**Antimicrobial Resistance Patterns in Organic and Conventional Dairy Herds in Sweden**

**Karin Sjöström <sup>1</sup> , Rachel A. Hickman <sup>2</sup> , Viktoria Tepper 2,3, Gabriela Olmos Antillón 1 , Josef D. Järhult <sup>4</sup> , Ulf Emanuelson <sup>1</sup> , Nils Fall <sup>1</sup> and Susanna Sternberg Lewerin 5,\***


Received: 3 November 2020; Accepted: 17 November 2020; Published: 21 November 2020

**Abstract:** Monitoring antimicrobial resistance (AMR) and use (AMU) is important for control. We used *Escherichia coli* from healthy young calves as an indicator to evaluate whether AMR patterns differ between Swedish organic and conventional dairy herds and whether the patterns could be related to AMU data. Samples were taken twice, in 30 organic and 30 conventional dairy herds. Selective culturing for *Escherichia coli*, without antibiotics and with nalidixic acid or tetracycline, was used to estimate the proportions of resistant isolates. Microdilution was used to determine the minimum inhibitory concentrations (MICs) for thirteen antimicrobial substances. AMU data were based on collection of empty drug packages. Less than 8% of the bacterial growth on non-selective plates was also found on selective plates with tetracycline, and 1% on plates with nalidixic acid. Despite some MIC variations, resistance patterns were largely similar in both periods, and between organic and conventional herds. For most substances, only a few isolates were classified as resistant. The most common resistances were against ampicillin, streptomycin, sulfamethoxazole, and tetracycline. No clear association with AMU could be found. The lack of difference between organic and conventional herds is likely due to a generally good animal health status and consequent low AMU in both categories.

**Keywords:** antibiotic; antibiotic resistance; livestock; antibiotic use; AMR; MDR; environment

### **1. Introduction**

Antimicrobial resistance (AMR) in bacteria is a natural phenomenon that is accelerated by the selection pressure caused by antimicrobial use (AMU). Antibacterial drugs are important tools in human and veterinary medicine, necessary to combat bacterial infections, conduct advanced surgical and immunosuppressive treatments as well as ensure global food security [1]. Overuse and misuse of antibacterial drugs in humans, companion animals and livestock promote antimicrobial resistance worldwide, leading to an increased risk of treatment failures [1]. Both veterinarians and physicians face the challenge of balancing the need to treat infections against the risk of promoting AMR. Historically, Sweden has been a strong advocate for developing and implementing strategies to reduce the selection

pressure by reducing AMU and closely monitoring AMU and AMR across all sectors [2–4]. Monitoring AMU in animals is challenging in many ways, as regards legislative framework, data sources and methods for data collection and analysis [5–7]. The quality and availability of data on livestock AMU vary between different regions of the world but many countries have spent a considerable effort on the development of data collection systems [5]. In Sweden, as in the rest of the European Union, antibiotics for animals are available on veterinary prescription only. Swedish AMU statistics stem from sales data from pharmacies, based on veterinary prescriptions [8]. Generally, farm-based data are not available in national statistics. In the Swedish dairy sector, however, detailed statistics on animal health and veterinary treatments are available and continuously evaluated.

The main reason for studying AMU is to monitor strategies to contain AMR, and to assess associations with AMR prevalence. Harada and Asai [9] reviewed data on AMR prevalence in bacteria from cattle in several countries around the world. Out of the included countries, Sweden presented the lowest prevalence figures, whereas countries such as Japan, France and Germany had medium levels and the Netherlands had the highest prevalence. AMR monitoring entails many methodological challenges. In the EU, data on AMR in zoonotic and indicator bacteria from humans, selected animal species and food are collected annually and jointly analyzed [6,7]. The AMR figures are based on epidemiological cut offs, so-called ECOFFs [10]. According to Commission Decision 213/652/EU, antimicrobial susceptibility testing of *Escherichia coli* from fecal samples taken at slaughter from calves <1 year of age should be included in AMR monitoring. This, however, applies only to countries where the total amount of meat from calf slaughter exceeds 10,000 tones/year and there is not sufficient longitudinal data to evaluate temporal trends in those countries that participate in harmonized monitoring [7]. In Sweden, indicator bacteria such as *E. coli* are isolated from the intestinal content of healthy pigs and poultry sampled at slaughter within the monitoring framework, or from feces collected from live animals in other projects [8]. In 2017, *E. coli* isolated from rectal swabs taken in a project, from 85 calves <2 months old, were included in the monitoring report [11]. Approximately half of the isolates in this study were susceptible to all substances tested while less than one-third were resistant to three or more substances. The most common resistance traits were against streptomycin, sulfamethoxazole, tetracycline or ampicillin. No isolate was resistant to cefotaxime, ceftazidime, colistin, florfenicol or gentamicin. Currently, these are the only available data on indicator *E. coli* from cattle in the national monitoring program.

There are no published papers that compare AMR prevalence in different dairy production systems in Sweden. Due to the link between AMU and AMR, it would be assumed that herd types with lower AMU would also have a lower prevalence of AMR. There are some international studies comparing AMU and AMR in organic and conventional herds. In the United States, organic farms are not allowed to treat with antibiotics at all, as was confirmed in a study on AMU [12,13]. In the European regulation for organic dairy herds (Council Regulation (EC) No 834/2007), AMU is restricted to a maximum number of three treatments per cow and year. The rules of the Swedish organic certification association are in some respects stricter than the EU regulations, such as requiring double withdrawal periods for milk from treated cows [14]. Whether these strict regulations are reflected in the prevalence of AMR in Swedish organic dairy herds is not known.

The aim of this study was to estimate the prevalence of AMR in Swedish dairy herds, using susceptibility testing of *E. coli* from healthy young calves as an indicator to evaluate whether AMR patterns differ between organic and conventional dairy herds and whether they could be related to AMU data.

#### **2. Materials and Methods**

#### *2.1. Study Population*

A convenience sampling design was used, where 30 organic and 30 conventional dairy herds were selected. The herds were located throughout Sweden, with an equal number of organic and conventional farms in each geographic area, and with herd sizes reflecting the overall population of Swedish dairy herds. Each farm was visited by the first author (KS) during the indoor season, February to May in 2016 and November 2016 to March in 2017.

#### *2.2. Faecal Sampling*

At the beginning of each study period, fecal samples from healthy calves (i.e., calves with no signs of disease) were collected by the first author. The aim was to sample 5 calves less than two months old, but if this was not possible at the time of the visit, the farmer took the remaining samples according to instructions provided and sent the samples to the laboratory. The fecal samples were collected from rectum with Amie's charcoal culture swabs (Copan diagnostics Inc., Murrieta, CA, USA) and either brought directly to the laboratory by the first author, or sent by standard mail. The samples were, upon arrival to the lab, stored in a refrigerator and analyzed within 48 h from sampling.

At the beginning of the second study period, the first author also collected fecal samples from the farm environment. Two fecal swab samples were collected: one from an indoor drainage site and one from the manure pit. E-swabs (Copan diagnostics Inc., Murrieta, CA, USA) were used for sampling, and the samples were brought to the laboratory by the first author and stored as described above.

#### *2.3. Collection of AMU Data*

In a parallel study, information about AMU was collected from the study herds during three months following each initial sampling visit [15]. On-farm data collection was performed according to the so-called BIN method, where empty drug containers were collected on each farm as described by Olmos Antillón et al. [15]. Briefly, the farm staff/owners were instructed to place discarded packaging of any drug used on farm (administered by them or a visiting veterinarian) into plastic bags throughout the observation period. The bags were collected one day after the end of each observation period. In the current study, these data were used as a proxy for AMU in the study herds.

#### *2.4. Antimicrobial Susceptibility Testing*

We used *E. coli* as the AMR indicator bacteria and the quality accredited laboratory methods described in detail by Duse et al. [16]. In summary, the samples were diluted in 3 mL of 0.9% NaCl and, subsequently, 50 µL of 10-fold dilutions (10−<sup>2</sup> and 10−<sup>4</sup> for calf samples, 10−<sup>1</sup> and 10−<sup>3</sup> for environmental samples) were streaked on PetrifilmTM (3MTM, St Paul, MN, USA) Select *E. coli* count (SEC) plates (3M Microbiology Products) and cultured overnight at 42 ◦C. The colony-forming units (CFUs) were counted the next day and calculated back to CFU/mL in the original sample.

The proportion of *E. coli* that were resistant to nalidixic acid and tetracycline was determined by parallel plating of the diluted fecal samples on SEC plates supplemented with 50 µL of 672 µg/mL nalidixic acid or 1344 µg/mL tetracycline, respectively. The plates were incubated overnight at 42 ◦C and the CFUs were counted on the next day. Isolates growing on these plates were regarded as resistant. Based on the estimated CFU/mL in the original sample, it was possible to estimate the proportion of tetracycline-resistant and nalidixic acid-resistant *E. coli*.

Subsequently, one random colony from each sample on the plates without antibiotics was selected, subcultured and identified as *E. coli* by morphology and the indole test. The antimicrobial susceptibility of these *E. coli* isolates was then determined with a VetMICTM (SVA, Uppsala, Sweden) panel of 13 antimicrobial substances. Epidemiological cut-off values for the minimum inhibitory concentration (MIC), determined according to the European Committee on Antimicrobial Susceptibility Testing [10], were used to classify isolates as susceptible or resistant. An isolate was defined as resistant if the MIC value was above the cut off for an antimicrobial substance and as multidrug resistant (MDR) if the MIC values were above the cut off for at least three substances from different antimicrobial classes.

All isolates with an MIC for colistin >2 mg/L were examined with PCR targeting *mcr* 1–5, according to Rebelo et al. [17]. Similarly, isolates that were resistant to third-generation cephalosporines (MIC for ceftazidime >0.5 mg/L), were examined further for extended spectrum beta-lactamase (ESBL) production and ESBL genes. Phenotypic confirmatory tests for production of ESBL in *E. coli* were performed with and without clavulanic acid in Sensititre EUVSEC2 (Thermo Fisher Scientific Inc., Waltham, MA, USA) microdilution panels and interpreted according to EUCAST [10]. PCR for identification of ESBL-encoding genes was performed on ESBL-producing isolates as previously described [18].

All analyses during the first period were performed by the accredited laboratory in the Swedish National Veterinary Institute (SVA), while analyses during period 2 were performed at the Zoonosis Science Center, Uppsala University. To ensure equal procedures in both periods, the researchers performing the analyses in the second period carried out the laboratory work and interpretations of the VetMIC plates on some samples together with SVA staff. These samples included isolates where the interpretation of MIC values was difficult as well as isolates where the results were more evident.

#### *2.5. Antimicrobial Resistance Patterns and Herd Production System*

The difference in the proportion of resistant isolates (for each antimicrobial substance as well as MDR) between organic and conventional herds was tested with Fisher's exact test. The difference in the proportion of *E. coli* growing on media supplemented with tetracycline or nalidixic acid, as compared to non-supplemented medium, between organic and conventional herds was assessed by the Mann Whitney U test for unpaired samples.

The mean and median MIC values and proportion of isolates classified as resistant for each substance, from herds treated or not treated with the same class of drug, were also assessed. In addition, the proportion of resistant isolates for each substance and the herd AMU for the corresponding substance, expressed as the total number of defined course doses (DCD/animal/year, see Olmos Antillón et al., 2020 for details on DCD calculations), was assessed for organic and conventional herds.

Heatmaps illustrating AMR patterns in calf isolates from each herd were generated in Python (www.python.org) using the matplotlib, pandas and seaborn packages. All Python scripts are available on request.

#### *2.6. Ethical Statement*

All animals in this study were treated according to the ethical standards of the Swedish regulations. The competent authorities stated that no ethical permission was required for this sampling. Participation in this study was voluntary, and the farmers were informed about the purpose and methods of this study. They were assured that all information would be treated anonymously and that they could withdraw from this study at any time.

#### **3. Results**

In total, 293 calves from 60 herds were sampled during the first period. During the second period, 258 calves from 54 herds were sampled (3 organic and 3 conventional farms did not participate in the second round). A total of 103 fecal environmental samples (54 from manure drainage and 49 from manure pit) were also taken from these 54 herds at the start of the second period. The herd size ranged from 48 to 230 cows, with a median of 80 cows in the organic herds and 110 in the conventional herds. The number of sampled calves per herd and time point varied from 2 to 6, with a median of 5. The age of the sampled calves ranged from 0 to 46 days, with a median of 15 days in both herd types in the first sampling round, while the median age was 18 days in organic herds and 26 days in conventional herds in the second sampling round.

#### *3.1. Proportion of Tetracycline- and Nalidixic Acid-Resistant E. Coli*

In the calf samples from organic herds in the first period, 7.3% of the bacterial growth on non-selective plates was also found on selective plates with tetracycline; the corresponding proportion for selective plates with nalidixic acid was 0.8%. In samples from conventional herds, 4.9% of the growth on non-selective plates was also found on selective plates with tetracycline and 1.0% on selective plates with nalidixic acid. The difference was significant (*p* < 0.05) for nalidixic acid but not for tetracycline. The results from the calf samples for period 2 had to be discarded due to methodological errors that could not be resolved. In the environmental samples from organic herds, 6.4% of the bacterial growth on non-selective plates was also found on selective plates with tetracycline, the corresponding proportion for selective plates with nalidixic acid was 2.6%. In samples from conventional herds, 9.5% of the growth on non-selective plates was also found on selective plates with tetracycline and 3.8% on selective plates with nalidixic acid. The differences observed were not significant.

#### *3.2. Antimicrobial Susceptibility*

Figures 1–3 show the distribution of MIC values for substances in the VetMICTM panel for calf isolates in period 1 and 2, and for environmental isolates, respectively. Some variations were seen but the resistance patterns were largely similar in period 1 and 2, and between organic and conventional herds. For most of the antimicrobial agents, only a few isolates had MIC values above the epidemiological cut offs. All isolates from calves and environment were susceptible to florfenicol. All but two isolates in period 2 (one from a calf and one from a manure drainage) were susceptible to gentamicin, with the resistant isolates having MICs just above the cut off. The proportions of isolates with MICs above the epidemiological cut offs were highest for ampicillin, streptomycin, tetracycline and sulfamethoxazole (Figures 1–3).

The proportions of isolates with AMR and MDR in the different samples are illustrated in Figure 4. There were no clear differences between calf samples in period 1 and period 2, whereas the proportions of both AMR and MDR were lower in the environmental samples. In period 1, 52% of the calf isolates were resistant to at least one of the tested antimicrobial substances, the corresponding figure for period 2 was 44%, and for environmental samples 27%. There was no significant difference between organic and conventional herds. In both periods, 28% of the calf isolates were MDR and in environmental samples 12% of the isolates were MDR (7% of isolates from manure drainage and 16% of isolates from manure pit). There was no significant difference between organic and conventional herds. There were very few significant differences between the herd types for single antimicrobial substances, and no consistent pattern over the two sampling occasions (Figures 1–3). The most common resistances were against ampicillin, streptomycin, sulfamethoxazole, and tetracycline. Most herds had a rather high proportion of isolates that were resistant to at least one antimicrobial, but the majority had no, or very few, samples that were MDR (Figure 4). In the first period, five herds had >60% of calf isolates with MDR, while three other herds had >60% of calf isolates with MDR in the second period.

In period 1, 31 *E. coli* isolates had a colistin MIC of >2 mg/L, but no *mcr* genes were detected. Three isolates were resistant to third-generation cephalosporins, where one was confirmed as ESBL producing and carried CTX-M-1.

In period 2, 11 *E. coli* isolates from calf samples had a colistin MIC above cut off, but at the time of PCR testing, pure cultures could not be obtained from four of these. The remaining seven were subjected to the *mcr*-PCR and all were negative. Five calf *E. coli* isolates were resistant to third-generation cephalosporins, but none of these were ESBL producing and were therefore not further analyzed for resistance genes. Four isolates from environmental samples had a colistin MIC above cut off but three could not be pure cultured for the PCR, the remaining one was negative in the *mcr*-PCR. When the microdilution tests were performed, all cultures were checked for purity and hence it was concluded that the contamination had occurred after this step, in the process of storing the isolates.

The resistance patterns in calf isolates from each herd and for each substance are illustrated in the heatmaps in Figure 5. No consistent pattern could be discerned between herd type or sampling period and the heatmaps confirm the generally low resistance prevalence in the sampled herds.


*Antibiotics* **2020**, *9*, x 6 of 18


**Figure 1.** Minimum inhibitory concentration (MIC) distribution of *Escherichia coli* isolates from calves, sampled in period 1, in 30 (148 isolates) organic (Org) and 30 (145 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical black lines are the epidemiological cut-off points, according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values. **Figure 1.** Minimum inhibitory concentration (MIC) distribution of *Escherichia coli* isolates from calves, sampled in period 1, in 30 (148 isolates) organic (Org) and 30 (145 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical black lines are the epidemiological cut-off points, according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values.


*Antibiotics* **2020**, *9*, x 7 of 18


**Figure 2.** Minimum inhibitory concentration (MIC) distributions of *Escherichia coli* isolates from calves, sampled in period 2, in 27 (127 isolates) organic (Org) and 27 (131 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical black lines are the epidemiologic cut-off points according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values. **Figure 2.** Minimum inhibitory concentration (MIC) distributions of *Escherichia coli* isolates from calves, sampled in period 2, in 27 (127 isolates) organic (Org) and 27 (131 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical black lines are the epidemiologic cut-off points according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values.


*Antibiotics* **2020**, *9*, x 8 of 18


**Figure 3.** Minimum inhibitory concentration (MIC) distributions of *Escherichia coli* isolates from environmental samples of farm manure (drainage and manure pit) at the beginning of period 2, in 27 (50 isolates) organic (Org) and 27 (53 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical thicker black lines are the epidemiologic cut-off points according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values. **Figure 3.** Minimum inhibitory concentration (MIC) distributions of *Escherichia coli* isolates from environmental samples of farm manure (drainage and manure pit) at the beginning of period 2, in 27 (50 isolates) organic (Org) and 27 (53 isolates) conventional (Conv) Swedish dairy herds, in a panel of 13 antimicrobial agents. Blue color indicates the range of concentrations tested. Vertical thicker black lines are the epidemiologic cut-off points according to EUCAST. *p* values test the difference between organic and conventional herds with Fisher's exact test. Yellow color denotes isolates from conventional herds and pink color shows MIC values.

*Antibiotics* **<sup>2020</sup>**, *<sup>9</sup>*, 834 *Antibiotics* **2020**, *9*, x 9 of 18 >60% of calf isolates with MDR, while three other herds had >60% of calf isolates with MDR in the second period.

isolates.

The proportions of isolates with AMR and MDR in the different samples are illustrated in Figure 4. There were no clear differences between calf samples in period 1 and period 2, whereas the proportions of both AMR and MDR were lower in the environmental samples. In period 1, 52% of the calf isolates were resistant to at least one of the tested antimicrobial substances, the corresponding figure for period 2 was 44%, and for environmental samples 27%. There was no significant difference between organic and conventional herds. In both periods, 28% of the calf isolates were MDR and in environmental samples 12% of the isolates were MDR (7% of isolates from manure drainage and 16% of isolates from manure pit). There was no significant difference between organic and conventional herds. There were very few significant differences between the herd types for single antimicrobial substances, and no consistent pattern over the two sampling occasions (Figures 1–3). The most common resistances were against ampicillin, streptomycin, sulfamethoxazole, and tetracycline. Most herds had a rather high proportion of isolates that were resistant to at least one antimicrobial, but the majority had no, or very few, samples that were MDR (Figure 4). In the first period, five herds had

**Figure 4.** Number of sampled herds with zero (0), low (1–25%), medium (26–60%) and high (>60%) proportions of *Escherichia coli* with antimicrobial resistance to any antimicrobial class (AMR) or to three or more antimicrobial classes (MDR) in Swedish organic and conventional dairy herds. Isolates from calf samples in period 1 (per 1) and period 2 (per 2) and environmental samples in period 2 **Figure 4.** Number of sampled herds with zero (0), low (1–25%), medium (26–60%) and high (>60%) proportions of *Escherichia coli* with antimicrobial resistance to any antimicrobial class (AMR) or to three or more antimicrobial classes (MDR) in Swedish organic and conventional dairy herds. Isolates fromcalf samples in period 1 (per 1) and period 2 (per 2) and environmental samples in period 2 (env). The resistance patterns in calf isolates from each herd and for each substance are illustrated in the heatmaps in Figure 5. No consistent pattern could be discerned between herd type or sampling period and the heatmaps confirm the generally low resistance prevalence in the sampled herds.

**Figure 5.** *Cont*.

**Figure 5.** Heatmaps showing the patterns of resistant and susceptible *Escherichia coli* isolates from Swedish organic and conventional dairy herds sampled in two different time periods. (**a**) conventional herds 1st sampling (**b**) conventional herds 2nd sampling (**c**) organic herds 1st sampling (**d**) organic herds 2nd sampling. Color scale illustrates average value for each herd and each antimicrobial substance tested, where 1 = resistant and 0 = susceptible. AMX = ampicillin, CAZ =

The resistance patterns in calf isolates from each herd and for each substance are illustrated in

the heatmaps in Figure 5. No consistent pattern could be discerned between herd type or sampling

period and the heatmaps confirm the generally low resistance prevalence in the sampled herds.

**Figure 5.** Heatmaps showing the patterns of resistant and susceptible *Escherichia coli* isolates from Swedish organic and conventional dairy herds sampled in two different time periods. (**a**) conventional herds 1st sampling (**b**) conventional herds 2nd sampling (**c**) organic herds 1st sampling (**d**) organic herds 2nd sampling. Color scale illustrates average value for each herd and each antimicrobial substance tested, where 1 = resistant and 0 = susceptible. AMX = ampicillin, CAZ = **Figure 5.** Heatmaps showing the patterns of resistant and susceptible *Escherichia coli* isolates from Swedish organic and conventional dairy herds sampled in two different time periods. (**A**) conventional herds 1st sampling (**B**) conventional herds 2nd sampling (**C**) organic herds 1st sampling (**D**) organic herds 2nd sampling. Color scale illustrates average value for each herd and each antimicrobial substance tested, where 1 = resistant and 0 = susceptible. AMX = ampicillin, CAZ = ceftazidime, CHL = chloramphenicol, CIP = ciprofloxacin, CST = colistin, CTX = cefotaxime, FLOR = florfenicol, GEN = gentamycin, NAL = nalidixic acid, STR = streptomycin, SXT = sulfamethoxazole, TET = tetracycline, and TMP = trimethoprim.

#### *3.3. Association between AMU and AMR*

All herds but one had used some antibiotic treatment during the observation periods. Table 1 shows the average and median MICs as well as the proportions of resistant isolates of each sample type, in herds with or without records of having used the corresponding antimicrobial substance. For most substances, the average MIC and the proportion of resistant isolates was lower in herds with low or no recorded treatments, while for others the opposite, or no difference, was seen. There was little or no difference in median MIC values, and the observed differences correspond to one dilution step.



<sup>a</sup> Treatments represent the following corresponding substances: a1 benzylpenicillin/amoxicillin/kloxacillinbensatin; a2 enrofloxacin; a3 dihydrostreptomycin; a4 oxytetracycline; a5 sulfadiazin-trimethoprim/sulfadoxine-trimethoprim, N = number of herds with recorded treatment of the corresponding substance, and <sup>b</sup> number of decimal points reflect the number of decimal points in the MIC values (i.e., depending on the concentration/degree of dilution in the test panel).

The overall proportions of resistant *E. coli* isolates from calf and environmental samples and the corresponding use of antimicrobial treatments in these herds, expressed as the average of the total number of defined course doses per animal per year for the corresponding antimicrobial substance, are shown in Table 2. Some variation but no clear association between the proportion of resistant isolates and recorded AMU or herd management category could be found.

**Table 2.** Proportion of resistant *Escherichia coli* isolates from fecal samples from calves and environment (manure drainage and manure pit) in 27 organic and 27 conventional Swedish dairy herds (sampling period 2) and the corresponding use of antimicrobial treatments in these herds, as determined by collection of empty drug packages. Organic = organic herds, conventional = conventional herds, %R = proportion of resistant isolates (%), and DCD = total number of defined course doses per animal per year for the corresponding antimicrobial substance, expressed as the average figure for all herds in the category during the two data collection periods.


From the available data, there was no indication of higher AMU in herds with higher proportions of MDR isolates.

#### **4. Discussion**

To the best of our knowledge, potential differences in AMR patterns between organic and conventional dairy herds in Sweden have not been previously studied. Some studies indicate that AMU is lower in organic herds [19,20], and AMR has been reported to be lower in organic pig herds, although the difference between organic and conventional production was less in Sweden than in some other countries [21]. Previous studies indicate that the differences in health status (that would affect AMU) between organic and conventional dairy herds in Sweden are small [22,23]. This is supported by the data from the herds in the present study that revealed no statistical difference in overall AMU between the two production systems, although some minor difference in patterns of AMU could be noted [15].

We chose to sample calves because previous data indicate that the prevalence of AMR decreases with the age of the animals [13,24,25] and we wanted to maximize the detection of AMR in the study herds. Sampling older animals might underestimate the levels of AMR, which should be taken into account when comparing data from different studies [24]. The environmental manure samples collected at the start of period 2 can be assumed to represent older animals (as cows produce most of the manure in the herd) and reflect a previous time period. The lower proportion of isolates classified as resistant in these samples, in comparison to the calf samples, support the assumption that herd-level AMR might be underestimated in samples from older animals. On the other hand, the storage time for the manure in the farm environment may also affect the composition and resistance pattern of the *E. coli* population in the samples.

In Europe, the prevalence of ESBL-producing *E. coli* in food-producing animals varies by country and animal species. In 2017, the prevalence in individual veal calves ranged from 7.1% in Denmark to 89.0% in Italy, with an EU mean of 44.5% [6]. Overall, in the eight EU countries supplying data on resistance in *E. coli* from calves to ampicillin, cefotaxime, ciprofloxacin and tetracycline for the

period 2009–2017, there were 10 decreasing and 8 increasing trends [7]. A study in Spanish cattle herds tested *E. coli* isolated from healthy animals of all ages and found a significantly higher prevalence of resistance in dairy herds than in beef herds [26]. As much as 97.8% of all isolates were resistant to fourth-generation cephalosporins, 87.4% were resistant to third-generation cephalosporins, 70.4% to tetracycline, 70.4% to sulfamethoxazole, 47.4% to trimethoprim, 41.5% to ciprofloxacin, 28.9% to chloramphenicol and 23.7% to gentamicin.

In a Chilean study, *E. coli* isolated from healthy dairy calves, calves with diarrhoea and their environment (bedding) showed 92% resistance to amoxicillin, 18.3% to ceftiofur, 27.5% to enrofloxacin, 25.5% to florfenicol, 7.2% to gentamicin, 53.6% to oxytetracycline and 37.5% to trim-sulfa [27]. Nearly half of the isolates (49%) were resistant to three or more substances. In a case-control study from 2012 in healthy and diarrheic calves in Swedish dairy herds, the corresponding figures were 25% resistant to ampicillin, 0% to ceftiofur, 14% to enrofloxacin, 0% to florfenicol, 0% to gentamicin and 32% to tetracycline, with 28% resistant to 3 or more substances [28].

From an international perspective, AMR levels are low in Sweden [8] and this was also confirmed in our study, with AMR figures well below the levels found in the studies cited above. As seen in Figure 5, the overall pattern in the sampled herds reflect a low level of AMR. The highest number of isolates with MIC values above epidemiological cut offs were seen for ampicillin, streptomycin, sulfamethoxazole and tetracycline. However, only a small percentage of colonies grew on the plates supplemented by tetracycline at the cut-off concentration, indicating that the selected isolates with higher MIC values constituted a minority of the *E. coli* present in the samples.

The most common cause for AMU in Swedish dairy herds is mastitis [29]. In 2018/2019, the reported treatment incidence for clinical mastitis in dairy cows was 9.0 recorded treatments per 100 lactations [29]. Benzylpenicillin is the most common drug used and is recorded for more than 90% of reported treatments in the period 2018–2019 [29]. Historically, tetracycline was one of the most commonly used antimicrobial substances in Swedish livestock. In the last decade, the overall sale of tetracycline for veterinary use in Sweden has halved [8]. Still, tetracycline, together with sulfonamides and trimethoprim are among the most frequently used substances in dairy herds, although with treatment incidences at less than 10% of the beta lactam use [29]. Nalidixic acid is a representative for quinolones, enrofloxacin is the only quinolone used in Swedish animals. Enrofloxacin is only recommended for treatment of mastitis if *Klebisella* spp. is confirmed, while for *E. coli* mastitis only supportive therapy is recommended [30]. Since 2012, legislation restricts the use of quinolones and third- and fourth-generation cephalosporins in animals to situations when bacterial culture and susceptibility testing demonstrate that there is no other effective treatment option [30]. The level of quinolone-resistant isolates was low in this study, and the results from selective culturing indicate that the resistant isolates constituted a minority of the *E. coli* in the samples. No treatment of dairy cattle with cephalosporins was reported in the last available statistics from the Swedish dairy association [29]. Only one ESBL-producing isolate out of eight cephalosporin-resistant *E. coli* was detected in this study, which supports the absence of a selective pressure (i.e., exposure to third- or fourth-generation cephalosporins) in the studied herds. Chloramphenicol is not allowed in food-producing animals in Sweden, but florfenicol is registered for use in cattle [31]. Veterinary treatment guidelines, however, do not recommend its use [29], and no treatment of dairy cattle with florfenicol is reported in the available statistics [29]. A total of 28 isolates had MICs above cut off for chloramphenicol. Chloramphenicol can be produced by *Streptomyces venezuelae* in soil and absorbed by grass and crops. If animals are fed roughage and crops/grains that have grown in soil where *Str. venezuelae* is present, it may be a source for chloramphenicol exposure of these animals [32]. Another reason for chloramphenicol resistance in the absence of a selective pressure could be the location of chloramphenicol resistance genes on transferable genetic elements that carry other resistance genes, causing co-resistance to other drugs where a selective pressure may exist. Co-resistance to chloramphenicol and other drug classes commonly used in cattle, such as dihydrostreptomycin and trimethoprim, was reported in bovine *E. coli* in a Japanese study [33]

and co-resistance to tetracycline was found in >92% of chloramphenicol resistant *E. coli* from humans and animals in a US survey [34].

A total of 46 isolates were defined as colistin resistant according to MIC values but no *mcr* gene was detected. Colistin is not used in Swedish cattle [29] and there is no colistin-containing drug preparation registered for use in cattle in Sweden [31]. So far, *mcr* genes constitute the only known transferable genetic element conferring colistin resistance, so the high MIC values may be caused by chromosomal mutations or a yet unidentified mobile genetic element [35], or may be due to the inherent difficulties in MIC analysis for colistin. The microdilution method for colistin MIC determination is challenging, as the concentration of free colistin may be affected by adherence to organic or inorganic material, or the presence of polysorbate in the dilution broth [36]. Within the scope of this study, we cannot determine whether some of the high MIC values were due to a lower than expected concentration of colistin in the MIC plates or whether the isolates were indeed colistin resistant.

The differences in average and median MIC values (see Table 1) are not surprising as the susceptibility testing method uses a series of 2-fold dilutions of the antimicrobial substance and hence a difference in one dilution step will cause a 2-fold difference in MIC. Repeated testing of the same isolate may result in a one-step change in MIC [10] and this should be kept in mind when interpreting results that are close to the cut-off value, regardless of whether ECOFFs or clinical breakpoints are used. AMR levels can be presented in various ways, but the most transparent is showing the MIC distributions, as in Figures 1–3. By showing the distribution of the obtained MIC values when testing a number of non-clinical isolates collected in a systematic manner, it might be possible to discern two phenotypic populations with varying levels of MIC. The epidemiological cut-off points provided by EUCAST are based on a large number of samples and aim to differentiate between the wild type of a bacterial population and isolates with acquired resistance, but there may be overlap in the MIC distributions of these two types of isolates [10]. Hence, cut offs or breakpoints may be regarded as guidance, not an absolute divider between wild-type isolates and isolates with acquired resistance (potentially due to exposure to antimicrobials). The representativeness of the isolates is also an issue, although single isolates from a few animals per herd are regularly used as a basis for illustrating the herd-level AMR pattern, or for national monitoring [7]. The results from our selective culturing, where the resistant isolates constituted a minority of the *E. coli* population in most of the samples, illustrate the challenge of obtaining representative isolates. Methodological variation caused by differences in sample transportation time or different people taking the samples was not expected to have affected the results, and no systematic differences related to these aspects were observed.

AMU contributes to AMR by exerting a selective pressure on bacterial populations, favoring clones carrying resistance traits that protect them from the antimicrobial substances used. The varying patterns of phenotypic resistance traits and sometimes contradictory relationship between AMR and AMU for specific substances seen in this study illustrate the complexity of AMR dynamics. The optimal timeframe for assessing the selective effect of AMU is difficult to pinpoint. We chose to use on farm-collected AMU information for two time periods during the indoor season in order to reflect the overall pattern of AMU in the study herds. The AMU data did not cover the entire time period before sampling but were deemed to be the most accurate reflection of actual on-farm use, not hampered by challenges in reporting [15]. One of the study farms stated to not have used any antibiotics for at least five, maybe as much as 10 years, either in animals (pets included) or people living on the farm. Still, resistant isolates of *E. coli* were found in the samples from this farm, demonstrating the unpredictability of temporal AMR patterns. The association between AMU and AMR may be apparent on a larger scale, although not easily demonstrated on an individual farm level. A recent European study in poultry, pigs and veal calves detected significant associations between on-farm AMR and national AMU for some substances in some herd types but not all [37].

AMR has been detected in bacteria isolated from flies, rats and other animals in farm environments [38], further illustrating the challenges in determining the associations between AMU and AMR. Similar patterns of ESBL-producing *E. coli* have been demonstrated in wild gulls and humans, and it has been suggested that the humans might have served as the source for AMR in animals [39]. A possible route for cattle is from people excreting antimicrobial residues (and resistant bacteria) into effluent [40] and further into surface water that is subsequently consumed by grazing livestock.

In the present study, there was no obvious difference in AMR patterns in organic and conventional herds, indicating that a generally good animal health status and consequent low AMU may have the same effect in controlling AMR as the specific AMU rules for organic production. Although the number of farms and sampling occasions were limited and small differences may have been difficult to discern, the strict regulation of AMU in both production systems in Sweden prompts the question whether other herd-level factors exert a higher influence on AMR patterns in this context. Further studies of the entire farm environment are needed to disentangle the complex web of AMR and its drivers on livestock farms.

#### **5. Conclusions**

No obvious difference in AMR patterns between organic and conventional herds could be detected in this study, most likely due to a generally good animal health status and consequent low AMU in both herd types. The results illustrate the general variation in AMR patterns on a farm level and the challenges in detecting associations with herd management or AMU. Further studies of the entire farm environment are needed to disentangle the complex web of AMR and its drivers on livestock farms.

**Author Contributions:** Conceptualization, K.S., U.E., N.F. and S.S.L.; investigation (fieldwork), K.S., (laboratory methodology and performance), V.T., R.A.H. and J.D.J.; data curation, K.S.; formal analyses, K.S., S.S.L., G.O.A.; writing—original draft preparation and editing, S.S.L.; manuscript review, K.S., V.T., R.A.H., J.D.J., U.E., N.F., G.O.A., S.S.L.; visualization, R.A.H.; supervision, U.E., S.S.L., J.D.J.; project administration, U.E.; funding acquisition, U.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Formas—The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, grant No. 2014-281.

**Acknowledgments:** The authors wish to acknowledge Helle Unnerstad, who contributed significantly to this study with her expertise in antimicrobial susceptibility testing and AMR issues. Sadly, she passed away before the writing of the manuscript. We are grateful to the farmers for taking the time to assist in data collection and allowing access to their farms.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Antimicrobial Resistance Profile of** *Staphylococcus hyicus* **Strains Isolated from Brazilian Swine Herds**

**Andrea Micke Moreno 1,\* , Luisa Zanolli Moreno <sup>1</sup> , André Pegoraro Poor <sup>1</sup> , Carlos Emilio Cabrera Matajira <sup>2</sup> , Marina Moreno <sup>1</sup> , Vasco Túlio de Moura Gomes <sup>1</sup> , Givago Faria Ribeiro da Silva <sup>1</sup> , Karine Ludwig Takeuti <sup>3</sup> and David Emilio Barcellos <sup>3</sup>**


**Abstract:** *Staphylococcus hyicus* is the causative agent of porcine exudative epidermitis. This disorder affects animals in all producing countries and presents a widespread occurrence in Brazil. This study evaluated strains from a historical collection in order to detect the presence of exfoliativetoxin-encoding genes (SHETB, ExhA, ExhB, ExhC, ExhD), characterize the strains using PFGE, and determine their respective antimicrobial resistance profiles. The results obtained from the evaluation of 77 strains from 1982 to 1987 and 103 strains from 2012 reveal a significant change in resistance profiles between the two periods, especially regarding the antimicrobial classes of fluoroquinolones, amphenicols, lincosamides, and pleuromutilins. The levels of multidrug resistance observed in 2012 were significantly higher than those detected in the 1980s. It was not possible to correlate the resistance profiles and presence of genes encoding toxins with the groups obtained via PFGE. Only 10.5% of the strains were negative for exfoliative toxins, and different combinations of toxins genes were identified. The changes observed in the resistance pattern of this bacterial species over the 30-year period analyzed indicate that *S. hyicus* could be a useful indicator in resistance monitoring programs in swine production. In a country with animal protein production such as Brazil, the results of this study reinforce the need to establish consistent monitoring programs of antimicrobial resistance in animals, as already implemented in various countries of the world.

**Keywords:** *Staphylococcus hyicus*; antimicrobial resistance; PFGE; exudative epidermitis; swine

### **1. Introduction**

*Staphylococcus hyicus* is the causative agent of exudative epidermitis in pigs, a generalized cutaneous infection characterized by skin exfoliation, excessive sebaceous secretion, and the formation of a brownish coat of exudate that may cover the entire body [1]. The disease is a widely recognized condition in pigs, especially in suckling and weaned piglets. It has been sporadically reported to cause significant morbidity that can be up to 90% in infected herds, and moderate mortality in naïve herds [2].

*S. hyicus* strains may be considered pathogenic or nonpathogenic according to their ability to induce exudative epidermitis in pigs and their ability to produce the exfoliative toxins, which are the main virulence factors necessary to induce the disease [3]. Until now, five exfoliative toxins from *S. hyicus* were described. SHETB was characterized in Japan [4], and ExhA, ExhB, ExhC, and ExhD were characterized in Denmark [5]. The Exh toxins have been shown to cause a loss of cell adhesion in the epidermis of porcine skin by cleaving desmoglein-1, while human desmoglein-1 is resistant to *S. hyicus* exfoliative toxins [3,6,7].

**Citation:** Moreno, A.M.; Moreno, L.Z.; Poor, A.P.; Matajira, C.E.C.; Moreno, M.; Gomes, V.T.d.M.; da Silva, G.F.R.; Takeuti, K.L.; Barcellos, D.E. Antimicrobial Resistance Profile of *Staphylococcus hyicus* Strains Isolated from Brazilian Swine Herds. *Antibiotics* **2022**, *11*, 205. https:// doi.org/10.3390/antibiotics11020205

Academic Editor: Clair L. Firth

Received: 31 December 2021 Accepted: 30 January 2022 Published: 6 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The disease is frequently treated with antimicrobial agents, but treatment is a problem because of the frequent occurrence of antimicrobial resistance in pig strains and subsequent treatment failure. The frequent occurrence of antimicrobial resistance has been previously reported among *S. hyicus* in different countries [1]; in contrast, there is limited information regarding the distribution of different resistance profiles of *S. hyicus* originating in Brazilian swine.

This investigation evaluated *S. hyicus* strains from Brazilian pigs with exudative epidermitis, examined in two different periods with an interval of 30 years, for the purpose of detecting the presence of genes encoding exfoliative toxins, characterizing the strains using PFGE, and determining the minimal inhibitory concentration of antimicrobial agents against each strain.

#### **2. Materials and Methods**

### *2.1. Bacterial Isolation and Culture Conditions*

A total of 77 *S. hyicus* strains isolated in the 1980s and 103 strains isolated in 2012 were evaluated. The strains were isolated from skin lesions of pigs presenting with exudative epidermitis in 27 swine herds from two states, Rio Grande do Sul and São Paulo. Skin swabs were inoculated onto Tween 80 agar plates and aerobically incubated for 18–24 h at 37 ◦C [8]. Colonies with morphological characteristics of *S. hyicus* were selected and identified using standard biochemical procedures [9] and polymerase chain reaction (PCR). Historical strains were stored in a lyophilized form following isolation, whereas recent strains were stored at −86 ◦C until characterization.

### *2.2. Detection of Genes Encoding Superoxide Dismutase A and Toxins SHETB, ExhA, ExhB, ExhC, and ExhD*

All strains were subjected to species-specific PCR with partial amplification of the *sodA* gene that encodes superoxide dismutase A, as previously described [10], to confirm the identification of *S. hyicus*. Genes encoding toxins SHETB, ExhA, ExhB, ExhC, and ExhD were detected as described in [5,11].

Purified DNA was recovered according to the protocol of Boom et al. [12], with previous enzymatic treatment for 60 min at 37 ◦C, with 100 mg of lysozyme and 20 mg of proteinase K (USBiological, Swampscott, MA, USA). Samples were stored at −20 ◦C until processing.

Polymerase chain reactions (50 µL) comprised 5 µL of genomic DNA, ultrapure water, 10× PCR buffer, 1.5 mM MgCl2, 200 µM of dNTPs, 10 pmol of each primer, and 1.25 U of Taq-DNA-polymerase (Fermentas Inc., Rockville, MD, USA). The amplified products were stained with BlueGreen® (LGC Biotecnologia, São Paulo, Brazil) and separated by electrophoresis using 1.5% agarose gel, using the 100 bp DNA Ladder® (New England Biolabs Inc., Ipswich, MA, USA).

#### *2.3. Molecular Typing by PFGE*

*S. hyicus* strains were grown in brain heart infusion broth for 18–24 h at 37 ◦C. Plug preparation and DNA extraction followed a previously described protocol [13]. The restriction enzyme *SmaI* (New England Biolabs, Ipswich, MA, USA) was used for DNA digestion at 30 ◦C for 24 h. Electrophoresis was performed using 1% SeaKem Gold® agarose (Cambrex Bio Science Rockland, East Rutherford, NJ, USA) and a CHEF-DR III System (Bio-Rad Laboratories, Hercules, CA, USA) with 0.5× TBE at 14 ◦C. DNA fragments were separated at 6 V/cm at a 120◦ fixed angle, with pulse times from 3 to 33 s ramping for 20 h. Gels were stained with a fluorescent DNA stain (SYBR® Safe, Invitrogen Corporation, Carlsbad, CA, USA) for 30 min and imaged under UV transillumination. Lambda DNA-PFGE marker (New England Biolabs, Ipswich, MA, USA) was used for fragment size determination.

#### *2.4. Broth Microdilution*

The minimal inhibitory concentration (MIC) was determined by the broth microdilution technique as recommended by the Clinical and Laboratory Standards Institute [14], using Sensititre™ Standard Susceptibility MIC Plates BOPO6F (TREK Diagnostic Systems/Thermo Fisher Scientific, Waltham, MA, USA). *Staphylococcus aureus* (ATCC 29213) was used as a quality control. The applied breakpoints for interpretation of results were obtained mainly from the CLSI supplement VET08 [14] and are described in Table 1. If interpretive criteria were not present in the VET08 dataset [14], applied breakpoints were calculated using the twenty-eighth edition of the CLSI performance standard M100 [15], and the literature [16,17]. The reported breakpoints were selected with the following order of preference: those described for swine species were favored, then those for *Staphylococcus* spp. (regardless of the animal species or human indication), and in cases where there was no description in the CLSI or EUCAST datasets, a literature reference was used.


**Table 1.** Antimicrobials' MIC range evaluated, and breakpoints applied to *S. hyicus*.

#### *2.5. Statistical Analysis*

The association analysis between resistance profile and strain origin was performed with SPSS 16.0 (SPSS Inc., Chicago, IL, USA), using chi-square and Fisher's exact tests. Statistical significance was considered when *p*-values were less than 0.05. The PFGE fingerprint patterns were analyzed by a comprehensive pairwise comparison of restriction fragment sizes, using the Dice coefficient. The mean values obtained from the Dice coefficient were employed in UPGMA, using BioNumerics 7.6 (Applied Maths NV, Sint-Martens-Latem, Belgium). The isolates were considered from different pulsotypes when they differed by four or more bands [18]. Resistance profiles were analyzed as categorical data with the Dice coefficient, using BioNumerics 7.6 software (Applied Maths NV, Sint-Martens-Latem, Belgium).

#### **3. Results**

All strains from this study were confirmed as *S. hyicus* by PCR. The detection of exfoliative-toxin-encoding genes resulted in the following frequencies: shetB 0%, exhA 34.4% (62/180), exhB 24.4% (44/180), exhC 76.1% (137/180), and exhD 54.4% (98/180). By considering the distribution of toxin genes according to the year of isolation, it was possible to observe a significant increase in the occurrence of the ExhA toxin and a significant reduction in the occurrence of the ExhB toxin between strains from the 1980s and 2012 (Table 2). Only 10.5% (19/180) of strains were negative for all toxin genes, and 13 different profiles were identified according four toxins detected.

**Table 2.** Frequency of strains positive for exfoliative toxins in the two periods evaluated (1980s and 2012).


*p*—probability of the chi-square test or Fisher's exact test (£).

Tests of 180 strains yielded 123 profiles through PFGE with the SmaI enzyme, presenting 9 to 20 fragments with sizes ranging from 40 to 300 kb. Strains showed a similarity greater than 70%, and it was possible to identify 28 pulsotypes. In several cases, pulsotypes grouped strains from the same farm, period of isolation, or state of origin. The dendrogram shown in Figure 1a,b illustrates the results observed in the PFGE analysis.

The observed pulsotypes were denoted as C1 to C28. Several pulsotypes clearly grouped the strains isolated in 2012, such as clusters C1, C2, C18, C19, C20, C23, C25, and C27. Other pulsotypes grouped strains isolated in the 1980s, such as clusters C7, C8, and C21. By considering the farm of origin, it was possible to observe the presence of strains from the same farm clustering into certain groups, such as cluster C17, which contained six strains from farm 18. However, other strains from this farm can also be found in groups C2, C10, and C16. This behavior is repeated in strains from different farms and can be observed most clearly at extreme points of the dendrogram, where strains from farm 15 are allocated into clusters C1, C6, C11, C13, C15, C26, and C27. It was not possible to correlate the resistance profiles, the presence of toxin-encoding genes, and the state of origin with the PFGE clusters.

All strains were subjected to the determination of the minimum inhibitory concentration; the observed resistance rates are presented in Table 3 and Figure 2. It is possible to observe that the resistance pattern changed when comparing strains from the 1980s to 2012 (a period of 30 years), particularly when considering the antimicrobial classes of fluoroquinolones, lincosamides, pleuromutilins, and amphenicols. According to the resistance phenotype, strains were classified into 86 resistance profiles, with 103 strains from 2012 classified into 55 profiles, and 77 strains from 1982 to 1987 into 31 profiles.

Multidrug-resistant strains (resistant to three or more different antimicrobial classes) were found in both assessed groups. However, we observed that the frequency of multidrugresistant strains isolated in 2012 was significantly higher (*p* < 0.001) than that of those isolated between 1982 and 1987 (Table 4). Among multidrug-resistant strains, there was wide variation in the number of antimicrobial classes against which the strains presented resistance between the studied periods. Among the strains from 1982 to 1987, there were no strains which were resistant to more than six antimicrobial classes, whereas in the 2012 group, 25% of tested strains were resistant to more than seven antimicrobial classes. The distribution of MIC values (MIC50 and MIC90) from the historic and 2012 strains is presented in the Supplementary Material (Tables S1 and S2).

(**a**)

**Figure 1.** *Cont*.

**Figure 1.** (**a**) Dendrogram showing the relationship among the *S. hyicus* pulsotypes, resistance profiles, and detection of toxin genes (Part I). (**b**) Dendrogram showing the relationship among the *S. hyicus* pulsotypes, resistance profiles, and detection of toxin genes (Part II).


**Table 3.** Resistance rates of *S. hyicus* from the 1980s and 2012 against tested antimicrobials.

*p*—probability of chi-square or Fisher's exact (£) tests.

**Table 4.** Frequency of *S. hyicus* strains presenting multidrug resistance to antimicrobials according to isolation period.


*p*—probability of the chi-square test.

**Figure 2.** Distribution of MIC values according to antimicrobial tested and period evaluated (1980s or 2012). **Figure 2.** Distribution of MIC values according to antimicrobial tested and period evaluated (1980s or 2012).

#### **4. Discussion**

Exudative epidermitis has been described in swine for over 170 years, and its impact on pig production is observed to this day in different countries worldwide. The analysis of genes encoding exfoliative toxins presented here revealed a high frequency of positive samples for one or more toxins. Few studies have described the frequency of *S. hyicus* toxigenic strains. In a study carried out in Japan, Futagawa-Saito et al. [19] described the following rates: ExhA 35.7% (74/207), ExhB 19.3% (40/207), ExhC 0.5% (1/207), and ExhD 16.9% (35/207). Their results are similar to those observed in this study, except for the ExhC toxin, whose gene was detected more frequently in Brazilian strains (76.1%). Andresen [20] described the detection of genes in 218 *S. hyicus* strains from different countries (across Europe, Japan, and EUA), observing the following frequencies: ExhA 10.6% (23/218), ExhB 5.5% (12/218), ExhC 3.2% (7/218), and ExhD 14.2% (31/218). The increase in the frequency of the ExhA toxin and the reduction in the ExhB toxin observed between the periods evaluated in this study have no similar descriptions in the literature.

Through PFGE, the evaluated strains showed a high genetic diversity, which is characteristic of well-adapted agents which are widely disseminated in the population. *S. hyicus* strains constitute part of the microbiota of healthy animals, even if these strains have been isolated from animals with a clinical picture of epidermitis. Few studies report the application of PFGE in the characterization of *S. hyicus* strains. The most striking association in the different clusters formed in this study is related to the period of isolation. Some pulsotypes stand out for grouping strains isolated in 2012, such as clusters C1 (six strains), C2 (six strains), C18 (five strains), C19 (six strains), C20 (four strains), C22 (six strains), C23 (six strains), C25 (eight strains), and C27 (six strains). That is, 53 of 103 strains from 2012 (51.4%) were clustered according to the isolation period. Other pulsotypes contained only those strains isolated in the 1980s, such as clusters C7 (8 strains), C8 (9 strains), and C21 (4 strains), totaling 21 of 77 strains from the 1980s (27.2%), grouped in common clusters.

It was also possible to observe some groups such as C3, C4, C12, and C15 in which there was a predominance of strains from the 1980s, but with the presence of some recent strains. The strains from certain farms tend to be grouped into specific pulsotypes; however, in all cases, specific isolates from each farm are dispersed at different points on the dendrogram. It was not possible to correlate the resistance profiles and presence of genes encoding toxins with the groups obtained in PFGE.

The MICs of the 180 studied strains were evaluated against 9 antimicrobial classes, and the rising resistance level in several of these classes certainly reflects the increase in antibiotic use in intensive pig production systems in Brazil in recent decades [21]. This indicates that *S. hyicus* could be an important bacterial species for use in antimicrobial resistance monitoring programs in pig production in Brazil, as has been achieved in Denmark [1].

Among the tested beta-lactams, we saw a slight increase in ampicillin and penicillin resistance rates over the 30 years evaluated. The frequency of ceftiofur resistance was low in both groups. This may be related to the high cost of ceftiofur what restricted its use via parenteral administration until 2012. Nevertheless, higher rates of ceftiofur resistance have been described in Canada, where Park et al. [22] described 71% (101/142) of assessed *S. hyicus* strains as ceftiofur resistant in a study conducted on 30 herds. The occurrence of *S. hyicus* resistance to penicillin and ampicillin, or to ampicillin, penicillin, and ceftiofur, and positivity for the presence of the *mecA* gene was also described in the same study and has been considered a risk for swine and human health [22].

A slight decrease in tetracycline resistance rates was also observed in the studied strains. The culmination of the use of tetracyclines in Brazilian swine production was in the 1980s and 1990s. However, a study conducted in 2017 [21] showed that this class was the third most used among 25 Brazilian swine herds, despite the widespread resistance genes among several bacterial species.

The tetracycline resistance rates are quite varied in the literature, according to different countries and studied periods. In Denmark, Wegener et al. [23] reported that 44% (44/100) of *S. hyicus* strains were tetracycline resistant between 1991 and 1992, while Aarestrup and

Jensen [1] reported that only 28.9% (109/377) of Danish *S. hyicus* strains were tetracycline resistant in 2002. In Canada, 55% (79/142) of *S. hyicus* strains were resistant to tetracycline [22], whereas only 1.4% (3/207) of strains were resistant to doxycycline in a Japanese study [19].

The fluoroquinolone resistance levels found were low but represented a significant increase among the studied strains. This change probably reflects the introduction of these antimicrobial in swine production in the 1990s and the selection of resistant strains since then. Our results corroborate previous reports of low levels of enrofloxacin (5.6%) and ciprofloxacin (4.8%) resistance described in Danish *S. hyicus* [1].

The tested aminoglycosides exhibited two distinct resistance patterns: gentamicin and neomycin had low levels of resistance in the 1980s, and these rates have not increased significantly since then, but spectinomycin presented a low resistance rate in the 1980s which has significantly increased as of 2012. Spectinomycin has been added to in-feed formulations for several years to aid the control of enteric infections; this does not occur with neomycin and gentamicin, which are mostly restricted to individual treatments in oral or injectable formulations in Brazil. High resistance rates against spectinomycin (45.1%) have also been described in Canada [22].

The amphenicols currently permitted for use in swine production in Brazil are florfenicol and thiamphenicol. The use of florfenicol in the country began in the 2000s and has intensified since then, especially in in-feed treatment formulations. Resistance levels of the strains isolated in the 1980s were extremely low but demonstrated a large increase in 2012. In a Danish study conducted in 2003, resistance rates to florfenicol were 0% [24]. The sulfonamides, represented by sulfadimethoxine and a combination of trimethoprim/sulfamethoxazole, presented low levels of resistance in strains from the 1980s compared to a slight reduction in 2012. The drop in resistance is probably due to the reduction in the use of sulfonamides in feed production animals in Brazil during some years.

The studied macrolides (tilmicosin, tylosin, and tulathromycin) presented only a slight increase in resistance levels, despite their wide use in the treatment of respiratory and enteric infections in intensive swine production. In the literature, among the macrolides, erythromycin resistance rates were most frequently described with a variation of 15% to 62% between 1991 and 2001, in *S. hyicus* isolated in Denmark [1,23].

For clindamycin, we found high resistance rates in both studied periods. Regarding lincosamide resistance, a 59% (59/100) lincomycin resistance rate was reported in Denmark in 1990 [25]. The combination of lincomycin and spectinomycin has been widely used in recent years to control and prevent respiratory and enteric infections in Brazilian swine production. This could explain the high resistance rates to both of these antimicrobial classes during the studied periods.

The pleuromutilin class, represented by tiamulin, was approved for use in pigs in 1979 in Europe and the United States and was introduced in Brazil in late 1990. The drug is the second most used during the weaning and growing phases, as described by Dutra et al. [21], reinforcing the observed result that tiamulin resistance rates have increased from 0% in the 1980s to 100% of the strains in 2012. In staphylococci, the transferable resistance mechanisms of pleuromutilins have been linked to vga genes, which codify the ABC transporter that exports pleuromutilins, streptogramin A, and lincosamides. There are seven vga resistance genes described thus far (vga A, B, C, D, and E) and all of them are located in plasmids [26]. It is suggested that the use of pleuromutilins may have also favored the selection of cfr-positive *Staphylococcus* of animal origin, and a high frequency of multidrug resistance in strains resistant to pleuromutilins has been observed. Mobile elements containing pleuromutilin resistance genes often contain genes encoding resistance to other antimicrobial classes. Not only does the use of pleuromutilins select strains with this set of resistance genes, but also the use of other antimicrobial classes can select pleuromutilin-resistant strains in a mutual selection process [27]. Considering these risks of cross-selection, in January 2020, the Brazilian government prohibited the use of lincomycin, tiamulin, and tylosin as growth promoters in animals [28].

Given the genetic components related to resistance against the multiple antimicrobial classes tested in this study, and the associations described in the literature among different genes and mobile elements, it becomes easier to understand the observed changes in the resistance profiles of *S. hyicus* strains over the 30-year period studied and the significant increase in the phenomenon of multidrug resistance.

#### **5. Conclusions**

The results described here expand current knowledge about porcine exudative epidermitis in Brazil, as well as painting a portrait of the change in the antimicrobial resistance profiles that can occur over time in a bacterial population. The selection of multidrugresistant *S. hyicus* strains in swine in this 30-year interval suggests that this phenomenon may also be occurring in other Gram-positive bacterial species of greater zoonotic potential such as *S. aureus, Enterococcus faecalis*, or *E. faecium*.

**Supplementary Materials:** The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/antibiotics11020205/s1, Table S1: Distribution of MIC values observed in *S. hyicus* strains isolated in 1980 decade. Table S2: Distribution of MIC values observed in *S. hyicus* strains isolated in 2012.

**Author Contributions:** Conceptualization, A.M.M. and L.Z.M.; methodology, A.M.M., D.E.B., C.E.C.M. and L.Z.M.; formal analysis, L.Z.M. and V.T.d.M.G.; investigation, A.M.M., M.M., G.F.R.d.S. and K.L.T.; resources, A.M.M. and D.E.B.; writing—original draft preparation, A.M.M. and L.Z.M.; writing—review and editing, L.Z.M., D.E.B., C.E.C.M. and A.P.P.; supervision, A.M.M. and D.E.B.; project administration, A.M.M. and L.Z.M.; funding acquisition, A.M.M. and D.E.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo (grant 2011/08541-5). C.E.C.M., V.T.d.M.G., and L.Z.M. are recipients of FAPESP PhD fellowships (grants 2015/26159-1, 2013/16946-0, and 2013/17136-2). A.M.M. is a CNPq fellow (grant 310736/2018-8). L.Z.M. is supported by CNPq (grant 151908/2020-6).

**Institutional Review Board Statement:** This study was approved by the FMVZ-USP ethics committee (protocol code 3083/2013, 07/05/2014).

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank all the farm owners, employees, and veterinarians who allowed access to the studied herds for their support.

**Conflicts of Interest:** The authors declare no conflict of interest.

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