TYPE
Original Research
13 July 2023
10.3389/fmicb.2023.1188872
PUBLISHED
DOI
OPEN ACCESS
EDITED BY
Etinosa Igbinosa,
University of Benin, Nigeria
REVIEWED BY
Ben Jesuorsemwen Enagbonma,
North-West University, South Africa
Yanan Wang,
Henan Agricultural University, China
*CORRESPONDENCE
Sandeep Tamber
[email protected]
RECEIVED 17
March 2023
June 2023
PUBLISHED 13 July 2023
Targeted metagenomics using
bait-capture to detect antibiotic
resistance genes in retail meat and
seafood
Annika Flint 1, Ashley Cooper 2, Mary Rao 1, Kelly Weedmark 1,
Catherine Carrillo 2 and Sandeep Tamber 1*
1
Bureau of Microbial Hazards, Health Canada, Sir Frederick Banting Driveway, Ottawa, ON, Canada,
Research and Development, Ottawa Laboratory (Carling), Canadian Food Inspection Agency, Ottawa,
ON, Canada
2
ACCEPTED 28
CITATION
Flint A, Cooper A, Rao M, Weedmark K,
Carrillo C and Tamber S (2023) Targeted
metagenomics using bait-capture to detect
antibiotic resistance genes in retail meat and
seafood.
Front. Microbiol. 14:1188872.
doi: 10.3389/fmicb.2023.1188872
COPYRIGHT
© 2023 Flint, Cooper, Rao, Weedmark, Carrillo
and Tamber. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in this
journal is cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Metagenomics analysis of foods has the potential to provide comprehensive
data on the presence and prevalence of antimicrobial resistance (AMR) genes
in the microbiome of foods. However, AMR genes are generally present in low
abundance compared to other bacterial genes in the food microbiome and
consequently require multiple rounds of in-depth sequencing for detection. Here,
a metagenomics approach, using bait-capture probes targeting antimicrobial
resistance and plasmid genes, is used to characterize the resistome and
plasmidome of retail beef, chicken, oyster, shrimp, and veal enrichment cultures
(n = 15). Compared to total shotgun metagenomics, bait-capture required
approximately 40-fold fewer sequence reads to detect twice the number of AMR
gene classes, AMR gene families, and plasmid genes across all sample types. For
the detection of critically important extended spectrum beta-lactamase (ESBL)
genes the bait capture method had a higher overall positivity rate (44%) compared
to shotgun metagenomics (26%), and a culture-based method (29%). Overall, the
results support the use of bait-capture for the identification of low abundance
genes such as AMR genes from food samples.
KEYWORDS
antimicrobial resistance, gene detection, resistome, beef, chicken, oysters, shrimp, veal
1. Introduction
Antimicrobial resistance (AMR) is widespread throughout the bacterial kingdom. The
mechanisms defining resistance are diverse, as are the genes encoding them. Some antibiotic
resistance genes (AMR genes) such as those encoding transport proteins, stress response
proteins, or regulators are encoded on the bacterial chromosome (Perry et al., 2014). Their
resistance functions are secondary to their primary role in maintaining cellular homeostasis.
Other AMR genes mediate resistance through the direct modification of the antibiotic or its
cellular target. Often, these AMR genes are encoded on mobile genetic elements such as plasmids
and are termed acquired resistance genes. From a public health perspective, acquired resistance
genes are a concern because of their potential to mobilize between bacterial species (van Hoek
et al., 2011). Such movement can explain the spread of AMR genes within and between multiple
sectors including the environment, animals, and humans.
Food is an important vehicle that contributes to the global spread of AMR across borders and
sectors (McDermott et al., 2002). Among food categories, AMR genes and AMR bacteria are most
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prevalent in meats and the use of antibiotics in animal husbandry has
been linked to the presence of AMR genes and AMR bacteria in food
animals (Nekouei et al., 2018; Carson et al., 2019). A number of
national surveillance programs monitor AMR trends in food animals
and meats have demonstrated the presence of clinically significant
AMR genes in meats [for examples, see (Porter et al., 2020; Tate et al.,
2021; Cox et al., 2022)]. The impact of these genes on human health
however remains underappreciated due to the challenges associated
with their detection in foods.
When considering the genomes of food cells and their associated
microbiota, AMR genes represent a small fraction of the genetic
content (Bengtsson-Palme et al., 2017). Of those AMR genes,
acquired AMR genes are minor constituents, and are often present at
levels below the sensitivity of direct detection methods (Randall et al.,
2017; Peto et al., 2019). Successful detection of acquired AMR genes
relies on their amplification to observable levels. This can
be accomplished through enrichment, wherein the food sample is
incubated in non-selective broth which permits bacterial recovery
from injury and cell replication. Once bacterial cells concentrations
reach a critical level they can be detected through plating onto
selective agar, or via molecular assays such as PCR (Rao et al., 2021).
Whether targeting particular taxa (e.g., Escherichia coli) or resistance
phenotypes (e.g., beta-lactam resistance), culture-based approaches offer
a limited view of the diversity of foodborne antibiotic resistance genes.
Metagenomics enables detection of all the AMR genes in the microbiome
of food (the resistome) (Forbes et al., 2017). However, it is
computationally and resource intensive especially for low abundance
genes that require extensive deep sequencing (Bengtsson-Palme et al.,
2017). To avoid repeated sequencing of off-target regions, targetedsequencing approaches such as bait-capture have been described (Zhou
and Holliday, 2012). Through the selective enrichment of AMR genes by
hybridization to biotinylated probes prior to sequencing, bait-capture has
been used to detect low abundance genes in stool, fecal, and wastewater
samples at a reduced sequencing depth compared to shotgun
metagenomics (Noyes et al., 2017; Lanza et al., 2018; Guitor et al., 2019).
Here, we describe the use of a bait-capture approach to detect AMR
genes in primary enrichments derived from retail meat and seafood
samples. The results obtained by bait-capture are compared to those
obtained by shotgun metagenomics sequencing as well as a previously
published culture-based method targeting third generation
cephalosporin (3GC) resistance and extended spectrum beta-lactamase
(ESBL) genes. The feasibility of integrating a bait-capture approach to the
routine monitoring of food surveillance samples is discussed.
2. Materials and methods
2.1. Baited library
The Resfinder (Zankari et al., 2012) (downloaded February 2017),
Plasmidfinder (Carattoli et al., 2014) (downloaded February 2017), and
NCBI Resistance Gene (Bioproject PRJNA31347, downloaded January
2017) databases were used to design a custom set of biotinylated
oligonucleotide baits designed and manufactured by Arbor Biosciences
(myBaits , Ann Arbor, MI, United States). The completed set consisted
of approximately 60,000 unique baits complementary to 4,276 unique
gene sequences (Figure 1 and Supplementary File S1).
®
2.2. Food samples and primary
enrichments
Primary enrichments from retail chicken, beef, and veal were
prepared as described in Rao et al. (2021). Briefly, 25 g portions of
FIGURE 1
Treemap showing composition of bait library. The bait library consisted of 4,276 probes with 4,009 AMR genes and 267 plasmid genes. Probes were
designed using sequences taken from the NCBI AMR, ResFinder and PlasmidFinder databases as described in the Methods. Abbreviations used for AMR
gene classes: bla – beta-lactam, ag – aminoglycoside, glyco – glycopeptide, mac – macrolide, quin – quinolone, tet – tetracycline, phen – phenicol,
tmp – trimethoprim, sul – sulfonamide.
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retail chicken breast (n = 4), ground beef (n = 4), ground veal (n = 3
plus 1 technical repeat) were enriched overnight in modified tryptone
soya broth (BD Difco) at 35°C. Seafood samples (live oyster shell
stock, n = 2 and frozen shrimp, n = 2) were from on-going Health
Canada surveillance programs. Seafood homogenates were prepared
as previously described and incubated in buffered peptone water (BD
Difco) overnight at 35°C. Target bacterial species (3GC resistant
Enterobacteriaceae) were isolated after plating onto selective agar
(ESBL Chromagar, Dalynn Biologicals). Isolates were screened by
broth microdilution for resistance to cefotaxime and ceftriaxone, and
the presence of beta-lactmase genes: blaCMY-2, blaTEM, blaSHV, blaOXA,
blaCTX-M1, and blaCTX-M9 by PCR as previously described (Rao et al.,
2021). Enrichments that harbored strains testing positive for either
3GC resistance or the presence of a target beta-lactamase gene were
cryopreserved in 2X Brucella broth (Becton Dickinson) containing
30% glycerol and frozen at −20°C for short-term storage.
were used to isolate biotinylated DNA and KAPA HiFi HotStart Ready
Mix (KAPA Biosystems) was used for amplification of bead-bound
enriched libraries using NEBNext Unique Dual Index primers
(12 cycles). The baited libraries were purified using a 0.8x AMPure XP
(Beckman Coulter) cleanup, pooled in equimolar concentrations, and
pair-end sequenced on a MiSeq instrument (v3 chemistry, 2 × 300 bp)
according to manufacturer instructions (Illumina Inc.).
2.6. Bioinformatic analysis
2.6.1. Read processing, downsampling, AMR and
plasmid gene family identification
Raw Illumina reads were processed using FastP (v0.20.0) to
remove adapter and barcode sequences, correct mismatched bases in
overlaps, and filter low-quality reads (Q < 20) (Chen et al., 2018). The
processed reads were randomly down-sampled per food commodity
to the same read depth using seqtk (v1.3)1 and the sample command
with default parameters and a seed of 31. AMR and plasmid gene
families in each sample were identified using SRST2 [v0.2, (Inouye
et al., 2014)] using the custom AMR and plasmid gene database used
for bait design (default parameters). Gains achieved using bait-capture
were calculated by dividing the percent baited mapped reads by the
percent unbaited mapped reads from SRST2. Pearson correlations
were calculated using GraphPad Prism v9.
2.3. DNA extraction and microbial DNA
enrichment library preparation
Ten milliliters of thawed food sample primary enrichments were
centrifuged for 5 min at 6000 × g at 4°C and the pellet was resuspended
in 400 μL of DNA/RNA Shield (Cedarlane). DNA was extracted using
the Quick-DNA HMW MagBead Kit with RNAse A treatment
according to the manufacturer’s protocol (Zymo Research Corp.). DNA
was quantified using a Qubit fluorometer (Thermo Fisher Scientific).
Microbial DNA was enriched using the NEBNext Microbiome DNA
Enrichment kit (New England Biolabs) according to the manufacturer’s
instructions and the Collect Enriched Microbial DNA protocol.
Enriched DNA samples were purified using 1.8x AMPure XP beads
following the AMPure XP Bead Cleanup protocol (Beckman Coulter).
2.6.2. AMR gene family abundances and alpha
diversity
AMR gene family abundances were calculated using the BAM
output files from SRST2. BAM files were sorted by name using
SAMtools [v1.7, (Danecek et al., 2021)] and the SAMtools sort
function. Read counts were generated using HTSeq [v0.12.4, (Putri
et al., 2022)] using the htseq-count function and parameters
stranded = no and MapQ alignment quality of 0. Read counts were
normalized to fragments per kilobase million. Statistically significant
differences between unbaited and baited samples were determined
using a Kruskal–Wallis test with a post hoc Dunn’s test with p < 0.001
considered significant. Alpha diversities were measured using Chao1
(AMR gene family richness) and Shannon (AMR gene family
diversity) indexes using Phyloseq [v1.34, (McMurdie and Holmes,
2012)] (estimate_richness function using default parameters) and the
HTSeq normalized read abundance data. Two-way ANOVAs with
Sidak’s multiple comparison tests were used to determine statistical
differences between shotgun metagenomic and enriched samples with
p < 0.05 considered significant. Data were graphed using GraphPad
Prism v6.
2.4. Shotgun metagenomic library
preparation and sequencing (unbaited)
Samples were fragmented using 2.5 μg of input DNA in 130 μL
microTUBE snap-cap tubes using a M220 Focused Ultrasonicator
(Covaris) to an approximate average fragment length of 700 bp
according to manufacturer instructions. The DNA samples were
further purified using a 0.8x AMPure XP (Beckman Coulter) cleanup.
Illumina libraries were constructed using the NEBNext UltraII DNA
Library Prep Kit (New England Biolabs) according to the manufacturer’s
instructions using 500 ng input DNA and 5 cycles of PCR enrichment.
Equimolar libraries were pooled and sent for paired-end Illumina
NovaSeq 6000 (2 × 150 bp) sequencing at Genome Quebec.
2.6.3. AMR rarefaction
Illumina read data were randomly down-sampled in decreasing
increments using seqtk (v1.3, see text footnote 1) and the sample
command with default parameters and a seed of 31. AMR gene
families at each read depth were identified using SRST2 [v0.2, (Inouye
et al., 2014)] and the custom AMR gene database used to design the
2.5. Bait capture library preparation and
sequencing
Construction of baited libraries was performed using the custom
set of biotinylated probes and NEBNext shotgun metagenomics
libraries according to the MYbaits manufacturer’s instructions (Daicel
Arbor Biosciences). Hybridization of customized baits with 100 ng of
metagenomics library was performed at 65°C for 20 h. Dynabeads
MyOne Streptavidin C1 magnetic beads (Thermo Fisher Scientific)
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bait library (default parameters). Data were graphed using Graph Pad
Prism v6.
AMR and plasmid genes. On average 304 million paired end reads per
sample were obtained from the unbaited libraries and 9.2 million
reads per sample for the baited libraries. After subsampling to the
same read depth per food commodity, a greater proportion of baited
reads mapped to genes within the AMR and plasmid databases
(p < 0.0001, and p = 0.0003 respectively, Table 1). Compared to the
unbaited dataset, the gains achieved with bait-capture ranged from
115–2280 X for AMR genes and 89–570 X for plasmids. The highest
gains were observed in those samples with the fewest unbaited reads
mapping to the AMR database (Pearson r = −0.58, p = 0.0180). The
majority of samples in the baited dataset (12/16) had significantly
higher AMR gene fragments per kilobase million (FPKM) compared
to their unbaited counterparts (Figure 2). The remaining samples were
either trending toward an increase (Beef 4) or had a high proportion
of low abundance fragments identified (Beef 2, 3, Chicken 4).
Collectively, the increased AMR gene content of the baited dataset
resulted in approximately 2-fold more detections of AMR gene family
classes, AMR gene families, and plasmids in the baited dataset
compared to the unbaited set (Table 2). The highest increases were
observed with the seafood samples (shrimp for AMR genes, and
oysters for plasmid genes).
The number of AMR classes and AMR gene families detected
increased with increasing sequencing depth until reaching saturation
in most samples. In total 20 AMR classes and 196 AMR gene families
were identified in the 16 samples (Supplementary Table S2). The
number of reads required to reach saturation for identification of
AMR classes and gene families was variable for each commodity, but
in general 25–50-fold fewer reads were required to reach saturation
with the baited dataset (overall average = 3.2 ± 2.8 M reads for AMR
class and 6.7 ± 2.2 M reads for AMR gene family) compared to the
unbaited dataset (average = 120 ± 79 M reads for AMR class and
2.7. Statistical analysis
Unless specified otherwise, statistical calculations were carried out
using GraphPad Prism v9. Differences between the means of the
unbaited and baited datasets were calculated using paired t-tests.
Differences in means between commodities were calculated using
unpaired t-tests or one way ANOVA with the Tukey’s post hoc test as
deemed appropriate. P values less than 0.05 were considered significant.
2.8. Data availability statement
All SRAs are available in GenBank under BioProject
ID PRJNA909287.
3. Results
3.1. Sequencing requirements for unbaited
and baited libraries
To compare sequence-based detection methods for AMR genes in
retail meats, two sets of sequencing libraries were prepared from
overnight enrichment cultures of retail beef, chicken, oyster, shrimp,
and veal. One library was prepared from the total DNA fraction
isolated from each enrichment culture (unbaited) and the other from
a subset of DNA that had been subjected to bait-capture to target
TABLE 1 Sequence read mapping to AMR gene and plasmid databases.
Sample
Raw reads (M)
Subsampled read
depths (M)
Unbaited
Baited
AMR database (% reads
mapped)
Unbaited
Baited
Unbaited
Baited
Gain
Beef 1
337
14.4
282
7.1
0.22
25.4
115 X
Beef 2
291
7.5
282
7.1
0.22
36.1
164 X
Beef 3
285
8.5
282
7.1
0.06
70.4
1173 X
Beef 4
290
7.3
282
7.1
0.21
46.1
Chicken 1
328
15.1
315
8.7
0.24
Chicken 2
401
9.7
315
8.7
0.14
Chicken 3
367
11.0
315
8.7
Plasmid database (% reads
mapped)
Unbaited
Baited
Gain
0.04
4.66
117 X
0.08
24.8
310 X
0.01
4.03
403 X
220 X
0.05
28.5
570 X
67.0
279 X
0.17
17.3
102 X
60.0
429 X
0.08
17.5
219 X
0.11
52.0
473 X
0.13
15.9
122 X
Chicken 4
318
8.8
315
8.7
0.52
79.9
154 X
0.07
11.0
157 X
Oyster 1
276
11.4
275
11
0.16
40.4
253 X
0.01
0.89
89 X
Oyster 2
306
11.3
275
11
0.08
57.9
724 X
0.00
0.14
NC
Shrimp 1
300
6.9
168
3.2
0.09
24.0
267 X
0.00
6.58
NC
Shrimp 2
170
3.3
168
3.2
0.02
10.7
535 X
0.00
3.49
NC
Veal 1
274
6.9
257
6.8
0.01
26.3
2630 X
0.00
5.82
NC
Veal 1TR
360
8.0
257
6.8
0.01
28.0
2800 X
0.00
6.61
NC
Veal 2
259
81
257
6.8
0.08
60.0
750 X
0.04
17.7
443 X
Veal 3
316
9.4
257
6.8
0.04
40.3
1008 X
0.00
6.15
NC
TR, technical repeat; NC, non-calculable.
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FIGURE 2
AMR gene family abundance. Raw read counts for ground beef (A), chicken (B), seafood (C), and veal (D) unbaited and baited libraries were obtained
from HTseq. Normalized read counts were calculated as fragments per kilobase million (FPKM) for gene families identified from SRST2 analysis.
Horizontal lines represent the median, boxes indicate the inter-quantile range and whiskers represent values within 1.5 IQR of the lower and upper
quantiles. Asterisks denote statistically significant differences (p < 0.001) using a Kruskal Wallis test.
171 ± 70 M reads for AMR gene family) (Figure 3). Similarly, the range
of reads required to reach saturation was much smaller for the baited
dataset (AMR class: 0.5–10 M reads; AMR gene family: 3.2–11 M
reads) compared to the unbaited dataset (AMR class: 10–250 M reads;
AMR gene family: 20–250 M reads). Some samples, particularly from
the unbaited beef libraries, did not reach a plateau, and required
significantly more reads to approach AMR class saturation compared
to the other meat types [218 M reads (beef) vs. 120 M reads (all other
meat types), p = 0.0354]. No other relationships were observed
between the minimum number of reads required to reach saturation
and meat type or number of AMR classes/gene families identified in
each sample. No trends were observed with respect to AMR class/gene
family identification and required number of reads.
compound, and sulfonamide resistance were the next most frequently
detected classes. Some differences in AMR gene content were noted
between commodities. On average, the shrimp samples had the
highest number of detections (80), followed by veal, chicken, oyster,
and beef (52, 49, 31, and 25 AMR genes respectively). Aminoglycoside
resistance was the most abundant resistance class detected in shrimp,
veal, and chicken, whereas beta-lactam resistance was the most
frequently detected class in the beef and oyster samples (Figure 3). The
remaining AMR classes were represented in varying degrees in the
different meat commodities. Fusidic acid resistance genes were
uniquely detected in the shrimp samples and these samples also had
significantly higher rates of detection of macrolide resistance genes
(p = 0.0004). There was a notable absence of quinolone resistance
genes in chicken (Supplementary Table S1). Apart from these
observations, the differences in AMR gene content among
commodities were not significant due to variability among individual
samples within each commodity (e.g., compare Chicken 1 and 4; Veal
1 and 2, Supplementary Table S1, Figure 4).
The number of AMR gene family classes, AMR gene families,
and plasmid genes detected were higher or equivalent using the
baited dataset compared to the unbaited set (Table 2). Per sample,
3.2. Resistome comparison
Both unbaited and baited datasets yielded 20 AMR classes.
Genes for beta-lactam, aminoglycoside, and tetracycline resistance
were the most prominent with detections in every sample
(Supplementary Table S1). Genes for phenicol, quaternary ammonium
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TABLE 2 Number of AMR gene family classes, AMR gene families, and plasmids detected in unbaited and baited libraries.
Sample
AMR classes
Unbaited
AMR gene families
Plasmid genes
Baited
Unbaited
Baited
Unbaited
Baited
Beef 1
7
9
18
24
21
34
Beef 2
5
10
13
36
23
36
Beef 3
3
8
7
25
5
25
Beef 4
5
6
11
14
24
31
Chicken 1
14
14
54
71
39
48
Chicken 2
9
11
36
49
25
27
Chicken 3
9
9
41
45
32
35
Chicken 4
6
9
14
29
21
24
Oyster 1
6
10
18
37
3
17
Oyster 2
4
7
11
24
3
13
Shrimp 1
9
17
22
99
16
47
Shrimp 2
6
15
11
61
15
40
Veal 1
3
5
6
14
14
19
Veal 1TR
3
6
7
18
14
22
Veal 2
12
16
70
108
48
68
Veal 3
9
9
55
65
22
34
Total
103
162
394
719
325
520
TR, technical repeat.
an average of 10 ± 4 AMR classes (range: 5–17) and 45 ± 29 AMR
gene families (range: 14–108) were identified using the baited
libraries compared to 7 ± 3 AMR classes (p = 0.0003) (range = 3–14)
and 25 ± 20 AMR gene families (p = 0.0001) (range = 6–70) using the
unbaited libraries (Table 2). Analysis of the gene family richness
between the baited and unbaited library sets did not reveal any
significant differences. Similarly, the evenness of the gene family
distribution was equivalent between baited and unbaited libraries
(Figure 5). With respect to the ranking of the resistance classes
within meat commodities, there was good agreement between the
baited and unbaited library sets when the relative abundance was
relatively high (Supplementary Table S2). For example, among the
chicken samples, the four most abundant AMR classes were
aminoglycoside, beta-lactam, tetracycline, and sulfonamide in both
datasets. The fifth and sixth most abundant classes, however, were
quaternary ammonium compounds and macrolides (baited) vs.
glycopeptide and streptothricin (unbaited).
had the least (average = 15). Across commodities, IncF was the most
frequently detected plasmid replicon followed by Col, IncH, IncX,
and Rep 1 (Figure 6). There was a higher association of Rep and US
type plasmids in shrimp than in other commodities (Rep 1, 7, 4, 10b,
22, all p < 0.0500). In chicken, there was a higher rate of detection for
IncA/C (8 detections compared to 0, 1, 0, and 3 for beef, oyster,
shrimp, and veal, p = 0.0025) and IncB/O/K (12 detections compared
to 0, 0, 0, and 2 for beef, oyster, shrimp, and veal, p = 0.0208)
compared to other commodities.
A high correlation was observed between the number of AMR
genes and number of plasmids detected in a sample (Pearson r = 0.85,
p < 0.0001). With respect to individual replicon types, this
relationship held for Col (r = 0.67, p = 0.0042) and IncX (r = 0.59,
p = 0.0160). Similarly, twelve of the twenty AMR gene family classes
were positively correlated with the presence of plasmid genes
(Table 3).
3.3. Plasmidome comparison
3.4. AMR gene content of baited and
unbaited libraries
Using the unbaited dataset, we were able to detect 302 plasmid
genes belonging to 35 replicon types within the retail meat
preenrichment samples. The baited dataset contained 520 plasmid
genes and 37 replicon types. There was quite a bit of variation in the
number of plasmids detected within samples of the same commodity.
On average, veal and chicken had the most plasmid detections in the
unbaited dataset (24 and 25 plasmid genes respectively) and oysters
had the least (3 plasmid genes) (Table 2). In the baited set the shrimp
samples had the most plasmids detections (average = 44) and oysters
Collectively among the 16 retail meat samples, there were 733
identifications of AMR gene families. Just over half of the detections
(380, 51%) were present in both the baited and unbaited data sets
(Table 4). Three hundred thirty-nine additional detections (47% of the
total) were made using the baited dataset alone. These detections
spanned all 20 AMR classes with the majority belonging to the
aminoglycoside (107/339) and beta-lactam resistance (57/339) classes.
Among the meat commodities the shrimp samples had the least
overlap between the baited and unbaited sets (19% of identified genes)
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FIGURE 3
Read depth required to detect AMR gene family classes and AMR gene families. Rarefaction curves of unbaited (A) or baited (B) libraries for AMR gene
family classes (left) and AMR gene families (right). Reads were randomly subsampled at specific intervals and AMR classes identified by SRST2 analysis.
Sequencing depth is shown as million reads (M).
with the majority of detections occurring in the baited dataset alone
(80% of identified genes). The chicken samples had the most overlap
(68%) with only about a third of detections occurring in only the
baited data set.
There were fourteen instances (2% of total detections) where
genes were identified in the unbaited dataset of some samples but not
in the corresponding baited dataset. These fourteen instances were
observed in all meat types and were attributed to eight genes (blaCFE,
4 instances, blaLEN, 2 instances, blaOHIO, 2 instances, oqxB, 2 instances,
bla2, once, fosA, once, mexX, once, and blaORN, once). Multiple alleles
of these genes were present in the bait probe library, except for blaCFE,
blaOHIO, and mexX, which were present only as single copies. All eight
genes except blaLEN, bla2, and mexX were detected at least once in the
baited dataset of other samples.
profiles of the meat samples based on the AMR genes present in these
32 isolates are shown in Figure 7. Beta-lactam resistance was most
prevalent among the isolates and the priority resistance genes, blaCMY,
blaTEM, blaSHV, blaCTX-M, and blaOXA were identified in several. A
comparison of the culture-based, unbaited, and baited sequencing
approaches showed that the baited data set had the most positive
detections for the priority beta-lactamase genes (Figure 8). Using the
baited approach, all 16 meat samples were identified as positive for at
least one priority beta-lactamase compared to 13 samples for both the
culture-based and unbaited sequencing approach. Of particular note
was blaTEM, which had a positivity rate of 63% using the baited dataset
compared to the culture method (27%) and unbaited dataset (25%).
blaCMY, blaSHV, and blaOXA also had a higher positivity rates using the
baited dataset compared to the other two approaches. blaCTX-M was an
exception, and a higher positivity rate (27%) using the culture-based
method compared to unbaited (0%) and baited (13%) sequencing.
3.5. Detection of specific AMR genes
4. Discussion
In a previous work, we described the isolation of twenty-eight
3GC resistant Enterobacteriaceae strains from the beef, chicken, and
veal samples studied here (Rao et al., 2021). Using similar procedures
on the oyster and shrimp samples of this study, four additional 3GC
resistant Enterobacteriaceae strains were obtained. The resistance gene
Frontiers in Microbiology
With metagenomic sequencing, the entire resistome of food
samples can be characterized within a single experiment. The
routine use of this technology would allow food testing
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Flint et al.
10.3389/fmicb.2023.1188872
FIGURE 4
Absolute quantification of AMR gene families in retail meat primary enrichments as identified in unbaited (U) and baited (B) datasets. AMR gene families
were identified using SRST2 and tabulated/visualized using GraphPad Prism v9. Colored segments of each stacked bar, if present, correspond to the
number of gene families mediating resistance to the antibiotic classes as listed in the legend from aminoglycoside (bottom) to phenicol/oxazolidinone
(top). * technical repeat of veal sample 1.
FIGURE 5
Alpha diversity of unbaited and baited beef, chicken, oyster, shrimp, and veal samples. (A) Chao1 gene family richness (estimated # of gene families)
and (B) Shannon’s Diversity indices (gene family evenness). Horizontal lines represent the median, boxes indicate the inter-quantile range and whiskers
represent values within 1.5 IQR of the lower and upper quantiles. Alpha diversities were calculated using PhyloSeq in R and raw normalized read
abundances per sample.
laboratories to expand the scope of their AMR monitoring
activities. However, metagenomics is expensive, often requiring
multiple rounds of in-depth sequencing to detect genes of low
abundance. Even so, genes of public health interest may still go
undetected. Here, we show that a targeted approach (bait-capture)
is a viable alternative to whole genome shotgun metagenomics for
the detection of antibiotic resistance genes in meat and seafood
enrichments. Across all of the tested foods, the baited libraries
required fewer reads (by approximately 40-fold) to detect a higher
number of AMR gene family classes, AMR gene families, and
plasmid genes compared to the unbaited libraries. These results
are similar to others reporting higher recovery of AMR genes
from fecal or wastewater samples when using bait-capture
Frontiers in Microbiology
supporting the use of this technique for the detection of low
abundance genes (Noyes et al., 2017; Lanza et al., 2018; Guitor
et al., 2019).
The most significant increases in AMR gene detection by bait
capture were seen in the samples that had the fewest unbaited reads
map to the AMR database. This implies that the baiting procedure was
especially useful for the detection of low abundance genes. This utility
was also apparent when comparing the most abundant AMR classes
among the commodities between the two datasets. There was good
agreement with the most abundant AMR classes, but the relationship
did not hold with the less abundant genes. These differences at the
lower end of the abundance scale did not impact the AMR gene
diversity between the two datasets as would be expected given the
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10.3389/fmicb.2023.1188872
FIGURE 6
Absolute quantification of plasmid genes families in retail meat primary enrichments as identified in unbaited (U) and baited (B) datasets. Plasmid genes
were identified using SRST2 and tabulated/visualized using GraphPad Prism v9. Colored segments of each stacked bar, if present, correspond to the
number of plasmids genes belonging to the replicons as listed in the legend from IncF (bottom) to Other (top). * technical repeat of veal sample 1.
TABLE 3 Relationship between AMR classes and plasmid genes detected
in retail meat preenrichments.
AMR class
ra
TABLE 4 Unique AMR gene families identified via total and targeted
sequencing.
Sample
P
Both
Baited
Unbaited
16 (61%)
8 (31%)
2 (8%)
12 (32%)
24 (65%)
1 (3%)
7 (28%)
18 (72%)
0
10 (67%)
4 (27%)
1 (7%)
54 (76%)
17 (24%)
0
35 (70%)
14 (28%)
1 (2%)
Chicken 3
39 (83%)
6 (13%)
2 (4%)
Chicken 4
13 (43%)
16 (53%)
1 (3%)
Oyster 1
16 (41%)
21 (54%)
2 (5%)
0.0390
Oyster 2
11 (46%)
13 (54%)
0
0.0407
Shrimp 1
21 (21%)
78 (78%)
1 (1%)
Shrimp 2
10 (16%)
51 (82%)
1 (2%)
Veal 1
6 (43%)
8 (57%)
0
Fusidic acid
0.98
0.0043
Glycopeptide
0.88
0.0019
Beef 1
Pleuromutilin
0.85
0.0072
Beef 2
Lincosamide
0.84
0.0011
Beef 3
Aminoglycoside
0.75
0.0007
Beef 4
Streptothricin
0.71
0.0100
Chicken 1
Tetracycline
0.69
0.0032
Chicken 2
Macrolide
0.68
0.0203
Phenicol
0.67
0.0049
Beta-lactam
0.63
0.0083
Lincosamide/Streptogramin
0.54
Trimethoprim
0.53
a, Pearson correlation coefficient (r).
minimal contribution of low abundance genes to the overall
composition of the resistome (Noyes et al., 2017).
Comparing the AMR genes identified in the baited and
unbaited datasets, there was no association of specific genes with
either dataset. This observation suggests that no genes were
Frontiers in Microbiology
Genes families identified (%)
09
Veal 1_TR
7 (39%)
11 (61%)
0
Veal 2
68 (62%)
40 (36%)
2 (2%)
Veal 3
55 (85%)
10 (15%)
0
Total
380
339
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FIGURE 7
ARG profile of 3GC resistant Enterobacteriaceae strains. 3GC resistant strains were isolated from retail meat primary enrichments by plating on
CHROMagar ESBL. Isolates were sequenced and AMR gene sequences identified using ResFinder 4.0 and tabulated/visualized using GraphPad Prism v
9. Colored segments of each stacked bar, if present, correspond to the number of genes mediating resistance to the antibiotic classes as listed in the
legend from aminoglycoside (bottom) to rifamycin (top). * technical repeat of veal sample 1.
FIGURE 8
Detection of priority beta-lactamase genes using unbaited, and baited sequencing approaches and comparison to a culture method. Each circle
indicates a retail meat sample, filled circles indicate a positive detection in the sample. Numbers indicate the positivity rate of gene detection for each
method across commodities (number of positive detections/number of samples). Circle with the broken outline indicated the technical replicate of
veal sample 1.
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improvements could involve optimizing bait-probe hybridization
or adding additional rounds of sequencing.
A potential drawback of using bait-probe libraries for AMR
detection is that the analysis is limited to known resistance genes and
prevents the detection of new targets. Screening for plasmid genes is
one way to identify samples potentially harboring novel resistance
genes, since multi-drug resistance (MDR) plasmids often carry more
than one resistance gene. Samples testing positive for MDR plasmids
can then be examined more carefully for the presence of AMR
bacteria. However, based on our data, more refinements need to
be made to the bait-probe library to make this approach more
informative. For example, targeting known MDR plasmids, or
clinically relevant mobile elements can aid in the identification of
samples carrying novel resistance genes or cassettes.
From an AMR perspective, bacterial populations that harbor
ESBL genes are important components of the meat microbiome. Of
the three methods tested, bait-capture was able to identify the most
ESBL positive samples. It may be possible that some of the positives
detected by bait-capture may have been due to the presence of DNA
in the sample, rather than from viable cells. It is also possible that the
sensitivity of bait-capture is higher than plating on selective agar due
to the presence of viable but non-culturable cells, silent (unexpressed)
AMR genes, or other technical reasons (Deekshit and Srikumar, 2022;
Yadav et al., 2022). One notable discrepancy between the culture
method and sequencing was the underrepresentation of the blaCTX-M
gene family in the sequenced datasets. This observation was also
noted previously in a study using human fecal samples (BengtssonPalme et al., 2015). Like many beta-lactam genes, members of the
blaCTX-M family comprise a large family of hundreds of alleles
(D'Andrea et al., 2013). It is not known why this gene was not
detected when other similarly large gene-families from the betalactamase class were readily identified by sequencing (blaCMY, blaTEM,
blaSHV, blaOXA). Future iterations of the bait-probe library will need to
focus on the improvement of blaCTX-M detection since this family is an
important, prevalent cause of resistance to third-generation
cephalosporins (Castanheira et al., 2021).
In describing and comparing the results of the baited and
unbaited datasets, several observations were made about the AMR
and plasmid gene content of retail beef, chicken, oysters, shrimp, and
veal. However, given the small sample size of our study, no significant
trends were noted as there was a high degree of variation between
samples of the same commodity. To make any firm conclusions on
the nature of the resistome in Canadian retail meat, more samples,
preferably collected as part of national surveillance programs, would
need to be analyzed using the bait capture methodology. Similar to
the approach used here, regulatory testing workflows for pathogen
detection often incorporate a primary enrichment step to encourage
the growth of target bacterial populations. Thus, a bait-capture
approach can be integrated after primary enrichment to enable
routine monitoring of foodborne AMR genes in the same target
populations of foodborne bacteria (Figure 9).
FIGURE 9
Workflow for AMR detection by bait-capture sequencing. Retail meat
microbiomes are cultured in enrichment media. Following sample
enrichment, the primary cultures are used for pathogen testing. At
this time, a portion of the primary culture can be placed in a DNA
preservative solution for downstream bait-capture. After microbial
DNA extraction, shotgun metagenomics libraries are constructed.
AMR baited libraries are constructed by hybridizing biotinylated AMR
probes to shotgun library DNA. Streptavidin beads are used to
separate the probe-hybridized target DNA followed by low cycle
PCR amplification. Baited libraries are sequenced on an Illumina
Miseq platform using 2×300bp chemistry followed by bioinformatics
analyses to identify the AMR profiles of each sample. ** denote
stopping points where samples can be stored at 4°C or −20°C.
favored by either method. Rather, gene detection varied from
sample to sample and likely reflected differences in individual
microbiomes and the abundance of the AMR genes therein
(Andersen et al., 2016). Genes with a higher frequency of
detection, for example blaCMY, had similar positivity rates across
methods, whereas lower abundance genes (blaTEM, blaSHV) were
more frequently detected in the baited dataset. The few instances
where genes were identified in only the unbaited datasets suggest
some optimization may be required when implementing the baitprobe library to ensure target genes are being captured. These
Frontiers in Microbiology
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found below: https://www.ncbi.nlm.nih.gov/
genbank/, BioProject ID PRJNA909287.
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Flint et al.
10.3389/fmicb.2023.1188872
Author contributions
Conflict of interest
AF: sequencing methodology, investigation, data curation,
and original draft preparation. MR: investigation. AC: concept,
bait library design, and construction. KW: sequencing
methodology. CC: concept and supervision. ST: concept,
supervision, original draft preparation, and funding acquisition.
All authors contributed to manuscript revision, read, and
approved the submitted version.
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
Funding
This work was funded by the Government of Canada Shared Priority
Project, Genomics Research and Development Initiative (GRDI)-AMR.
Supplementary material
Acknowledgments
The Supplementary material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1188872/
full#supplementary-material
The authors would like to thank Alex Gill for critical review of
the manuscript.
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