence. N = 130, 106, 90 P. multocida,
M. haemoltyica, H. somni isolates, respectively.
Pathogens 2024, 13, 300 9 of 21
Figure 3. Venn diagram demonstrating co-occurrence of 26 AMR genes identified in respiratory
bacterial isolates from weaned dairy heifers separated by bacterial species (P. multocida, M.
haemolytica, H. somni). Each species of respiratory isolate is represented by a different circle. Co-
occurrence of AMR genes between different species of bacterial respiratory isolate is represented
where circles overlap. N = 130, 106, 90 P. multocida, M. haemolytica, H. somni isolates, respectively.
The correlation between AMR phenotype, as measured by susceptibility testing, and
AMR gene ID was determined using Mahew’s correlation coefficient (MCC). The overall
correlation between AMR gene ID and AMR phenotype of study isolates was low (median
MCC = 0.15) when measured across all 326 isolates, between all AMR genes identified and
the 11 AMD drugs analyzed for phenotype using susceptibility testing. The MCC ranged
from −0.57 to 1 depending on the drug–gene–bacterial species combination analyzed. The
likelihood that these correlations occurred by chance was tested using McNemar’s test.
When analyzed by each isolate (P. multocida, M. haemolytica, or H. somni) and each AMR
gene independently, the MCC was below 0.7 (the cutoff for excellent correlation) for most
drug–gene–bacterial species combinations analyzed. The MCC was greater than 0.7 with
a McNemar p-value < 0.5 for the following limited relationships representing lack of
susceptibility (AMR phenotype): the bla-ROB-1 gene and penicillin AMR phenotype
across all three species, and in M. haemolytica individually (MCC = 0.81, 0.84, respectively),
the erm42 gene and tulathromycin AMR phenotype in M. haemolytica (MCC = 0.83), and
the floR gene and florfenicol AMR measured across all three species (MCC = 0.80). It is
important to note that when there are no discordant pairs (NDP), the MCC is perfect at 1
Pathogens 2024, 13, 300 10 of 21
or −1; however, the 0 values in the McNemar equation created by NDP result in a P value
of 1, and thus the significance of the correlation is inestimable. There were five
comparisons where the MCC = 1, including between the floR gene and florfenicol AMR in
both P. multocida and M. haemolytica, the erm42 gene and tildipirosin AMR in P. multocida,
the bla-ROB-1 gene and penicillin AMR in H. somni, and the tetH gene and tetracycline
AMR in M. haemoltyica. No MCC values were calculated for ceftiofur due to a lack of
isolates classified as resistant or intermediate by MIC interpretation. The performance of
AMR gene ID for the determination of respiratory bacterial isolate resistance phenotype
is displayed in Figure 4.
Figure 4. Performance of genotype to predict AMR phenotype. Presence of AMR genes determined
by WGS compared to phenotype testing using susceptibility testing and breakpoint interpretation,
in respiratory bacterial isolates (P. multocida, M. haemolytica, and H. somni) from weaned dairy
heifers.
The strength of the association between genotype and phenotype varied widely
depending on the bacterial species, the AMD being tested, and the specific AMR gene. For
example, identification of the erm42 gene was well correlated (MCC of 0.96, 0.92, and 1,
respectively), highly sensitive (sensitivity of 0.98, 1, and 1, respectively), and specific
(specificity of 1 for all) for prediction of the AMR phenotype in P. multocida to tilmicosin,
gamithromycin, and tildipirosin; however, it was less well correlated (MCC of 0.26) and
Pathogens 2024, 13, 300 11 of 21
less sensitive (sensitivity of 0.31) for predicting phenotypic resistance to tulathromycin.
Inversely, identification of the same erm42 gene in M. haemolytica was less well correlated
(MCC of 0.4, 0.7, and 0.5, respectively) and less sensitive (sensitivity of 0.36, 0.61, and 0.42,
respectively) for predicting phenotypic AMR for tilmicosin, gamithromycin, and
tildipirosin and beer correlated (MCC of 0.83) with greater sensitivity (sensitivity of 0.76)
for the prediction of phenotypic AMR to tulathromycin. Table 1 displays the variation
observed between AMR determined by gene ID and susceptibility testing within the
macrolide sub-class, across the three bacterial species examined in this study. The MCC
values, sensitivity, specificity, and McNemar’s test results comparing genotype to
phenotype for all gene–AMD–bacterial species combinations are listed in Table S2.
Table 1. Mahew’s correlation coefficient values, sensitivity, and specificity of identifying
respiratory bacterial isolate resistance by AMR gene identification using WGS compared to CLSI
breakpoint interpretation in macrolide class AMD. Where discordant cell counts were <24,
McNemar’s exact binomial test was used, demarcated by *. Data from H. somni were excluded from
the table due to MCC values consistently below 0.25. NDP = no discordant pair; both discordant
cells (false positives and false negatives) have zero counts, rendering McNemar’s test statistic
inestimable.
Drug Isolate Species
AMR Genes
Mahew’s Correlation
Coefficient Sensitivity Specificity McNemar’s
p
Value
Tilmicosin
P. multocida
erm42 0.96 0.98 1 0.5 *
mphE 0.15 0.09 1 <0.001
msrE 0.15 0.09 1 <0.001
M. haemolytica
erm42 0.40 0.36 1 <0.001
mphE 0.20 0.11 1 <0.001
msrE 0.20 0.11 1 <0.001
Gamithromycin
P. multocida
erm42 0.92 1 0.89 0.125 *
mphE 0.17 0.10 1 <0.001
msrE 0.17 0.10 1 <0.001
M. haemolytica
erm42 0.70 0.61 1 <0.001 *
mphE 0.36 0.20 1 <0.001
msrE 0.36 0.20 1 <0.001
Tulathromycin
P. multocida
erm42 0.26 1 0.31 <0.001
mphE 0.60 0.41 1 <0.001 *
msrE 0.60 0.41 1 <0.001 *
M. haemolytica
erm42 0.83 0.76 1 0.008 *
mphE 0.42 0.24 1 <0.001
msrE 0.42 0.24 1 <0.001
Tildipirosin
P. multocida
erm42 1 1 1 NDP
mphE 0.16 0.09 1 <0.001
msrE 0.16 0.09 1 <0.001
M. haemolytica
erm42 0.50 0.42 1 <0.001
mphE 0.26 0.14 1 <0.001
msrE 0.26 0.14 1 <0.001
When each method was used to describe MDR, the MDR genotype consistently
overestimated the MDR phenotype by one class. This discrepancy is visualized in Figure
5; the blue gene ID columns of the histograms are shifted to the right. The most prevalent
paerns of AMR in P. multocida isolates were phenotypic resistance to four AMD classes
(prevalence of 0.49) and genotypic AMR to five classes (0.57); the most prevalent paerns
of AMR in M. haemolytica isolates were phenotypic and genotypic resistance to three AMD
classes (0.25 and 0.36, respectively); the most prevalent paerns of AMR in H. somni
Pathogens 2024, 13, 300 12 of 21
isolates were phenotypic and genotypic resistance to two AMD classes (0.5 and 0.51,
respectively).
Figure 5. Step diagrams demonstrating the number of AMD classes to which an isolate was
considered resistant based on MIC breakpoint interpretation or AMR gene ID. AMR determined by
phenotype is peach, and genotype is blue. The AMD tested were grouped into classes based on AMR
gene drug class-predicted resistance as follows: β-lactam (penicillin, ceftiofur), tetracycline
(tetracycline), phenicol (florfenicol), macrolide (tulathromycin, tildipirosin, tilmicosin,
gamithromycin), aminoglycoside (spectinomycin), fluoroquinolone (danofloxacin, enrofloxacin). N
= 6 AMD classes. N = 326 isolates. Diagrams represent all three isolates combined (P. multocida, M.
haemolytica, and H. somni) in panel (A), and separated by species for panels (B–D). CARD = AMR
gene ID. MIC = AMR determined by MIC breakpoint analysis.
Figure 6 depicts the performance of AMR gene ID in predicting AMR (high vs. low
MIC) against drugs with no applicable interpretation breakpoint (ampicillin, clindamycin,
neomycin, sulfadimethoxine, tiamulin, trimethoprim–sulfamethoxazole, and tylosin).
Combinations with high correlation included the following: the bla-ROB-1 gene was well
correlated with a high MIC to ampicillin in M. haemolytica (MCC = 1); aph3′-Ia and aph3”-
Ib were well correlated with high MIC to neomycin in P. multocida (MCC = 1); and the
aph3′-Ia, aph3”-Ib, aph6-Id were well correlated with a high MIC to neomycin in M.
haemolytica (MCC = 0.96). All other correlations between genotype and phenotype among
AMD without an applicable breakpoint were moderate to low (MCC < 0.7).
Pathogens 2024, 13, 300 13 of 21
Figure 6. Performance of genotype to predict AMR phenotype of isolates with no applicable CSLI
MIC breakpoint. Presence of AMR genes determined by WGS compared to phenotype testing using
susceptibility testing and Jenks natural breakpoint classification for ‘high’ vs. ‘low’ MIC, in
respiratory bacterial isolates (P. multocida, M. haemolytica, and H. somni) from weaned dairy heifers.
4. Discussion
As hypothesized, AMR genes are highly prevalent in the study population. However,
AMR genotype and phenotype are only well correlated for a subset of specific gene–
bacteria–drug combinations.
The WGS of 326 bacterial respiratory isolates from weaned dairy heifers identified 26
known AMR genes associated with resistance to 8 broad classes of AMD. Genes predicted
to confer resistance to medically important AMD commonly used in livestock including
tetracyclines, sulfonamides, β-lactams, phenicols, and macrolides were among the most
prevalent. Genes predicted to confer resistance to aminoglycosides were likewise
frequently identified. There is a voluntary ban on the extra-label use of aminoglycosides
in cale [63]; however, several products containing neomycin sulfate are currently
Pathogens 2024, 13, 300 14 of 21
approved for use in cale. Genes predicted to confer resistance to diaminopyridine class
AMD (example: trimethoprim) were also commonly identified; there are no drugs from
this class labeled for use in cale, however, these AMD are used in an extra-label manner
in some calf-rearing facilities, other veterinary species, and human medicine. Genes
predicted to confer resistance to elfamycin class AMD were likewise frequently identified;
this class of AMD has no drugs labeled for use in veterinary medicine; however, some
AMD in this class are used in research. Genes predicted to confer AMR to tetracycline,
aminoglycoside, phenicol, β-lactam, and macrolide class AMD were highly prevalent
(>50%, up to 100% prevalence) in P. multocida isolates, similarly high in M. haemolytica
isolates, and less prevalent in H. somni isolates. These Pasteurellaceae are clinically
important respiratory bacteria associated with BRD in cale, and P. multocida is
particularly clinically important in calves with pneumonia. The prevalence of AMR genes
in our study population raises concerns about the use of the medically important classes
of drugs in this population of calves. Previous investigations have linked intensive rearing
and AMD use in calves with AMR [64]. Although the analysis of AMD treatment history
with AMR gene identification was beyond the scope of this study, it is reasonable to
hypothesize that AMD use in pre-weaned dairy calves creates selective pressure on the
respiratory Pasteurellaceae that may persist into the post-weaning period. This is
problematic because respiratory disease is the primary cause of disease among weaned
dairy heifers in the US [2]; the high prevalence of AMR genes in weaned dairy heifers
reported in our study suggests that many of the drugs used for the treatment of BRD in
this production group may continue to create selective pressure for AMR, and treatment
may not be effective when these AMR genes are expressed. Additionally, there is a high
rate of co-occurrence between AMR genes, which are commonly located in mobile genetic
elements in Pasteurellaceae [65]; this could complicate efforts to decrease AMR because
selective pressure on one class of AMD may also create selective pressure for other classes
of AMD.
Genotype was variably correlated to phenotype and depended on which bacterial
species and what drugs were compared. Concordance between genotype and phenotype
was generally high for most aminoglycosides, macrolides, phenicols, and tetracycline.
Conversely, concordance between genotype and phenotype was poor for
fluoroquinolones; no fluoroquinolone AMR genes were identified in the sample
population, yet the AMR phenotype was observed. This finding is consistent with another
study of California cale respiratory isolates in which investigators reported that 20 of 64
isolates demonstrated resistance to a fluoroquinolone during susceptibility testing;
however, no fluoroquinolone AMR genes were identified [37]. This incongruence may be
due to a lack of representative single nucleotide polymorphism variants for these
organisms in the database, known genes that are not currently classified as conferring
AMR to fluoroquinolones, previously unrecognized genetic determinants of AMR acting
in the study population, or phenotypic resistance associated with metabolic processes and
not necessarily acquired genetic elements. It is also possible that other databases not
explored in this study contain additional gene references; however, it was beyond the
scope of our study to explore multiple AMR gene databases or gene-finding
methodologies or to individually validate gene identities of patrial AMR gene matches.
Concordance was also poor between genotype and phenotype for cephalosporins.
Six AMR genes identified predicted resistance to β-lactam AMD including the subclass
cephalosporins, according to CARD database metadata (PBP3(ftsI), bla-ROB1, bla-ROB2,
bla-ROB5, bla-ROB7 and bla-OXA-2); however, no isolates demonstrated a resistance
phenotype to ceftiofur, a third generation cephalosporin. Although some bla-OXA genes
confer limited resistance to penam type β-lactam AMD such as oxacillin, bla-OXA-2 is
among those bla-OXA genes listed as extended-spectrum including the conference of
resistance to cephalosporin class AMD. Some of these genes, such as PBP3(ftsI), were
highly prevalent in P. multocida and M. haemolytica isolates (100 and 99%, respectively);
the bla-ROB-1 was identified in 29% of M. haemolytica isolates. Similar findings have been
Pathogens 2024, 13, 300 15 of 21
reported from other respiratory isolates of cale. One study reported that 0 of 48
respiratory isolates from cale demonstrated phenotypic AMR to ceftiofur based on MIC
testing; however, the prevalence of the genes bla-OXA-2 and bla-ROB-1 were
approximately 100% and 82%, respectively [36]. Another study of respiratory bacterial
isolates from cale reported 0 of 64 isolates classified as resistant to ceftiofur based on MIC
testing, despite finding the bla-ROB-1 gene in study isolates [37]. This marked difference,
which appears to be present in multiple studies of bovine respiratory bacterial isolates,
may be due to AMR genes not being expressed, AMR genes improperly predicted to
confer resistance to cephalosporins, conference of resistance being limited to older
generations of cephalosporins, or due to laboratory testing conditions that do not reflect
biologic conditions in which resistance may occur. As an example of the laer, if the
breakpoint used for determining resistance from susceptibility testing is much higher than
the concentration that separates wild-type isolates from those with acquired genetic
elements that confer AMR (such as when using an epidemiologic cutoff), a phenotype of
resistance based on the clinical breakpoint may not be determined by this test. The clinical
breakpoint values for ceftiofur for BRD pathogens were set in 1988, prior to the
establishment of the CLSI veterinary antimicrobial susceptibility testing subcommiee,
and used peak total serum drug concentrations (rather than unbound drug) following
systemic administration, along with MIC90 values against BRD pathogens to set
susceptible, intermediate, and resistant breakpoints (personal communication, Mike
Sweeney and Jeff Was, 11/7/23). This legacy clinical breakpoint was based solely on
pharmacokinetic data. Current standards now integrate the microbiologic,
pharmacokinetic, and pharmacodynamic data based on the approved dosing regimens
and clinical outcome data, to establish a clinically relevant and host-disease-specific
clinical breakpoint [66–68]. An epidemiologic breakpoint for ceftiofur in bovine
respiratory bacterial isolates is not available. A 2016 study investigated the label dose of
ceftiofur crystalline-free acid (long-acting formulation of the drug) in plasma, interstitial
fluid (ISF), and pulmonary epithelial lining fluid (PELF) [69]. The mean maximum
concentration reported in plasma was 4.26 µg/mL; however, the mean maximum
concentrations in ISF and PELF reported were 0.2 and 2.09 µg/mL, respectively. An early
study of ceftiofur sodium in 1996 investigated MICs for P. multocida, M. haemolytica, and
H. somni isolates from cale; the study reports a mode MIC90 of <0.0039, 0.0015, and
<0.0019 µg/mL, respectively [70]. Using the data reported in the 2016 study, ISF
concentrations do not reach the breakpoint used to define susceptibility of 2 µg/mL, and
only in some animals does the PELF reach concentrations above 2 µg/mL [69]. Historic
MIC90 data demonstrate relatively low concentrations effective in inhibiting bacterial
growth [70]. It is possible that the CLSI breakpoint of 2 µg/mL may be so high that even
with some acquired resistance mechanisms, the Pasteurellaceae investigated may not
survive under in vitro testing of relatively high drug concentrations. When treating a
disease or applying selective pressure for AMR in vivo, drug concentrations in the tissue
or fluid compartment at the site where bacteria are located are likely more representative
of the local environment for the selection of AMR than serum values. Ceftiofur
concentrations are lower in PELF, ISF, and bronchial secretions than plasma [69,71]. Thus,
the AMR genes identified in this study and others may provide an advantage in vivo with
selective pressure from the use of AMD that reaches lower concentrations at the site of
bacterial growth than the in vitro breakpoints reflect. This mismatch between breakpoint
testing and in vivo drug concentrations at the site of interest is one possible explanation
for the discordance identified between phenotypic and genotypic AMR for ceftiofur in this
study, and other studies of bovine respiratory bacteria.
Discordance was also identified when MDR was compared between phenotype and
genotype. The MDR potential of isolates was underestimated by its phenotype compared
to the genotype. The process of WGS demonstrates all known AMR genes present in the
isolate, even if they are not currently being expressed. In a similar study on Salmonella
from small ruminants in Peru, partial concordance between the genotype and phenotype
Pathogens 2024, 13, 300 16 of 21
was identified. However, investigators also found some AMR was not predicted by the
genotype [31]. A global study of AMR in Salmonella typhoid also found a similar result
[28], suggesting that phenotype determined by MIC is not only conferred by known genes
and may include yet-to-be-discovered regulatory methods.
Various factors [72,73] are important for the expression of AMR genes, which may
not be accurately reflected in culture and susceptibility testing conditions. Susceptibility
testing may determine the in vitro susceptibility of the isolate at the time of sampling, but
does not demonstrate the full AMR potential of the isolate. The clinical implication of
these differences between testing modalities depends on the clinical question being
investigated by testing. If clinicians or investigators are more interested in the AMR
potential of an isolate or sample, WGS will likely yield more information, with the
possible current exception of fluoroquinolones. If clinicians are more interested in the
active expression of AMR in vitro, susceptibility, and MIC breakpoint interpretation
testing may be more useful. Methods to determine AMR gene expression in samples using
RNA are more recently available, however, due to some sample handling requirements,
are not widely used yet.
Statistical models for assessing the likelihood that correlations between genotype and
phenotype occurred by chance, have limitations. Comparisons with a perfect positive
correlation (MCC = 1) between genotype and phenotype for a specific combination of
AMR gene and AMR phenotype could not be assessed for significance using McNemar’s
test due to lack of discordant data (sum of genotype positive phenotype negative and
genotype negative phenotype positive is zero), rendering the test statistic inestimable.
Alternative tests of homogeneity were not appropriate for use due to the violation of their
assumption of independence, which may result in biased interpretations. Exploring novel
methods to compare genotypic and phenotypic AMR traits on a continuous scale rather
than as the presence or absence dichotomy may reduce inestimable test statistics and
beer reflect the biological complexity of drug resistance in bacterial pathogens.
The use of MALDI-TOF for bacterial ID has previously shown limited concordance
with WGS in Campylobacter isolates [40]; the present study identified a small number of
isolates with evidence of contamination from other respiratory isolates on WGS that were
identified by MALDI-TOF as pure cultures. Although this was infrequent, clinicians and
researchers should be aware of occasional contamination from morphologically similar
bacterial isolates that may not always be detected by MALDI-TOF.
The DNPS samples upper airway bacteria and, although it has been demonstrated to
correlate with bacteria found in the lower airways, it is not a direct test of lower airway
bacteria identification. This study selectively isolated P. multocida, M. haemolytica, and H.
somni from the upper airway; these three Pasteurellaceae are only a fraction of the bacteria
that make up the upper respiratory microbiome and thus do not necessarily represent all
species or resistance genes present in this biologic niche.
Prevalence data should be interpreted in light of sample size, which was broken
down by species. However, with nearly 100 isolates for each species, this appears to be an
adequate survey of the target organisms from the sample site when compared with other
studies using WGS in bovine respiratory bacteria [36,37]. Quality control allows for
population-level analyses, so it is also possible that some individual isolate genomes may
have a small probability of a gene going undetected by WGS. The use of MIC testing as
the in vitro assay used to determine phenotypic AMR also has limitations, including
limited test ranges and breakpoints that may or may not match up well with the
environment where bacteria live in the host, and the lack of breakpoints for some AMD.
Additionally, there are many factors that influence the expression of AMR genes in
bacterial isolates, and specific testing and control of all factors known to affect gene
expression was outside the scope of this study.
The high prevalence and frequent co-occurrence of AMR genes in this population
suggest that the genetic potential for AMR is high; the previous MIC testing results
demonstrate that AMR phenotypes are common. Frequent co-occurrence of AMR genes
Pathogens 2024, 13, 300 17 of 21
suggests that efforts to simply change drug use from one class to another class may not
decrease AMR as intended. Thus, efforts to prevent BRD, and to decrease reliance on AMD
may be particularly valuable in this population of animals. Technologic advances that
increase the accessibility of genomic methods of AMR identification and improvements in
phenotypic tests of AMD susceptibility may help inform AMR research and judicious
AMD use.
5. Conclusions
This study aimed to determine the prevalence of AMR genes in Pasteurellaceae
sampled from the upper airway of weaned dairy heifers and to determine the concordance
between AMR genotype and phenotype in study isolates. Genes that predict the
conference of AMR to multiple medically important AMD, which are also used in calf
rearing, were highly prevalent in the study population. Concordance between phenotype
and genotype of AMR varied depending on the AMD and species tested. Concordance
between genotype and phenotype was particularly poor for fluoroquinolone class AMD
(enrofloxacin and danofloxacin) and the drug ceftiofur. The lack of excellent concordance
suggests these tests cannot be interpreted interchangeably for all AMD in these three
respiratory Pasteurellaceae. It is unlikely that either method (WGS or susceptibility
testing) of testing for AMR in select isolates represents a perfect prediction of AMR in
vivo.
Supplementary Materials: The following supporting information can be downloaded at:
hps://www.mdpi.com/article/10.3390/pathogens13040300/s1, Material S1: Methods for Culture
and Susceptibility Testing; Table S1: Standard Drug Classes Used for AMR Gene Classification; Table
S2: Summary Statistics for AMR Gene ID vs. Culture and Susceptibility Testing for Identification of
AMR; Table S3: QC Metrics; Table S4: MALDI vs. culture ID. MALDI-TOF bacterial isolate
identification (ID) compared to genomic ID from WGS.
Author Contributions: S.D.: Conceptualization, Funding Acquisition, Investigation, Methodology,
Project Administration, Visualization, and Writing—Original Draft Preparation. C.S.: Data Curation,
Formal Analysis, Software, Visualization, and Writing—Review and Editing. S.A.:
Conceptualization, Formal Analysis, Investigation, and Writing—Review and Editing. D.W.:
Investigation, Methodology, and Resources. W.E.: Investigation, Resources, and Writing—Review
and Editing. G.M.: Investigation and Resources. K.C.: Investigation, Methodology, and Resources.
J.W.: Data Curation and Writing—Review and Editing. H.F.: Bacterial Isolation and Genomic DNA
Preparation, Generation and Curation of Phenotypic Antimicrobial Susceptibility Test Data, and
Manuscript Review. M.C.: Funding Acquisition, Methodology, and Writing—Review and Editing.
B.W.: Experimental Planning, Data Curation, Formal Analysis, Software, Visualization, and
Writing—Review and Editing. All authors have read and agreed to the published version of the
manuscript.
Funding: This study was funded by the Antimicrobial Use and Stewardship (AUS) Program of the
California Department of Food and Agriculture (CDFA).
Institutional Review Board Statement: This study was approved by the UC Davis Institutional
Animal Care and Use Commiee (protocol #20114).
Informed Consent Statement: Informed consent from herd management was obtained verbally
prior to commencing study activities.
Data Availability Statement: Genomic and antimicrobial susceptibility data used in the data
analysis of this manuscript have been made publicly available, including genome sequences
uploaded to the Sequence Read Archive, with no confidential metadata included in the study
analysis or the public data files. This study was funded by the Antimicrobial Use and Stewardship
(AUS) Program of the California Department of Food and Agriculture (CDFA) and is subject to
California Food and Agricultural Code (FAC) Sections 14400 to 14408. FAC Section 14407 requires
that data collected be held confidential to prevent the individual identification of a farm or business.
Acknowledgments: The authors would like to thank the producers who participated in the original
study from which the sample isolates were obtained; without their generous participation, this
Pathogens 2024, 13, 300 18 of 21
study would not have been possible. The authors would also like to acknowledge the California
Animal Health and Food Safety laboratory for processing all samples and isolates, performing the
original culture and susceptibility analysis, and preparing samples for WGS.
Conflicts of Interest: The authors do not believe there are any competing interests in this work. The
authors disclose that one author, G.M., has a commercial affiliation with a veterinary practice; this
veterinary practice was financially compensated for G.M.’s participation in the study. However, the
veterinary practice had no role in the study design and analysis, the decision to publish, or
manuscript preparation.
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