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Applied and Environmental Microbiology, October 2008, p. 6178-6186, Vol. 74, No. 20
0099-2240/08/$08.00+0 doi:10.1128/AEM.00704-08
Copyright © 2008, American Society for Microbiology. All Rights Reserved.
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Agriculture and Agri-Food Research Centre, Lethbridge, Alberta, Canada T1J 4B1,1 Public Health Agency of Canada, Winnipeg, Manitoba, Canada R3E 3R2,2 Agriculture and Agri-Food Research Centre, London, Ontario, Canada N5V 4T33
Received 25 March 2008/ Accepted 11 August 2008
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Commensal E. coli is often used as an indicator organism to assess the extent and type of resistance in the gastrointestinal tract since it plays a dynamic role in the ecology of multidrug-resistant bacteria and has been shown to be a reservoir of resistance (8, 42). Studies have demonstrated that young cattle have a higher prevalence of resistant fecal E. coli than older stock held on the same farm (12, 27), while carriage of ampicillin-resistant (Ampr) E. coli declines with calf age (22). Elucidating how young animals are affected by continuous subtherapeutic antimicrobial administration and defining the dynamics of acquisition of resistance in E. coli are essential for establishing the mechanism(s) of resistance transmission in feedlot cattle. Serotyping and resistance profiling have provided useful information relating to E. coli populations in cattle (20); however, these techniques have their limitations in establishing the movement of strain types. We used a pulsed-field gel electrophoresis (PFGE) fingerprinting method to discriminate between E. coli strains to understand the persistence of strain types and linked the derived genotypes to antibiogram profiles (ABGs; susceptibility testing) for assessing the movement of transferable resistance elements between strains. We chose to monitor Ampr and tetracycline (TE)-resistant (Tetr) commensal E. coli due to increasing resistance to these antimicrobials in humans.
Using healthy beef cattle maintained for 197 days in a feedlot setting, we investigated the influence of administration of dietary chlortetracycline alone (T) or in combination with sulfamethazine (TS) on resistance selection and distribution of resistance determinants. Further, we examined if resistance was disseminated by specific E. coli strain types between animals in the cohort. The concentrations of the antimicrobial agents used were similar to those of growth promoters used in the cattle industry. The specific objectives were addressed by monitoring (i) the shedding of Ampr and Tetr E. coli over time and assessing the effects of time and individual antimicrobial treatments on the two E. coli populations, (ii) the prevalence and stability of E. coli genetic and phenotypic diversity over time between treatments, and (iii) the distribution and emergence of common TE (tet), ampicillin (amp), and sulfonamide (sul) determinants in untreated versus treated beef cattle.
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For first 80 days, the steers were fed a typical forage-based background diet (70% barley silage, 25% barley grain, and 5% dry matter supplemented with vitamins and minerals) (Fig. 1). Thereafter, animals were shifted over a 21-day transition period from the background diet to a grain-based finishing diet (84% barley grain, 10% barley silage, and 5% supplements). The finishing diet was administered for an additional 124 days. The antimicrobials were administered continually for 197 days starting on day 0 (after fecal collection) and were withdrawn 28 days prior to slaughter. All animals were cared for according to the guidelines set out by the Canadian Council on Animal Care (7).
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FIG. 1. Schematic representation of the time line of the experiment, outlining the arrival of animals, diets, and sampling days. The letters A to I represent sampling times (gray bars). The subtherapeutic antimicrobials were administered via top dressing (diagonal hatching) continuously for 197 days. Isolates from periods A (background diet) and H (finishing diet) were analyzed via susceptibility testing and PFGE and for resistance determinants. The numbers below the time periods represent the numbers of days into the trial. The drawing is not to scale.
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Isolation, identification, and enumeration of E. coli.
Each fecal sample (10 g wet weight) was aseptically placed in a sterile stomacher bag (Fisher Scientific, Ottawa, Ontario, Canada) containing 90 ml of 1x phosphate-buffered saline and mixed for 2 min (230 rpm, room temperature) with a Stomacher (Seward Ltd., Worthing, West Sussex, United Kingdom). The resulting slurries were 10-fold serially diluted, and 100 µl of the appropriate dilution was plated onto the following standard media for E. coli isolation in duplicate: MacConkey agar containing no antibiotics (Difco, Sparks, MD) for total E. coli isolation, MacConkey agar containing ampicillin (32 µg ml–1) for Ampr E. coli, and MacConkey agar containing TE hydrochloride (16 µg ml–1) for isolation of Tetr E. coli. These antimicrobial concentrations are recommended breakpoints for E. coli according to the CLSI (11). The plates were incubated at 39°C for 24 h, and individual colonies were enumerated to calculate CFU counts by taking into account the number of observed colonies and the dilution factor. The numbers of CFU per gram (wet weight) of total, Ampr, and Tetr E. coli populations were calculated. These counts were used for statistical analysis in determining the effects of antimicrobial treatments and time on total, Ampr, and Tetr E. coli strain shedding, as well as to assess treatment-time interactions. Two isolates per animal from nonselective (MacConkey agar containing no antibiotics) plates were arbitrarily selected and confirmed to be E. coli with API 20E (bioMérieux Inc., Durham, NC). These positive isolates were streaked onto Trypticase soy agar (Difco) and grown at 39°C for 24 h. Each isolate was stored in 20% glycerol at –80°C until processed.
During standardization, we found that clonal types from individual animals were generally stable and hence one isolate per animal was used for characterization, which included PFGE analysis, susceptibility profiling, and characterization of resistance determinants. Thus, 80 isolates from the growing (period A, n = 40) and finishing (period H, n = 40) periods were analyzed for each group (control, T, and TS).
Susceptibility testing.
The antimicrobial susceptibilities (phenotypes) of E. coli isolates from periods A and H to a panel of 12 antimicrobials was determined with a disk diffusion assay following CLSI standards (11). For this purpose, Mueller-Hinton II agar (Difco) was used and cells were harvested from the surface of the medium with a cotton swab after 24 h growth at 37°C. Cells were suspended in sterile saline (0.85% NaCl), cell density was adjusted to a 0.5 McFarland turbidity standard, and the diluted cells were plated. Following incubation, zone sizes were measured to two decimal points and used for quantitative analysis. Isolates resistant to two or more antimicrobials were defined as dual resistant or multiresistant, respectively. E. coli ATCC 25922 (American Type Culture Collection, Manassas, VA) was included in each assay as a control strain. Antimicrobial agents were tested with BD BBL Sensi-Disc antimicrobial susceptibility test discs (Becton Dickinson & Co., Sparks, MD) with the breakpoints (micrograms per milliliter) indicated as follows: amoxicillin-clavulanic acid (20 and 10, respectively), ampicillin (AMP; 10), amikacin (30), ceftriaxone (30), cefoxitin (30), ciprofloxacin (5), gentamicin (10), kanamycin (30), nalidixic acid (30), streptomycin (STR; 10), tetracycline (TE; 30), and trimethoprim-sulfamethoxazole (SXT; 1.25 and 23.75, respectively). The ABG of each isolate was established based on the resistance(s) observed.
Genotyping.
PFGE was conducted to assess the genetic diversity of E. coli and to analyze strain transmission in the control and treatment groups during periods A and H. Isolates were subtyped on the basis of PFGE patterns (PPs) of XbaI-cleaved chromosomal DNA in accordance with the standard protocol established by the Centers for Disease Control and Prevention (PulseNet; Centers for Disease Control and Prevention, Atlanta, GA). DNA fragments were resolved by electrophoresis on 1% SeaKem Gold agarose gels (Cambrex BioScience, Rockland, ME) with a contour-clamped homogeneous electric field DRII PFGE apparatus (Bio-Rad Laboratories, Mississauga, Ontario, Canada), with 0.5x Tris-borate-EDTA (BioBasics Inc., Edmonton, Alberta, Canada) as the running buffer, for 17 h at 14°C with a linearly ramped switching time from 2.2 to 55 s and a voltage of 6.0 V cm–1. Gels were stained with 0.5 mg liter–1 ethidium bromide (Sigma-Aldrich Canada Ltd., Oakville, Ontario, Canada), destained in distilled water for 2 h, and photographed with an AlphaImager gel doc (version 4.1.0; Alpha Innotech FC, San Leandro, CA). XbaI-digested Salmonella enterica serovar Branderup H9812 was used as a marker and included in the first, middle, and last lanes of each gel to account for run-to-run variability. Comparison of digested profiles to identify restriction endonuclease digestion pattern clusters (REPCs) was performed with BioNumerics software, version 4.0 (Applied Maths, Austin, TX). Fingerprints were clustered by using the Dice coefficient evaluated by the unweighted-pair group method. A tolerance and optimization of 1% was allowed to account for gel differences. Each isolate was assigned a PP identification number. Isolates that were >90% related were considered highly related and were grouped as a cluster group. Patterns that did not fall into any particular REPC were also assigned a pattern identification number, and if they were detected only once during the trial they were considered unique.
Characterization of resistance determinants.
Based on the ABGs E. coli isolates from periods A and H were characterized for genes responsible for ampicillin resistance (Ampr), TE resistance (Tetr), dual resistance (Ampr Tetr), and sulfonamide resistance (Sulr). Individual isolates that showed resistance were screened for related resistance determinants (amp, tet, or sul). This was done by using multiplex PCR for three β-lactamase-encoding genes (blaOXA1, blaPSE1, and blaTEM1), 14 TE resistance-encoding alleles [tet(A), tet(B), tet(C), tet(D), tet(E), tet(G), tet(K), tet(L), tet(M), tet(O), tet(S), tetA(P), tet(Q), and tet(X)], and 3 sul alleles (sul1, sul2, and sul3). Details of primers, annealing temperatures, and amplicon sizes are provided in Table 1. Plasmids with known, sequenced genes were used as positive controls (Table 1).
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TABLE 1. PCR primers and control strains used for identification of Ampr, Tetr, and Sulr determinants
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To ensure the absence of PCR inhibitors in lysate, a positive control that amplified a 200-bp fragment of the 16S rRNA gene was included in each PCR run with the primer sets and conditions described previously (25). All PCRs were performed on a PTC 100 thermocycler (MJ Research Inc., Watertown, MA), and each run included a negative control (no template DNA) and an appropriate positive control (control plasmid for the tet, amp, or sul determinant). All PCR products (18 µl) were resolved on 1.5% agarose gels containing ethidium bromide by standard procedures. A 100-bp DNA ladder (MBI Fermentas, Burlington, Ontario, Canada) was used to determine product sizes.
Statistical analysis.
E. coli CFU counts were averaged from replicate plates. The counts were log transformed, and the mean and variance were calculated for each pen before performing the weighted analyses, with the inverse of the variance for each pen being used as the weight. For assessing treatment effect, counts from four time periods (A, D, E, and H) were analyzed for the total, Ampr, and Tetr E. coli populations. For assessing time effects, the numbers of CFU were analyzed by a weighted analysis of variance by the MIXED procedure (36) with treatment, time, and their interaction in the model as fixed effects and pen nested in treatment as the random effect. Time was treated as a repeated-measures effect to account for potential correlations among the various times. Various types of variance-covariance structures were fitted, and the one with the lowest Akaike information criterion value was used for the final analysis. Linear and quadratic orthogonal polynomials were used to check the time effect for trends. The UNIVARIATE procedure was used to check the residuals for normality and for potential outliers. When an outlier was detected, it was removed before the final analysis was performed. P values were used to estimate significant differences between various treatments over time, and P values of
0.05 were considered to indicate significant differences.
Cluster analysis was performed on PFGE clonal types by jackknife comparison between REPCs within a treatment. The resulting PPs were characterized by using the criteria described by Tenover et al. (40).
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FIG. 2. Ampr (A) and Tetr (B) E. coli counts (log CFU g–1 [wet weight]) in periods A and H with no antibiotic treatment (control), 350 mg head–1 day–1 chlortetracycline (T), and 350 mg head–1 day–1 each chlortetracycline and sulfamethazine (TS).
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Periods A, D, E, and H were used to assess the effects of antimicrobial treatment and time on E. coli counts. Least-square differences revealed significant treatment effects on Tetr E. coli (P < 0.001), and a significant effect of time on all three (total, Ampr, and Tetr) E. coli populations (P < 0.001) was observed. Analysis revealed a significant treatment-time interaction for Tetr (P < 0.001) but not for Ampr E. coli (P = 0.9532). Orthogonal contrasts revealed significant linear relationships with time for Tetr E. coli in the control (P < 0.001) and TS (P < 0.001) groups and a significant quadratic relationship for the T group (P < 0.001). Although the highest Tetr E. coli increase per week from period A period to H was observed in the TS group (0.14 log CFU g–1 week–1), followed by the control group (0.11 log CFU g–1 week–1), the highest counts (absolute numbers) of Tetr E. coli bacteria shed (CFU per gram) were observed in the T group and followed the trend T > TS > control. We found limited information in the literature on the effect of TE administration on Tetr E. coli shedding over time.
Susceptibility profiles.
All of the isolates examined in this study carried determinants of resistance to at least one of the antimicrobials tested, as also observed previously in domesticated animals (38, 39). The antibiogram diversity, irrespective of the treatments, decreased as the trial progressed and animals adapted to a finishing diet (period H) (Fig. 3C). Within the control population in period A, isolates displayed 11 ABGs, with SXT TE being a common phenotype found in 22.5% of the isolates (Table 2). By period H, only three ABGs were recorded (Fig. 3C), with AMP TE detected in 50% of the isolates (Table 2). Isolates from the T treatment group in period A also displayed 11 ABGs, which decreased to 3 by period H (Fig. 3C). For this treatment, however, higher frequencies of detection of TE and SXT TE (30 and 27.5% of the isolates) in period A and AMP TE and TE (37.5% of the isolates for both phenotypes) in period H were observed (Table 2). In the TS treatment group, period A isolates displayed 13 ABGs, which also decreased to 3 by period H (Fig. 3C); the most common phenotype in period A was TE (30% of the isolates), which by period H had changed to AMP TE (55% of the isolates) (Table 2). In the literature, we did not find any evidence of a decrease in phenotypic diversity with animal age; however, a higher prevalence of resistant fecal E. coli in younger than older animals held within the same farm has been previously reported (3, 20, 21, 27, 30). Khachatryan et al. (27) have suggested that the intestinal physiology of young animals favors certain niche-specific clones which are retained in older animals. Environmental sources have also been implicated as playing a role in the inoculation of particular strain types, with active competition in the bovine gut leading to the expansion of these niche-specific strains (28). We know for certain that the animals in this study were not exposed to any antimicrobials prior to arrival at our feedlot and that the 6- to 8-month-old animals had diverse phenotypes on day 0. Therefore, animal-to-animal transmission of selected phenotypes and strains (discussed later) seems to better fit predominate detection of TE and AMP TE by period H in the control and treatment groups. A fitness advantage of strains carrying these prevalent phenotypes, however, cannot be ruled out.
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FIG. 3. Numbers of PPs (A) REPCs (B), and ABGs (C) in tested isolates from periods A and H within the control group and the T and TS treatment groups.
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TABLE 2. Frequencies of detection of particular antibiogram patterns during periods Aa and Ha in control group and T and TS treatment groups
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Temporal distribution of strains.
In order to better understand strain prevalence, isolates were genotyped and correlated to ABGs. Given a similarity coefficient of 90% (Dice index), PFGE data revealed 44 distinct PPs among the 240 isolates examined (Fig. 4). When comparing PFGE data in period A, some PPs (3 and 11) were detected in all of the treatment groups whereas others were specific to a particular treatment group. Further, some isolates within the control (n = 13), T (n = 20), and TS (n = 21) treatment groups did not cluster into any REPC. Unique isolates (n = 8) were also detected (Fig. 4).
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FIG. 4. Dendrograms showing the relationship between isolates shed in period A (day 0; n = 40) and those shed in period H (day 197; n = 40) by the control (A), T treatment (B), and TS treatment (C) groups. PPs, time periods, ABGs, and REPCs (>90% banding similarity) in the indicated time periods are shown. REPCs are separated by different symbols among the three treatment groups.
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The reduction in the numbers of PPs and REPCs correlates well with the limited number of ABGs by period H, indicating overall lower strain diversity by the finishing phase in cattle in a feedlot setting. This decrease in genetic diversity was not a treatment effect since it was also observed in animals in the control group. Instead, it appears to be due to strain selection and propagation of selected strain types via animal-to-animal transmission. In order to validate this strain transmission hypothesis, the PFGE types of individual animals were tracked over time and E. coli isolates from animals in the same pens were observed to cluster into related REPCs, indicative of sufficient movement of E. coli between animals. We found that animals within a pen, among different pens, and also within this cohort started shedding E. coli strains with similar genotypic characteristics over time. Given these results, environmental sources are speculated to play a role in strain transmission among cattle. The animals in the control and treatment groups were housed spatially separate from each other, and no contact was possible between animals on different treatments and even among some replicates of the same treatment. Some nose-to-nose and body contact between animals in adjacent pens and within the same pen could be responsible for possible strain transmissions, but this limited contact cannot explain the presence of the same strain type in spatially separate pens. The animal-to-animal transmission is further validated by the diverse PPs of animals in period A which had little to no chance of strain transmission at day 0. It has also been established by other studies that the administration of antimicrobials selects for resistant bacteria which are subsequently transferred through contaminated food or water and that the feedlot environment is a crucial source of new strains (23). Similarly, in our study we found that antimicrobial administration to beef cattle selects for resistant E. coli and we speculate the feedlot environment plays a critical role in resistance dissemination. The exact route of strain transmission was not characterized in this study, but fecal material is considered a possible source. Our results are in contrast to those of a previous study in which animals in interspersed pens maintained distinct E. coli populations (28).
We recognize that using a single isolate per animal may have its limitations. Therefore, upon observing similar clonal types in animals on different treatments, we PFGE typed an additional isolate from each animal in the T and TS groups in period H. We found that these supplementary isolates grouped in the already described phenotypes and PPs. In the past, single isolates have been used to assess the temporal diversity of antimicrobial-resistant E. coli without compromising the trends over time (6, 23). In addition, the repeated measure offered by four replicates in each treatment group gave us a statistically significant sample size, confirming the trends, and showed that an increase in sample size did not result in an increased genotype number, as also previously observed (23).
Distribution of resistance genes in E. coli.
Isolates from periods A and H displaying ampicillin resistance (Ampr, n = 19), TE resistance (Tetr, n = 116), dual resistance (Ampr Tetr; n = 105), or SXT resistance (Sulr, n = 69) upon susceptibility testing were characterized for resistance alleles in order to understand the distribution of genes and their acquisition over time. In many cases, we found that genotypically similar strains did not necessarily carry the same resistance determinants.
β-Lactamase genes.
Ampr alone was observed exclusively in 19 of the period A isolates and was never observed among isolates from period H (Table 3). Upon testing for β-lactamase genes, we found that blaTEM1 was present in 14 of these isolates while blaPSE1 was detected in the remaining 5 (Table 3). blaTEM1 has been previously detected as a predominant allele in beef cattle (18).
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TABLE 3. PCR detection of Ampr and Tetr determinants in isolates from periods A and Ha
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Several Tetr isolates (n = 32) from periods A and H contained more than one tet allele (dual tet resistance) (Table 3). tet(A) tet(B) was detected in periods A (11.5%) and H (13.2%); combined tet(B) tet(C) and tet(A) tet(C) were also observed.
An increase in the number of dual-resistant Ampr Tetr isolates from period A (21.9%) to period H (78.1%) and a marked reduction in the number of isolates carrying a single resistance, either Ampr or Tetr alone, in period H was clearly observed and not restricted to a few individual animals or pens. These results are corroborated by ABGs, which showed an increased prevalence of isolates carrying AMP TE. Of the 105 isolates which tested positive for combined Ampr Tetr (Table 3), 23 belonged to period A and 82 belonged to period H. tet(A) was detected frequently in periods A (30.4%) and H (41.5%), while blaTEM1 conferred ampicillin resistance in periods A (82.6%) and H (67.1%). blaOXA1 and blaPSE1 were never detected within these dual-resistant isolates. These results indicate an increase in combined tet(A) and blaTEM1 acquisition by period H. As noted via genotyping previously, predominant detection of PP 30 in period H with phenotype AMP TE or TE suggested that this strain type either acquired or lost Ampr over time to display this phenotype. Resistance gene distribution supports the acquisition of Ampr within PP 30 (Fig. 4; Table 3). Other investigators have reported a similar genetic linkage of antimicrobial drug resistance genes (39). As observed for Tetr single resistance, Ampr Tetr isolates also showed an increase in dual TE resistance genes, viz., tet(A) tet(B), tet(B) tet(C), and tet(A) tet(C), in periods A to H (Table 3).
Sulfonamide genes.
Of the isolates examined from periods A and H, 69 exhibited Sulr (Table 4). sul2 was detected at a higher frequency (47.8%) than sul1 (17.4%) (Table 4). Similar higher sul2 than sul1 detection has been reported earlier in humans and swine (19) and attributed to a fitness advantage in clinical human E. coli (15). sul3 was never detected in this study but has been previously reported in E. coli from pigs (18, 19, 33, 35). Dual sul1 sul2 genes were also observed in isolates from both periods A (31.9%) and H (40%) (Table 4).
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TABLE 4. PCR detection of Sulr determinants in isolates from periods A and H that displayed Sulr upon susceptibility testing
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This research was supported by a GAPS grant from Agriculture and Agri-Food Canada (AAFC) and an A-base grant (AAFC) to R.S.
Published ahead of print on 22 August 2008. ![]()
Contribution 38707054 from Agriculture and Agri-Food Research Centre, Lethbridge, Alberta, Canada. ![]()
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