Prevalence and antimicrobial resistance patterns of Salmonella isolated from poultry farms in southeastern United States

Prevalence and antimicrobial resistance patterns of Salmonella isolated from poultry farms in... ABSTRACT Salmonella spp. are among the most common foodborne pathogens, and increase in the occurrence of antimicrobial drug-resistant Salmonella poses a severe risk to public health. The main objective of this study was to determine changes in Salmonella prevalence and their antimicrobial resistance on poultry farms following recommendations to changes in biosecurity practices. Four poultry farms were sampled by collecting cloacal swabs, drag swabs, and litter samples prior to recommended biosecurity changes (March–April) and post recommendations (October–November). Prevalence of Salmonella was 3 to 4% during pre-recommendations, while the prevalence was higher (P > 0.05), ranging from 5 to 14% during post recommendations. Higher Salmonella prevalence was observed for pre- and post-recommendation phases by sample type in cloacal and drag samples −5% for farm 1, drag swab −6% on farm 2, cloacal swab −6% for farm 3, and drag swab −17% on farm 4. The PCR confirmed Salmonella were serotyped and tested for antimicrobial resistance. Six serotypes of Salmonella were identified with S. Enteritidis (52%) being the most prevalent, followed by S. Berta (38%), S. Mbandaka (7%), S. Typhimurium (2%), S. Kentucky (0.4%), and S. Tennessee (0.4%). A total of 7% isolates exhibited resistance to at least one of the 8 antimicrobials. Higher resistance was observed for tetracycline, streptomycin, and nalidixic acid. A single isolate of S. Mbandaka exhibited multidrug resistance to tetracycline, amoxicillin/clavulanic acid, and ampicillin. Based on these prevalence results, it can be inferred that, irrespective of implementation of improved biosecurity practices, seasonal variation can cause changes in the prevalence of Salmonella on the farms. Resistance to clinically important antimicrobials used to treat salmonellosis is a concern to public health. INTRODUCTION Salmonella is an important pathogen highly associated with poultry products such as eggs and chicken meat (USDA-FSIS, 2016a). Increasing prevalence of multidrug resistance (MDR) in Salmonella serotypes of both animal and human origin and, in particular, resistance to clinically important antimicrobials is an emerging concern, worldwide. The intensity and magnitude to resistance vary globally and are influenced by the use of antimicrobials in both human and veterinary medicine and geographical variations in the epidemiology of Salmonella infections (Zhao et al., 2006). Antimicrobial resistant (AMR) bacteria or AMR genes can be transferred from animals to humans through contact with animals or the food chain by consumption of products from food-producing animals colonized with this type of bacteria (Jindal et al., 2015). The United States is the largest producer of poultry, and the second largest exporter of poultry meat in the world (USDA, 2012). Poultry is the most consumed meat product in the United States, and outbreaks of Salmonella due to consumption of undercooked or contaminated poultry products are of high risk to human health (USDA-FSIS, 2016a). In the United States, foodborne illnesses due to Salmonella are the leading cause of hospitalizations and deaths, and the second highest cause of illnesses (USDA-FSIS, 2016a). The Centers for Diseases Control and Prevention (CDC) reports that AMR poses an economic burden of $35 billion to society because of a reduction in productivity and $20 billion related to healthcare costs (CDC, 2013b) annually in the United States. Foodborne illnesses caused by AMR bacteria result in longer stays at hospitals largely due to initial treatment failures (Acheson and Hohmann, 2001), highlighting the need to address the issue. In 2013, the U.S. Department of Agriculture (USDA) estimated a consumption of 57.7 pounds of chicken per person (USDA-ERS, 2015). Poultry products are often associated with Salmonella, and the spread of Salmonella resistant to nalidixic acid, azithromycin, ampicillin, and trimethoprim-sulfafmethoxazole, which are important antimicrobial agents used for clinical treatments (CDC, 2015), are of concern. In response to increasing prevalence of AMR bacteria, the CDC, FDA, USDA, and local and state health departments have created an interagency called the National Antimicrobial Resistance Monitoring System (NARMS) for tracking AMR bacteria associated with raw meat and poultry, humans, and food-producing animals (CDC, 2014). Several reports are available on the prevalence and AMR of Salmonella in poultry products and poultry processing plants (Poppe et al., 2001; Nayak and Kenney, 2002; Logue et al., 2003; Jain and Chen, 2006). However, little information is available on the prevalence and antimicrobial resistant profiles of Salmonella during pre-and post implementation of biosecurity practices and changes made in biosecurity practices on poultry farms. Restrictions on the use of antimicrobials that are highly important for human medicine, stringent regulations in veterinary medicine, intensifying collaborations between human and veterinary medicine, discovering new antimicrobials, and establishing surveillance systems for a prompt detection of AMR are some of the actions and initiatives taken from international entities in recent yr (Paphitou, 2013). Therefore, the objectives of this study were to determine the prevalence of Salmonella during pre- and post-recommended changes in biosecurity practices and to characterize the Salmonella serotypes and their antimicrobial profiles. MATERIALS AND METHODS Sampling Plan A survey of 120 broiler farms was conducted to determine the management and biosecurity practices on each farm (English, 2015). Based on the responses (32/120), 4 poultry farms were selected for our research with similar characteristics, such as ventilation and size as described by English (2015). All farms were sampled for Salmonella at 2 stages: once before a series of biosecurity recommendations and a second sampling after the recommendations were made and implemented for 6 weeks. Recommendations were based on results from the survey, taking into account lack of practices or programs, such as rodent and insect control or supervision of visitors for both houses and farms. These recommendations consisted of visitors changing clothing before entering the farm, showering before and after entering the farm, maintaining records of entry and exit to the farm, recording house entry, implementing a rodent and insect control program, and the use of coveralls and shoe covers. The pre-recommendation phase sampling was performed during March and April, while the post-recommendation phase sampling was performed during October and November. Each sampling phase had a total of 3 samplings, which were performed on d 1, d 14, and d 30 after the chicks were placed on the farms. Each farm was comprised of 4 houses, which were divided into 4 quadrants across the width so litter and drag samples could be taken in each quadrant. Twelve cloacal samples were taken from the center of the house. This resulted in a total of 16 drag swab samples, 16 litter samples, and 48 cloacal swab samples from each farm. Sampling Method Sterile drag swabs (Solar-Cult, Ogdensburg, NY) were used to collect environmental samples from each house. The first drag swab was dragged between the first water line and house wall starting from the middle of the house surrounding the water line to end at the middle of the house. This procedure was followed in the other half of the house surrounding the same water line to get a second sample. Samples 3 and 4 were obtained by following the same procedure on the second water line. Polyester-tipped applicators (Puritan, Guilford, ME) were used for cloacal swabs, which were pre-moistened with buffered peptone water (BPW; Criterion, Santa Maria, CA). Samples were then inoculated into 5 mL of BPW. Each litter sample was composed of 3 subsamples of 100 g taken from different locations in the same quadrant of the house, combined and mixed by hand. All samples were stored on ice until further analysis. Salmonella Enrichment Salmonella enrichment for drag swabs was performed by adding 20 mL of Tetrathionate Brilliant Green Broth (TTB; Himedia, Mumbai, India) to the sample, vortexed, and incubated at 37°C for 24 hours. One milliliter of BPW containing the cloacal swabs was added to 20 mL of TTB and incubated at 37°C for 24 h for Salmonella enrichment. Both drag and cloacal swab enrichments were streaked onto Xylose Lysine Tergitol-4 agar (XLT4; BD, Sparks, MD) and incubated at 37°C for 24 hours. Additionally, 90 mL of phosphate buffered saline (PBS) were added to 10 g of litter sample in 15.2 × 22.9 cm filter bags (VWR Sterile Sampling Bag, VWR, Radnor, PA), stomached for 60 s, enriched, and isolated similar to the swab samples. Black colonies, indicating H2S production, were subjected to a precipitation test with antiserum (DIFCO™ Salmonella O Antiserum Poly A—I and Vi; BD, Sparks, MD) for Salmonella confirmation. Presumptive Salmonella were shipped to the Department of Food Science at Purdue University in 1 mL of tryptic soy agar tubes containing a single isolated colony. Upon receiving, each isolate was grown in 10 mL Tryptic Soy Broth (TSB) (Acumedia; Lansing, MI) and incubated at 37°C for 24 hours. After incubation, each sample was streaked for isolation onto XLT4 plates and incubated at 37°C for 24 hours. A single isolated colony was picked from the XLT4 plates, based on typical Salmonella colony characteristics and further grown in micro-centrifuge tubes (Costar; Corning, NY) containing 1 mL of TSB and incubated at 37°C for 24 hours. This was followed by centrifugation at 5,000 X g for 3 minutes. (Thermo Scientific; Model: Sorvall Legend Micro 17 Centrifuge, Waltham, MA). The supernatant was removed, and the pellet was re-suspended in 1 mL of 20% glycerol, which was finally mixed with sterile beads and cryopreserved at −80°C for further analysis. DNA Extraction and PCR Amplification A boiling method was used to extract DNA based on Ngamwongsatit et al. (2008). After incubation, each micro-centrifuge tube was centrifuged at 5,000 × g for 2 minutes. The supernatant was removed and the pellet was re-suspended in 500 μL of DNAse free water (Life Technologies; Grand Island, NY) and vortexed. Following this, the micro-centrifuge tube was heated in a dry bath at 100°C for 10 minutes. After boiling, the micro-centrifuge tube was centrifuged at 10,000 × g for 5 min, and the supernatant was removed and placed in a sterile micro-centrifuge tube. The purity of DNA was evaluated based on protein/DNA ratio of absorbance (A260/A280; Epoch spectrophotometer, Biotek, Winooski, VT), and the purity standard was a A260/A280 ratio between 1.8 and 2.0. Quantification of DNA was performed for results expecting a concentration of 10 to 100 ng, which was present in all the samples. After quantification, the DNA was stored at −20°C until further use. A total of 595 presumptive Salmonella isolates was subjected to PCR using a primer set based on the hilA gene with an amplicon of 784 bp (Panthamanathan et al., 2003). For the reaction, GoTaqR Green Master Mix (Promega Corp., Madison, WI) with a concentration of 0.7X was mixed with a concentration of 0.5 μM of each forward and reverse primers. DNA was diluted based on quantification results to obtain a DNA template in a concentration of 10 to 100μg and DNAse free water to make the volume of 25 μL for each reaction. For the negative control, DNAse free water was used, and for the positive control, a DNA template of a known Salmonella strain (Salmonella Typhimurium ATCC 700720) was used. Reactions were performed with a QuantStudio (ThermoFisher Scientific, Carlsbad, CA) using different parameters with an initial denaturation cycle at 94°C for 5 min, followed by a denaturation at 94°C for 30 s, an annealing temperature of 65°C for 30 s with 25 cycles, and ending with an extension temperature of 72°C for 10 minutes. After completion of each cycle, amplicons were gel electrophoresed to observe an amplicon of 784 bp. Serotyping and Antimicrobial Resistance Analysis All 262 PCR-confirmed Salmonella isolates were sent to the National Veterinary Service Laboratory (NVSL, Ames, IA) for serotyping. Salmonella serotyping was based on agglutination for antisera where O antigens, H antigens phase 1, and H antigens phase 2 were used as a formula to identify serotypes based on the Kauffman–White scheme. PCR-confirmed isolates were analyzed for antimicrobial resistance using the CMV3AGNF SensititreTM Gram negative plate (product no. YCMV3AGNF; Remel; Lenexa, KS), which allowed testing for resistance against cefoxitin (FOX), azithromycin (AZI), chloramphenicol (CHL), tetracycline (TET), ceftriaxone (AXO), amoxicillin/clavulanic acid 2:1 (AUG2), ciprofloxacin (CIP), gentamicin (GEN), nalidixic acid (NAL), ceftiofur (XNL), sulfisoxazole (FIS), trimethoprim/sulfamethoxazole (SXT), ampicillin (AMP), and streptomycin (STR). The analysis was performed using a previously described broth micro-dilution method (Trek Diagnostics, Waltham, MA), and minimum inhibitory concentration (MIC) breakpoints for determining resistance or susceptibility were based on those published by the Clinical Laboratory Standards Institute (CLSI, 2014) and NARMS (FDA, 2011). Each isolate was inoculated into 4 mL of demineralized water (Thermo Fisher) by picking 3 to 5 colonies from an overnight culture on tryptic soy agar (TSA; Neogen, Lansing, MI) to visually match a McFarland turbidity standard of 0.5. Next, 10 μL of the suspension were transferred to a tube of 11 mL cation adjusted Mueller–Hinton broth with TES buffer (CAMHBT) (Thermo Fisher) to create an inoculum containing 1 × 105 CFU/mL. This inoculum was transferred into a sterile reagent reservoir (Corning, Corning, NY) and then 50 μL were inoculated into each well of a SensititreTM plate. Inoculated plates were covered with an adhesive seal and incubated for 24 h at 35°C. The MIC was recorded as the minimum concentration that a particular antimicrobial completely inhibited growth, except for the sulfonamide drugs FIS and SXT, in which MIC was recorded as the concentration allowing 10 to 20% growth compared to the control. With each batch of SensititreTM plates prepared, a plate containing a quality control organism with known MIC values (E. coli ATCC 25922) was prepared. If quality control MIC values did not fall within the range of known MIC values, results from accompanying plates were disregarded, and corresponding isolates were retested. Data Analysis Results of prevalence obtained during pre- and post recommendation were compared on each farm to establish statistical significances between the 2 phases. A similar procedure was followed to establish differences in AMR prevalence for each farm. Since prevalence data were obtained in percentages (discrete dataset), an arcsine transformation was performed to obtain a continuous dataset and decrease variability (Warton and Hui, 2011). Differences were analyzed using comparative analysis (t test) where P ≤ 0.05 was used to determine significant differences. All data were analyzed using the statistical program SAS 9.3 version (SAS, Cary, NC). RESULTS AND DISCUSSION Salmonella Prevalence Presumptive Salmonella isolates were confirmed by PCR, and the results are shown in Table 1. No differences (P > 0.05) in the prevalence of Salmonella spp. were observed between the pre- and post-recommendation phases in our study, except for farm 4 where a higher (P < 0.05) prevalence was observed at the post-recommendation phase. For prevalence of Salmonella measured by the presence of this pathogen at house level on each farm, positive results for Salmonella were present in 3 out of 4 houses on each farm (75% prevalence) during the pre-recommendation phase, while during the post-recommendation phase, the prevalence was observed to be 100% (data not shown). These results are consistent with reports from Alali et al. (2010), in which Salmonella was present on all 4 broiler farms tested for Salmonella prevalence. Results similar to our study also have been reported by Mathole et al. (2017), in which cloacal swabs obtained from 286 chickens in South Africa suggested a 3.15% prevalence of Salmonella. In our study, differences in prevalence between farms at pre- and post-recommendation phases can be attributed to seasonal variation, possible irregular management of farms, and differences in employees and their practices. Houses were cleaned out during spring, which also could explain the lower levels of Salmonella during the pre-recommendation phase compared to the prevalence during the post-recommendation phase. These findings are consistent with reports by Bailey et al. (2001), in which it was suggested that Salmonella prevalence was greater in fall as compared to spring and summer. Entry of pathogens into poultry and level of infections can be controlled via biosecurity practices and cleaning, mainly using an all-in/all-out approach, as one of the most important sources for contamination consists of sick animals (Ethelberg et al., 2014). The biosecurity improvements recommended in our study were based on the survey results from English (2015) and included a change of clothing for visitors before entering the houses, shoe cover use, treatment of litter between flocks, implementation of a rodent and insect control program, restricted entry of personnel, and registration of entry and exit to the farms. According to results in this study, the biosecurity measures recommended were not effective (P > 0.05) in minimizing Salmonella prevalence on the farms. This can be attributed to several factors, such as seasonal variations and variation in the implementation of the recommended biosecurity practices. Laanen et al. (2014) reported that increased cost for implementing biosecurity measures, along with a lack of rewards for producers and communication on how to implement biosecurity measures, were among the top 5 reasons for the lack of implementation of biosecurity measures. However, there have been other documented studies reporting that the cost of implementing biosecurity practices can be effective in the long term (Wegener et al., 2003). This study was conducted in Denmark, and it was reported that Salmonella controls resulted in an increase by $0.02/Kg of broilers (Wegener et al., 2003); the cost of retail broilers is reported to be U.S. $4.12/Kg (USDA, 2016b). If control practices recommended by the study in Denmark were applied, the cost to control Salmonella could result in a 0.5% increase of the retail price. Thus, informing producers about the cost and benefits can have a positive impact on the farmers’ perspectives, therefore facilitating the process of implementation. Table 1. Prevalence (%) of Salmonella spp. from farms during the pre- and post-recommendation phases. % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B A,BDifferent superscripts represent significant differences between the sampling phases (P < 0.05) within a farm. View Large Table 1. Prevalence (%) of Salmonella spp. from farms during the pre- and post-recommendation phases. % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B A,BDifferent superscripts represent significant differences between the sampling phases (P < 0.05) within a farm. View Large As evident, implementation of biosecurity practices in poultry are important for prevention or control of pathogens, one of the recommendations made in our study was rodent control, which is essential, since rats can contaminate disinfected houses with Salmonella (Lister, 2008). Recommendations on hygiene and disinfection were not made; however, Wales et al. (2006) demonstrated that while Salmonella eradication was not possible, there was a decrease in the prevalence of this pathogen after dry cleaning. Spread of pathogens between farms can take place when farms are close to each other through vectors such as insects, rodents, and birds, as well as humans (Albihn and Vinnerås, 2007). Salmonella Serotypes Figure 1 represents the total distribution of Salmonella found on all 4 farms, irrespective of recommendation phases. Among all Salmonella, 6 different serotypes were identified: S. Enteritidis (52%), S. Berta (38%), S. Mbandaka (7%), S. Typhimurium (2%), S. Kentucky (0.4%), and S. Tennessee (0.4%). Observations from our study are similar to reports by Roy et al. (2002), in which S. Kentucky, S. Enteritidis, S. Mbandaka, S. Berta, S. Enteritidis, and S. Typhimurium were the common serotypes from poultry, poultry products, and poultry environment. On farm 1 (Figure 2A), S. Enteritidis was observed to be the most common serotype in the pre- and post-recommendation phases. S. Typhimurium was the second most prevalent (32%) serotype during the pre-recommendation phase, while no S. Typhimurium was found in the post-recommendation phase. New serotypes were observed with low prevalence in the post-recommendation phase. Farm 1 showed the highest diversity of Salmonella serotypes. For farm 2 (Figure 2B) during the pre-recommendation phase, the only serotype present was S. Enteritidis, which is also the most prevalent in the post-recommendation phase followed by S. Berta and S. Mbandaka. Farm 3 (Figure 2C) showed similar behavior to farm 2, where S. Enteritidis is the only serotype present in the pre-recommendation phase. Unlike the other farms, farm 4 (Figure 2D) is mostly associated with S. Berta during the post-recommendation phase. The only S. Tennessee found among all Salmonella isolates is found in the post-recommendation phase on farm 4. Overall, S. Enteritidis was observed to be most prevalent on farms 1, 2, and 3, while S. Berta was the most prevalent on farm 4 during the post-recommendation phase. With well-documented information about S. Enteritidis being the most common serotype found on poultry farms, mitigation efforts are usually focused on this serotype. However, from our study, it is evident that S. Berta is the most prevalent serotype on farm 4, and mitigation strategies used for S. Enteritidis might not be effective against other highly prevalent Salmonella serotypes. Furthermore, given the inconsistencies in prevalence rates and serotypes on different farms, it can be challenging to control Salmonella and develop robust biosecurity measures for implementation on the farms. Figure 1. View largeDownload slide Distribution of Salmonella spp. on farms during the pre- and post-recommendation phases. Figure 1. View largeDownload slide Distribution of Salmonella spp. on farms during the pre- and post-recommendation phases. Figure 2. View largeDownload slide Prevalence (%) of Salmonella serotypes on farms during the pre- and post-recommendation phases: (A) Farm 1; (B) Farm 2; (C) Farm 3; and (D) Farm 4. Figure 2. View largeDownload slide Prevalence (%) of Salmonella serotypes on farms during the pre- and post-recommendation phases: (A) Farm 1; (B) Farm 2; (C) Farm 3; and (D) Farm 4. Based on the type of samples collected, cloacal swab samples had the highest prevalence of S. Enteritidis during pre- and post-recommendation phases (Table 2). It was observed that S. Enteritidis was consistently isolated from cloacal swabs, drag swabs, and litter samples during both phases. S. Berta, which was most prevalent during the post-recommendation phase, was isolated from cloacal and drag swabs from all 4 farms and only on the litter samples on farm 4 (Table 2). S. Mbandaka was distributed on all farms only during the post-recommendation phase in the cloacal swabs, in the drag swabs on farm 4, and in the litter samples on farm 4. S. Typhimurium was isolated only from farm 1 in the drag swabs and litter samples, while S. Kentucky and S. Tennessee were isolated from drag swabs on farm 1 and 4, respectively (Table 2). Incidence of S. Typhimurium was associated with litter and drag swabs samples on farm 1 during the pre-recommendation phase, which is contrary to reports by Singh et al. (2013), in which S. Typhimurium was isolated from cloacal samples with a prevalence of 4.4% (8/180) in layer chickens. Three out of the 6 serotypes found in this study are in the list of top 20 most common serotypes associated with public health (CDC, 2011). Based on the CDC Atlas (CDC, 2013a), S. Berta, S. Enteritidis, S. Mbandaka, and S. Typhimurium were isolated from humans from 1968 to 2011. Additionally, S. Berta was associated with chicken: 52% (577/1,305) of clinical sources and 65% (810/1246) of non-clinical sources; S. Enteritidis with a 50% (15,526/30,880) incidence on chicken from clinical sources and 83% (5,513/6,677) for non-clinical sources; and S. Mbandaka was isolated from chicken 30% (1,660/5,484) for clinical sources and 49% (1,248/2,571) of non-clinical sources (CDC, 2013a). Therefore, knowing actual serotypes that are present on farms is important to create control programs to decrease or eliminate relevant serotypes. Table 2. Prevalence (%) of Salmonella serotypes by sample type on each farm at the pre- and post-recommendation phases. Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 *Number of Salmonella with the prevalence % shown in parentheses. View Large Table 2. Prevalence (%) of Salmonella serotypes by sample type on each farm at the pre- and post-recommendation phases. Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 *Number of Salmonella with the prevalence % shown in parentheses. View Large Antimicrobial Resistance All isolates positive for Salmonella were subjected to AMR testing. No differences (P > 0.05) were observed in the AMR of Salmonella spp. during pre- and post-recommendation phases within each farm (Table 3). Resistance patterns, sample type, and serotypes of Salmonella are presented in Table 4. It was observed that 63% (12/19) of Salmonella were resistant to TET, followed by 26% (5/19) being resistant to STR and 21% (4/19) to NAL, irrespective of the farms from which the Salmonella originated and the recommendation phase. In contrast, lower resistance (5%; 1/19) was observed for AXO, AZI, CHL, AMP, and AUG2. For all Salmonella, it was observed that 68% (13/19) were resistant to at least one antimicrobial class, 26% (5/13) were resistant to 2 classes, and 5% (1/19) were resistant to 3 antimicrobial classes, which were classified as multidrug resistant (MDR). From the 13 isolates with resistance to one antimicrobial class, 54% (7/13) were resistant to TET, 23% (3/13) were resistant to NAL from the quinolones class, 15% (2/13) were resistant to STR from the aminoglycosides class, and the remaining 8% (1/13) were resistant to CHL, which belongs to the phenicols antimicrobial class. The most prevalent serotype among the resistant Salmonella was S. Enteritidis with 42%, followed by S. Berta (37%) and S. Mbandaka with 11 and 5% each for S. Typhimurium and S. Kentucky. Cloacal samples were most often antimicrobial resistant (47%), followed by 42% of drag swabs and 11% associated with litter sample from which one was the MDR isolate (Table 4). According to NARMS reports on distribution of Salmonella serotypes found in retail chicken, the most prevalent serotype is S. Typhimurium with 43%, followed by S. Enteritidis in 13% of the samples, S. Kentucky in 28% of the samples, and S. Mbandaka in 2% of samples. No S. Berta was found in retail chicken meat samples (FDA, 2013). Table 3. Antimicrobial resistance of Salmonella on 4 different farms. Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A ASame letters represent no significant differences between seasons (P > 0.05). View Large Table 3. Antimicrobial resistance of Salmonella on 4 different farms. Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A ASame letters represent no significant differences between seasons (P > 0.05). View Large Table 4. Antimicrobial resistance patterns@ of Salmonella from poultry farms. Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta *TET = Tetracycline; AUG2 = Amoxicillin/Clavulanate; AMP = Ampicillin; STR = Streptomycin; AXO = Ceftriaxone; NAL = Nalidixic Acid; AZI = Azithromycin; CHL = Chloramphenicol. @12/19 (63%) were resistant to TET; 5/19 (26%) were resistant to STR; 4/19 (21%) were resistant to NAL; and 1/19 (5%) were resistant to AXO, AZI, CHL, AMP, and AUG2. View Large Table 4. Antimicrobial resistance patterns@ of Salmonella from poultry farms. Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta *TET = Tetracycline; AUG2 = Amoxicillin/Clavulanate; AMP = Ampicillin; STR = Streptomycin; AXO = Ceftriaxone; NAL = Nalidixic Acid; AZI = Azithromycin; CHL = Chloramphenicol. @12/19 (63%) were resistant to TET; 5/19 (26%) were resistant to STR; 4/19 (21%) were resistant to NAL; and 1/19 (5%) were resistant to AXO, AZI, CHL, AMP, and AUG2. View Large Alali et al. (2010) reported that 6.9% of Salmonella isolates were resistant to TET, 91.4% were resistant to STR, 55.2% were resistant to AUG2, and 56.9% were resistant to AMP. Singh et al. (2013) reported 23% of the Salmonella were resistant to TET and CHL, and 11.5% were resistant to STR. Differences in the prevalence of AMR of Salmonella can be attributed to the number of isolates tested, as well as the selection process. In our study, all PCR confirmed Salmonella were subjected to antimicrobial susceptibility in contrast to Alali et al. (2010), in which 58 isolates out of 115 isolates were tested against AMR. One isolate out of the 19 antimicrobial resistant isolates demonstrated resistance to TET and AZI. On a limited basis, the use of AZI to treat Salmonella infections has been studied, suggesting its use as an alternate antimicrobial to treat these infections (Acheson and Hohman, 2001). Nalidixic acid is categorized as a quinolone, an antimicrobial class that is known to be one of the first-choice antimicrobials to treat salmonellosis. Resistance in 23% of the isolates to this antimicrobial agent is concerning, due to its importance in human medicine. Isolates with resistance to this antimicrobial agent are likely to develop resistance to other antimicrobial agents that also belong to the quinolones class (Sárközy, 2001). A resistance pattern to NAL and AXO was observed in only one of the Salmonella isolates in our study. When bacterial strains exhibit resistance to an antimicrobial agent of any particular antimicrobial class, resistance to other antimicrobial agents from that same class is likely to occur (Tenover, 2006). In this study, the antimicrobials of quinolones classes tested were NAL and CIP, where resistance to NAL was observed but not CIP. Although NAL and CIP belong to the quinolones class, resistance to the first antimicrobial is developed with a single chromosomal point mutation, whereas resistance to CIP generally requires at least 2 chromosomal mutations (Crump et al., 2003). Quinolones are often a first choice to treat Salmonella infections; CIP exclusively is the first option to treat salmonellosis in adults (Mølbak, 2005). Although resistance to NAL occurs without predisposing these same isolates to CIP resistance, this still represents a risk to human health. Aminoglycosides are another class of antimicrobials that are commonly used in the poultry industry. In our study, resistance to GEN was not observed, while resistance to STR was seen and can be the result of use of this antimicrobial agent against necrotic enteritis, fowl cholera, or Staphylococcus spp. (Landoni and Albarellos, 2015). While resistance to CHL was observed to be low in our study, the use of this agent can increase in human medicine when access to cephalosporins or cephems is limited (Collignon et al., 2009). In our study, no specific association between AMR patterns and Salmonella serotypes was observed, which is in agreement with Álvarez-Fernández et al. (2012). CONCLUSIONS Results from this study demonstrate that recommendations on management and biosecurity practices on poultry farms have very limited success for Salmonella control if not implemented correctly. Among all Salmonella isolated from the farms, S. Enteritidis was the most prevalent, followed by S. Berta. All farms followed similar trends of having S. Enteritidis as the only serotype present during the pre-recommendation phase, and the presence of new serotypes during the post-recommendation phase was observed. Resistance to first-choice antimicrobials to treat salmonellosis was not observed; however, resistance to clinically important antimicrobials such as AXO, NAL, and AZI was exhibited by Salmonella. Antimicrobial resistance was found to be different on each farm and among farms. This study contributes to AMR studies and the presence of AMR foodborne pathogens in poultry. Further studies such as DNA fingerprinting could be carried out to determine if the AMR isolates are persistent, and to determine if there is cross-contamination among farms. Additional studies could be performed to determine sources of AMR genes and how they are being transferred among bacteria. REFERENCES Acheson D. , Hohmann E. L. . 2001 . Nontyphoidal salmonellosis . Clin. Infect. Dis. 32 : 263 – 269 . Google Scholar CrossRef Search ADS PubMed Alali W. 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Poultry Science Oxford University Press

Prevalence and antimicrobial resistance patterns of Salmonella isolated from poultry farms in southeastern United States

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Oxford University Press
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© 2018 Poultry Science Association Inc.
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0032-5791
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1525-3171
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10.3382/ps/pex449
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Abstract

ABSTRACT Salmonella spp. are among the most common foodborne pathogens, and increase in the occurrence of antimicrobial drug-resistant Salmonella poses a severe risk to public health. The main objective of this study was to determine changes in Salmonella prevalence and their antimicrobial resistance on poultry farms following recommendations to changes in biosecurity practices. Four poultry farms were sampled by collecting cloacal swabs, drag swabs, and litter samples prior to recommended biosecurity changes (March–April) and post recommendations (October–November). Prevalence of Salmonella was 3 to 4% during pre-recommendations, while the prevalence was higher (P > 0.05), ranging from 5 to 14% during post recommendations. Higher Salmonella prevalence was observed for pre- and post-recommendation phases by sample type in cloacal and drag samples −5% for farm 1, drag swab −6% on farm 2, cloacal swab −6% for farm 3, and drag swab −17% on farm 4. The PCR confirmed Salmonella were serotyped and tested for antimicrobial resistance. Six serotypes of Salmonella were identified with S. Enteritidis (52%) being the most prevalent, followed by S. Berta (38%), S. Mbandaka (7%), S. Typhimurium (2%), S. Kentucky (0.4%), and S. Tennessee (0.4%). A total of 7% isolates exhibited resistance to at least one of the 8 antimicrobials. Higher resistance was observed for tetracycline, streptomycin, and nalidixic acid. A single isolate of S. Mbandaka exhibited multidrug resistance to tetracycline, amoxicillin/clavulanic acid, and ampicillin. Based on these prevalence results, it can be inferred that, irrespective of implementation of improved biosecurity practices, seasonal variation can cause changes in the prevalence of Salmonella on the farms. Resistance to clinically important antimicrobials used to treat salmonellosis is a concern to public health. INTRODUCTION Salmonella is an important pathogen highly associated with poultry products such as eggs and chicken meat (USDA-FSIS, 2016a). Increasing prevalence of multidrug resistance (MDR) in Salmonella serotypes of both animal and human origin and, in particular, resistance to clinically important antimicrobials is an emerging concern, worldwide. The intensity and magnitude to resistance vary globally and are influenced by the use of antimicrobials in both human and veterinary medicine and geographical variations in the epidemiology of Salmonella infections (Zhao et al., 2006). Antimicrobial resistant (AMR) bacteria or AMR genes can be transferred from animals to humans through contact with animals or the food chain by consumption of products from food-producing animals colonized with this type of bacteria (Jindal et al., 2015). The United States is the largest producer of poultry, and the second largest exporter of poultry meat in the world (USDA, 2012). Poultry is the most consumed meat product in the United States, and outbreaks of Salmonella due to consumption of undercooked or contaminated poultry products are of high risk to human health (USDA-FSIS, 2016a). In the United States, foodborne illnesses due to Salmonella are the leading cause of hospitalizations and deaths, and the second highest cause of illnesses (USDA-FSIS, 2016a). The Centers for Diseases Control and Prevention (CDC) reports that AMR poses an economic burden of $35 billion to society because of a reduction in productivity and $20 billion related to healthcare costs (CDC, 2013b) annually in the United States. Foodborne illnesses caused by AMR bacteria result in longer stays at hospitals largely due to initial treatment failures (Acheson and Hohmann, 2001), highlighting the need to address the issue. In 2013, the U.S. Department of Agriculture (USDA) estimated a consumption of 57.7 pounds of chicken per person (USDA-ERS, 2015). Poultry products are often associated with Salmonella, and the spread of Salmonella resistant to nalidixic acid, azithromycin, ampicillin, and trimethoprim-sulfafmethoxazole, which are important antimicrobial agents used for clinical treatments (CDC, 2015), are of concern. In response to increasing prevalence of AMR bacteria, the CDC, FDA, USDA, and local and state health departments have created an interagency called the National Antimicrobial Resistance Monitoring System (NARMS) for tracking AMR bacteria associated with raw meat and poultry, humans, and food-producing animals (CDC, 2014). Several reports are available on the prevalence and AMR of Salmonella in poultry products and poultry processing plants (Poppe et al., 2001; Nayak and Kenney, 2002; Logue et al., 2003; Jain and Chen, 2006). However, little information is available on the prevalence and antimicrobial resistant profiles of Salmonella during pre-and post implementation of biosecurity practices and changes made in biosecurity practices on poultry farms. Restrictions on the use of antimicrobials that are highly important for human medicine, stringent regulations in veterinary medicine, intensifying collaborations between human and veterinary medicine, discovering new antimicrobials, and establishing surveillance systems for a prompt detection of AMR are some of the actions and initiatives taken from international entities in recent yr (Paphitou, 2013). Therefore, the objectives of this study were to determine the prevalence of Salmonella during pre- and post-recommended changes in biosecurity practices and to characterize the Salmonella serotypes and their antimicrobial profiles. MATERIALS AND METHODS Sampling Plan A survey of 120 broiler farms was conducted to determine the management and biosecurity practices on each farm (English, 2015). Based on the responses (32/120), 4 poultry farms were selected for our research with similar characteristics, such as ventilation and size as described by English (2015). All farms were sampled for Salmonella at 2 stages: once before a series of biosecurity recommendations and a second sampling after the recommendations were made and implemented for 6 weeks. Recommendations were based on results from the survey, taking into account lack of practices or programs, such as rodent and insect control or supervision of visitors for both houses and farms. These recommendations consisted of visitors changing clothing before entering the farm, showering before and after entering the farm, maintaining records of entry and exit to the farm, recording house entry, implementing a rodent and insect control program, and the use of coveralls and shoe covers. The pre-recommendation phase sampling was performed during March and April, while the post-recommendation phase sampling was performed during October and November. Each sampling phase had a total of 3 samplings, which were performed on d 1, d 14, and d 30 after the chicks were placed on the farms. Each farm was comprised of 4 houses, which were divided into 4 quadrants across the width so litter and drag samples could be taken in each quadrant. Twelve cloacal samples were taken from the center of the house. This resulted in a total of 16 drag swab samples, 16 litter samples, and 48 cloacal swab samples from each farm. Sampling Method Sterile drag swabs (Solar-Cult, Ogdensburg, NY) were used to collect environmental samples from each house. The first drag swab was dragged between the first water line and house wall starting from the middle of the house surrounding the water line to end at the middle of the house. This procedure was followed in the other half of the house surrounding the same water line to get a second sample. Samples 3 and 4 were obtained by following the same procedure on the second water line. Polyester-tipped applicators (Puritan, Guilford, ME) were used for cloacal swabs, which were pre-moistened with buffered peptone water (BPW; Criterion, Santa Maria, CA). Samples were then inoculated into 5 mL of BPW. Each litter sample was composed of 3 subsamples of 100 g taken from different locations in the same quadrant of the house, combined and mixed by hand. All samples were stored on ice until further analysis. Salmonella Enrichment Salmonella enrichment for drag swabs was performed by adding 20 mL of Tetrathionate Brilliant Green Broth (TTB; Himedia, Mumbai, India) to the sample, vortexed, and incubated at 37°C for 24 hours. One milliliter of BPW containing the cloacal swabs was added to 20 mL of TTB and incubated at 37°C for 24 h for Salmonella enrichment. Both drag and cloacal swab enrichments were streaked onto Xylose Lysine Tergitol-4 agar (XLT4; BD, Sparks, MD) and incubated at 37°C for 24 hours. Additionally, 90 mL of phosphate buffered saline (PBS) were added to 10 g of litter sample in 15.2 × 22.9 cm filter bags (VWR Sterile Sampling Bag, VWR, Radnor, PA), stomached for 60 s, enriched, and isolated similar to the swab samples. Black colonies, indicating H2S production, were subjected to a precipitation test with antiserum (DIFCO™ Salmonella O Antiserum Poly A—I and Vi; BD, Sparks, MD) for Salmonella confirmation. Presumptive Salmonella were shipped to the Department of Food Science at Purdue University in 1 mL of tryptic soy agar tubes containing a single isolated colony. Upon receiving, each isolate was grown in 10 mL Tryptic Soy Broth (TSB) (Acumedia; Lansing, MI) and incubated at 37°C for 24 hours. After incubation, each sample was streaked for isolation onto XLT4 plates and incubated at 37°C for 24 hours. A single isolated colony was picked from the XLT4 plates, based on typical Salmonella colony characteristics and further grown in micro-centrifuge tubes (Costar; Corning, NY) containing 1 mL of TSB and incubated at 37°C for 24 hours. This was followed by centrifugation at 5,000 X g for 3 minutes. (Thermo Scientific; Model: Sorvall Legend Micro 17 Centrifuge, Waltham, MA). The supernatant was removed, and the pellet was re-suspended in 1 mL of 20% glycerol, which was finally mixed with sterile beads and cryopreserved at −80°C for further analysis. DNA Extraction and PCR Amplification A boiling method was used to extract DNA based on Ngamwongsatit et al. (2008). After incubation, each micro-centrifuge tube was centrifuged at 5,000 × g for 2 minutes. The supernatant was removed and the pellet was re-suspended in 500 μL of DNAse free water (Life Technologies; Grand Island, NY) and vortexed. Following this, the micro-centrifuge tube was heated in a dry bath at 100°C for 10 minutes. After boiling, the micro-centrifuge tube was centrifuged at 10,000 × g for 5 min, and the supernatant was removed and placed in a sterile micro-centrifuge tube. The purity of DNA was evaluated based on protein/DNA ratio of absorbance (A260/A280; Epoch spectrophotometer, Biotek, Winooski, VT), and the purity standard was a A260/A280 ratio between 1.8 and 2.0. Quantification of DNA was performed for results expecting a concentration of 10 to 100 ng, which was present in all the samples. After quantification, the DNA was stored at −20°C until further use. A total of 595 presumptive Salmonella isolates was subjected to PCR using a primer set based on the hilA gene with an amplicon of 784 bp (Panthamanathan et al., 2003). For the reaction, GoTaqR Green Master Mix (Promega Corp., Madison, WI) with a concentration of 0.7X was mixed with a concentration of 0.5 μM of each forward and reverse primers. DNA was diluted based on quantification results to obtain a DNA template in a concentration of 10 to 100μg and DNAse free water to make the volume of 25 μL for each reaction. For the negative control, DNAse free water was used, and for the positive control, a DNA template of a known Salmonella strain (Salmonella Typhimurium ATCC 700720) was used. Reactions were performed with a QuantStudio (ThermoFisher Scientific, Carlsbad, CA) using different parameters with an initial denaturation cycle at 94°C for 5 min, followed by a denaturation at 94°C for 30 s, an annealing temperature of 65°C for 30 s with 25 cycles, and ending with an extension temperature of 72°C for 10 minutes. After completion of each cycle, amplicons were gel electrophoresed to observe an amplicon of 784 bp. Serotyping and Antimicrobial Resistance Analysis All 262 PCR-confirmed Salmonella isolates were sent to the National Veterinary Service Laboratory (NVSL, Ames, IA) for serotyping. Salmonella serotyping was based on agglutination for antisera where O antigens, H antigens phase 1, and H antigens phase 2 were used as a formula to identify serotypes based on the Kauffman–White scheme. PCR-confirmed isolates were analyzed for antimicrobial resistance using the CMV3AGNF SensititreTM Gram negative plate (product no. YCMV3AGNF; Remel; Lenexa, KS), which allowed testing for resistance against cefoxitin (FOX), azithromycin (AZI), chloramphenicol (CHL), tetracycline (TET), ceftriaxone (AXO), amoxicillin/clavulanic acid 2:1 (AUG2), ciprofloxacin (CIP), gentamicin (GEN), nalidixic acid (NAL), ceftiofur (XNL), sulfisoxazole (FIS), trimethoprim/sulfamethoxazole (SXT), ampicillin (AMP), and streptomycin (STR). The analysis was performed using a previously described broth micro-dilution method (Trek Diagnostics, Waltham, MA), and minimum inhibitory concentration (MIC) breakpoints for determining resistance or susceptibility were based on those published by the Clinical Laboratory Standards Institute (CLSI, 2014) and NARMS (FDA, 2011). Each isolate was inoculated into 4 mL of demineralized water (Thermo Fisher) by picking 3 to 5 colonies from an overnight culture on tryptic soy agar (TSA; Neogen, Lansing, MI) to visually match a McFarland turbidity standard of 0.5. Next, 10 μL of the suspension were transferred to a tube of 11 mL cation adjusted Mueller–Hinton broth with TES buffer (CAMHBT) (Thermo Fisher) to create an inoculum containing 1 × 105 CFU/mL. This inoculum was transferred into a sterile reagent reservoir (Corning, Corning, NY) and then 50 μL were inoculated into each well of a SensititreTM plate. Inoculated plates were covered with an adhesive seal and incubated for 24 h at 35°C. The MIC was recorded as the minimum concentration that a particular antimicrobial completely inhibited growth, except for the sulfonamide drugs FIS and SXT, in which MIC was recorded as the concentration allowing 10 to 20% growth compared to the control. With each batch of SensititreTM plates prepared, a plate containing a quality control organism with known MIC values (E. coli ATCC 25922) was prepared. If quality control MIC values did not fall within the range of known MIC values, results from accompanying plates were disregarded, and corresponding isolates were retested. Data Analysis Results of prevalence obtained during pre- and post recommendation were compared on each farm to establish statistical significances between the 2 phases. A similar procedure was followed to establish differences in AMR prevalence for each farm. Since prevalence data were obtained in percentages (discrete dataset), an arcsine transformation was performed to obtain a continuous dataset and decrease variability (Warton and Hui, 2011). Differences were analyzed using comparative analysis (t test) where P ≤ 0.05 was used to determine significant differences. All data were analyzed using the statistical program SAS 9.3 version (SAS, Cary, NC). RESULTS AND DISCUSSION Salmonella Prevalence Presumptive Salmonella isolates were confirmed by PCR, and the results are shown in Table 1. No differences (P > 0.05) in the prevalence of Salmonella spp. were observed between the pre- and post-recommendation phases in our study, except for farm 4 where a higher (P < 0.05) prevalence was observed at the post-recommendation phase. For prevalence of Salmonella measured by the presence of this pathogen at house level on each farm, positive results for Salmonella were present in 3 out of 4 houses on each farm (75% prevalence) during the pre-recommendation phase, while during the post-recommendation phase, the prevalence was observed to be 100% (data not shown). These results are consistent with reports from Alali et al. (2010), in which Salmonella was present on all 4 broiler farms tested for Salmonella prevalence. Results similar to our study also have been reported by Mathole et al. (2017), in which cloacal swabs obtained from 286 chickens in South Africa suggested a 3.15% prevalence of Salmonella. In our study, differences in prevalence between farms at pre- and post-recommendation phases can be attributed to seasonal variation, possible irregular management of farms, and differences in employees and their practices. Houses were cleaned out during spring, which also could explain the lower levels of Salmonella during the pre-recommendation phase compared to the prevalence during the post-recommendation phase. These findings are consistent with reports by Bailey et al. (2001), in which it was suggested that Salmonella prevalence was greater in fall as compared to spring and summer. Entry of pathogens into poultry and level of infections can be controlled via biosecurity practices and cleaning, mainly using an all-in/all-out approach, as one of the most important sources for contamination consists of sick animals (Ethelberg et al., 2014). The biosecurity improvements recommended in our study were based on the survey results from English (2015) and included a change of clothing for visitors before entering the houses, shoe cover use, treatment of litter between flocks, implementation of a rodent and insect control program, restricted entry of personnel, and registration of entry and exit to the farms. According to results in this study, the biosecurity measures recommended were not effective (P > 0.05) in minimizing Salmonella prevalence on the farms. This can be attributed to several factors, such as seasonal variations and variation in the implementation of the recommended biosecurity practices. Laanen et al. (2014) reported that increased cost for implementing biosecurity measures, along with a lack of rewards for producers and communication on how to implement biosecurity measures, were among the top 5 reasons for the lack of implementation of biosecurity measures. However, there have been other documented studies reporting that the cost of implementing biosecurity practices can be effective in the long term (Wegener et al., 2003). This study was conducted in Denmark, and it was reported that Salmonella controls resulted in an increase by $0.02/Kg of broilers (Wegener et al., 2003); the cost of retail broilers is reported to be U.S. $4.12/Kg (USDA, 2016b). If control practices recommended by the study in Denmark were applied, the cost to control Salmonella could result in a 0.5% increase of the retail price. Thus, informing producers about the cost and benefits can have a positive impact on the farmers’ perspectives, therefore facilitating the process of implementation. Table 1. Prevalence (%) of Salmonella spp. from farms during the pre- and post-recommendation phases. % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B A,BDifferent superscripts represent significant differences between the sampling phases (P < 0.05) within a farm. View Large Table 1. Prevalence (%) of Salmonella spp. from farms during the pre- and post-recommendation phases. % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B % Prevalence Farm Pre-recommendation phase Post-recommendation phase 1 7/240 (3%)A 15/240 (6%)A 2 6/240 (3%)A 14/240 (6%)A 3 9/240 (4%)A 12/240 (5%)A 4 6/240 (3%)A 34/240 (14%)B A,BDifferent superscripts represent significant differences between the sampling phases (P < 0.05) within a farm. View Large As evident, implementation of biosecurity practices in poultry are important for prevention or control of pathogens, one of the recommendations made in our study was rodent control, which is essential, since rats can contaminate disinfected houses with Salmonella (Lister, 2008). Recommendations on hygiene and disinfection were not made; however, Wales et al. (2006) demonstrated that while Salmonella eradication was not possible, there was a decrease in the prevalence of this pathogen after dry cleaning. Spread of pathogens between farms can take place when farms are close to each other through vectors such as insects, rodents, and birds, as well as humans (Albihn and Vinnerås, 2007). Salmonella Serotypes Figure 1 represents the total distribution of Salmonella found on all 4 farms, irrespective of recommendation phases. Among all Salmonella, 6 different serotypes were identified: S. Enteritidis (52%), S. Berta (38%), S. Mbandaka (7%), S. Typhimurium (2%), S. Kentucky (0.4%), and S. Tennessee (0.4%). Observations from our study are similar to reports by Roy et al. (2002), in which S. Kentucky, S. Enteritidis, S. Mbandaka, S. Berta, S. Enteritidis, and S. Typhimurium were the common serotypes from poultry, poultry products, and poultry environment. On farm 1 (Figure 2A), S. Enteritidis was observed to be the most common serotype in the pre- and post-recommendation phases. S. Typhimurium was the second most prevalent (32%) serotype during the pre-recommendation phase, while no S. Typhimurium was found in the post-recommendation phase. New serotypes were observed with low prevalence in the post-recommendation phase. Farm 1 showed the highest diversity of Salmonella serotypes. For farm 2 (Figure 2B) during the pre-recommendation phase, the only serotype present was S. Enteritidis, which is also the most prevalent in the post-recommendation phase followed by S. Berta and S. Mbandaka. Farm 3 (Figure 2C) showed similar behavior to farm 2, where S. Enteritidis is the only serotype present in the pre-recommendation phase. Unlike the other farms, farm 4 (Figure 2D) is mostly associated with S. Berta during the post-recommendation phase. The only S. Tennessee found among all Salmonella isolates is found in the post-recommendation phase on farm 4. Overall, S. Enteritidis was observed to be most prevalent on farms 1, 2, and 3, while S. Berta was the most prevalent on farm 4 during the post-recommendation phase. With well-documented information about S. Enteritidis being the most common serotype found on poultry farms, mitigation efforts are usually focused on this serotype. However, from our study, it is evident that S. Berta is the most prevalent serotype on farm 4, and mitigation strategies used for S. Enteritidis might not be effective against other highly prevalent Salmonella serotypes. Furthermore, given the inconsistencies in prevalence rates and serotypes on different farms, it can be challenging to control Salmonella and develop robust biosecurity measures for implementation on the farms. Figure 1. View largeDownload slide Distribution of Salmonella spp. on farms during the pre- and post-recommendation phases. Figure 1. View largeDownload slide Distribution of Salmonella spp. on farms during the pre- and post-recommendation phases. Figure 2. View largeDownload slide Prevalence (%) of Salmonella serotypes on farms during the pre- and post-recommendation phases: (A) Farm 1; (B) Farm 2; (C) Farm 3; and (D) Farm 4. Figure 2. View largeDownload slide Prevalence (%) of Salmonella serotypes on farms during the pre- and post-recommendation phases: (A) Farm 1; (B) Farm 2; (C) Farm 3; and (D) Farm 4. Based on the type of samples collected, cloacal swab samples had the highest prevalence of S. Enteritidis during pre- and post-recommendation phases (Table 2). It was observed that S. Enteritidis was consistently isolated from cloacal swabs, drag swabs, and litter samples during both phases. S. Berta, which was most prevalent during the post-recommendation phase, was isolated from cloacal and drag swabs from all 4 farms and only on the litter samples on farm 4 (Table 2). S. Mbandaka was distributed on all farms only during the post-recommendation phase in the cloacal swabs, in the drag swabs on farm 4, and in the litter samples on farm 4. S. Typhimurium was isolated only from farm 1 in the drag swabs and litter samples, while S. Kentucky and S. Tennessee were isolated from drag swabs on farm 1 and 4, respectively (Table 2). Incidence of S. Typhimurium was associated with litter and drag swabs samples on farm 1 during the pre-recommendation phase, which is contrary to reports by Singh et al. (2013), in which S. Typhimurium was isolated from cloacal samples with a prevalence of 4.4% (8/180) in layer chickens. Three out of the 6 serotypes found in this study are in the list of top 20 most common serotypes associated with public health (CDC, 2011). Based on the CDC Atlas (CDC, 2013a), S. Berta, S. Enteritidis, S. Mbandaka, and S. Typhimurium were isolated from humans from 1968 to 2011. Additionally, S. Berta was associated with chicken: 52% (577/1,305) of clinical sources and 65% (810/1246) of non-clinical sources; S. Enteritidis with a 50% (15,526/30,880) incidence on chicken from clinical sources and 83% (5,513/6,677) for non-clinical sources; and S. Mbandaka was isolated from chicken 30% (1,660/5,484) for clinical sources and 49% (1,248/2,571) of non-clinical sources (CDC, 2013a). Therefore, knowing actual serotypes that are present on farms is important to create control programs to decrease or eliminate relevant serotypes. Table 2. Prevalence (%) of Salmonella serotypes by sample type on each farm at the pre- and post-recommendation phases. Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 *Number of Salmonella with the prevalence % shown in parentheses. View Large Table 2. Prevalence (%) of Salmonella serotypes by sample type on each farm at the pre- and post-recommendation phases. Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 Pre-recommendation Post recommendation Farms Cloacal Drag Litter Total Cloacal Drag Litter Total S. Enteritidis* 1 11 (85%) 1 (8%) 1 (8%) 13 20 (95%) 1 (5%) 0 21 2 6 (50%) 2 (17%) 4 (33%) 12 23 (74%) 5 (16%) 3 (10%) 31 3 19 (90%) 2 (10%) 0 21 22 (85%) 1 (4%) 3 (12%) 26 4 10 (100%) 0 0 10 2 (100%) 0 0 2 S. Berta* 1 0 0 0 0 2 (67%) 1 (33%) 0 3 2 0 0 0 0 1 (50%) 1 (50%) 0 2 3 0 0 0 0 6 (86%) 1 (24%) 0 7 4 0 0 0 0 31 (55%) 36 (41%) 21 (24%) 88 S. Mbandaka* 1 0 0 0 0 1 (100%) 0 0 1 2 0 0 0 0 1 (100%) 0 0 1 3 0 0 0 0 2 (100%) 0 0 2 4 0 0 0 0 2 (14%) 9 (64%) 3 (21%) 14 S. Typhimurium* 1 0 2 (33%) 4 (67%) 6 0 0 0 0 S. Tennessee* 4 0 0 0 0 0 1 (100%) 0 1 S. Kentucky* 1 0 0 0 0 0 1 (100%) 0 1 *Number of Salmonella with the prevalence % shown in parentheses. View Large Antimicrobial Resistance All isolates positive for Salmonella were subjected to AMR testing. No differences (P > 0.05) were observed in the AMR of Salmonella spp. during pre- and post-recommendation phases within each farm (Table 3). Resistance patterns, sample type, and serotypes of Salmonella are presented in Table 4. It was observed that 63% (12/19) of Salmonella were resistant to TET, followed by 26% (5/19) being resistant to STR and 21% (4/19) to NAL, irrespective of the farms from which the Salmonella originated and the recommendation phase. In contrast, lower resistance (5%; 1/19) was observed for AXO, AZI, CHL, AMP, and AUG2. For all Salmonella, it was observed that 68% (13/19) were resistant to at least one antimicrobial class, 26% (5/13) were resistant to 2 classes, and 5% (1/19) were resistant to 3 antimicrobial classes, which were classified as multidrug resistant (MDR). From the 13 isolates with resistance to one antimicrobial class, 54% (7/13) were resistant to TET, 23% (3/13) were resistant to NAL from the quinolones class, 15% (2/13) were resistant to STR from the aminoglycosides class, and the remaining 8% (1/13) were resistant to CHL, which belongs to the phenicols antimicrobial class. The most prevalent serotype among the resistant Salmonella was S. Enteritidis with 42%, followed by S. Berta (37%) and S. Mbandaka with 11 and 5% each for S. Typhimurium and S. Kentucky. Cloacal samples were most often antimicrobial resistant (47%), followed by 42% of drag swabs and 11% associated with litter sample from which one was the MDR isolate (Table 4). According to NARMS reports on distribution of Salmonella serotypes found in retail chicken, the most prevalent serotype is S. Typhimurium with 43%, followed by S. Enteritidis in 13% of the samples, S. Kentucky in 28% of the samples, and S. Mbandaka in 2% of samples. No S. Berta was found in retail chicken meat samples (FDA, 2013). Table 3. Antimicrobial resistance of Salmonella on 4 different farms. Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A ASame letters represent no significant differences between seasons (P > 0.05). View Large Table 3. Antimicrobial resistance of Salmonella on 4 different farms. Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A Pre-recommendation Post recommendation Farm No. positive isolates No. positive isolates 1 3/19 (16%)A 4/26 (15%)A 2 1/12 (8%)A 1/34 (3%)A 3 1/21 (5%)A 2/35 (6%)A 4 1/10 (10%)A 6/105 (6%)A ASame letters represent no significant differences between seasons (P > 0.05). View Large Table 4. Antimicrobial resistance patterns@ of Salmonella from poultry farms. Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta *TET = Tetracycline; AUG2 = Amoxicillin/Clavulanate; AMP = Ampicillin; STR = Streptomycin; AXO = Ceftriaxone; NAL = Nalidixic Acid; AZI = Azithromycin; CHL = Chloramphenicol. @12/19 (63%) were resistant to TET; 5/19 (26%) were resistant to STR; 4/19 (21%) were resistant to NAL; and 1/19 (5%) were resistant to AXO, AZI, CHL, AMP, and AUG2. View Large Table 4. Antimicrobial resistance patterns@ of Salmonella from poultry farms. Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta Resistance pattern* Sample type Serotype TET+AUG2+AMP Litter S. Mbandaka TET+STR Drag S. Kentucky TET+STR Cloacal S. Berta TET+STR Drag S. Berta AXO+NAL Cloacal S. Enteritidis AZI+TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Cloacal S. Enteritidis TET Drag S. Enteritidis TET Cloacal S. Berta TET Drag S. Berta TET Drag S. Berta TET Cloacal S. Mbandaka NAL Litter S. Enteritidis NAL Cloacal S. Enteritidis NAL Drag S. Enteritidis STR Cloacal S. Berta STR Drag S. Typhimurium CHL Drag S. Berta *TET = Tetracycline; AUG2 = Amoxicillin/Clavulanate; AMP = Ampicillin; STR = Streptomycin; AXO = Ceftriaxone; NAL = Nalidixic Acid; AZI = Azithromycin; CHL = Chloramphenicol. @12/19 (63%) were resistant to TET; 5/19 (26%) were resistant to STR; 4/19 (21%) were resistant to NAL; and 1/19 (5%) were resistant to AXO, AZI, CHL, AMP, and AUG2. View Large Alali et al. (2010) reported that 6.9% of Salmonella isolates were resistant to TET, 91.4% were resistant to STR, 55.2% were resistant to AUG2, and 56.9% were resistant to AMP. Singh et al. (2013) reported 23% of the Salmonella were resistant to TET and CHL, and 11.5% were resistant to STR. Differences in the prevalence of AMR of Salmonella can be attributed to the number of isolates tested, as well as the selection process. In our study, all PCR confirmed Salmonella were subjected to antimicrobial susceptibility in contrast to Alali et al. (2010), in which 58 isolates out of 115 isolates were tested against AMR. One isolate out of the 19 antimicrobial resistant isolates demonstrated resistance to TET and AZI. On a limited basis, the use of AZI to treat Salmonella infections has been studied, suggesting its use as an alternate antimicrobial to treat these infections (Acheson and Hohman, 2001). Nalidixic acid is categorized as a quinolone, an antimicrobial class that is known to be one of the first-choice antimicrobials to treat salmonellosis. Resistance in 23% of the isolates to this antimicrobial agent is concerning, due to its importance in human medicine. Isolates with resistance to this antimicrobial agent are likely to develop resistance to other antimicrobial agents that also belong to the quinolones class (Sárközy, 2001). A resistance pattern to NAL and AXO was observed in only one of the Salmonella isolates in our study. When bacterial strains exhibit resistance to an antimicrobial agent of any particular antimicrobial class, resistance to other antimicrobial agents from that same class is likely to occur (Tenover, 2006). In this study, the antimicrobials of quinolones classes tested were NAL and CIP, where resistance to NAL was observed but not CIP. Although NAL and CIP belong to the quinolones class, resistance to the first antimicrobial is developed with a single chromosomal point mutation, whereas resistance to CIP generally requires at least 2 chromosomal mutations (Crump et al., 2003). Quinolones are often a first choice to treat Salmonella infections; CIP exclusively is the first option to treat salmonellosis in adults (Mølbak, 2005). Although resistance to NAL occurs without predisposing these same isolates to CIP resistance, this still represents a risk to human health. Aminoglycosides are another class of antimicrobials that are commonly used in the poultry industry. In our study, resistance to GEN was not observed, while resistance to STR was seen and can be the result of use of this antimicrobial agent against necrotic enteritis, fowl cholera, or Staphylococcus spp. (Landoni and Albarellos, 2015). While resistance to CHL was observed to be low in our study, the use of this agent can increase in human medicine when access to cephalosporins or cephems is limited (Collignon et al., 2009). In our study, no specific association between AMR patterns and Salmonella serotypes was observed, which is in agreement with Álvarez-Fernández et al. (2012). CONCLUSIONS Results from this study demonstrate that recommendations on management and biosecurity practices on poultry farms have very limited success for Salmonella control if not implemented correctly. Among all Salmonella isolated from the farms, S. Enteritidis was the most prevalent, followed by S. Berta. All farms followed similar trends of having S. 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Poultry ScienceOxford University Press

Published: Mar 27, 2018

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