Dataset Information

Antimicrobial Resistance in Swine Fecal Specimens Across Different Farm Management Systems.

ABSTRACT: Antimicrobial use in agricultural animals is known to be associated with increases in antimicrobial resistance. Most prior studies have utilized culture and susceptibility testing of select organisms to document these phenomena. In this study we aimed to detect 66 antimicrobial resistance (AMR) genes for 10 antimicrobial agent classes directly in swine fecal samples using our previously developed antimicrobial resistance TaqMan array card (AMR-TAC) across three different swine farm management systems. This included 38 extensive antimicrobial use (both in treatment and feed), 30 limited antimicrobial use (treatment only), and 30 no antimicrobial use farms. The number of resistance genes detected in extensive antimicrobial use farms was higher than in limited and no antimicrobial use farms (28.2 genes ± 4.2 vs. 24.0 genes ± 4.1 and 22.8 genes ± 3.6, respectively, p < 0.05). A principal component analysis and hierarchical clustering of the AMR gene data showed the extensive use farm samples were disparate from the limited and no antimicrobial use farms. The prevalence of resistance genes in extensive use farms was significantly higher than the other farm categories for 18 resistance genes including bla SHV, bla CTX-M1 group, bla CTX-M9 group, bla VEB, bla CMY2-LAT, aac(6')-lb-cr, qnrB1, gyrA83L-E. coli, armA, rmtB, aac(3)-IIa, mphA, 23S rRNA 2075G-Campylobacter spp., mcr-1, catA1, floR, dfrA5-14, and dfrA17. These genotypic findings were supported by phenotypic susceptibility results on fecal E. coli isolates. To examine the timing of AMR gene abundance in swine farms, we also performed a longitudinal study in pigs. The results showed that AMR prevalence occurred both early, presumably from mothers, as well as after weaning, presumably from the environment. In summary, detection of AMR genes directly in fecal samples can be used to qualitatively and quantitatively monitor AMR in swine farms.

SUBMITTER: Pholwat S

PROVIDER: S-EPMC7311580 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Antimicrobial Resistance in Swine Fecal Specimens Across Different Farm Management Systems.

Pholwat Suporn S Pongpan Tawat T Chinli Rattapha R Rogawski McQuade Elizabeth T ET Thaipisuttikul Iyarit I Ratanakorn Parntep P Liu Jie J Taniuchi Mami M Houpt Eric R ER Foongladda Suporn S

Frontiers in microbiology 20200617

Antimicrobial use in agricultural animals is known to be associated with increases in antimicrobial resistance. Most prior studies have utilized culture and susceptibility testing of select organisms to document these phenomena. In this study we aimed to detect 66 antimicrobial resistance (AMR) genes for 10 antimicrobial agent classes directly in swine fecal samples using our previously developed antimicrobial resistance TaqMan array card (AMR-TAC) across three different swine farm management sy ...[more]

PMID: 32625181

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Project description:Intestinal infection with enterotoxigenic Escherichia coli (ETEC) is an important disease in swine resulting in significant economic losses. Knowledge about the epidemiology, the diagnostic approach and methods of control are of fundamental importance to tackle the disease. The ETEC causing neonatal colibacillosis mostly carry the fimbriae F4 (k88), F5 (k99), F6 (987P) or F41, while the ETEC of post-weaning diarrhoea carry the fimbriae F4 (k88) and F18. These fimbriae adhere to specific receptors on porcine intestinal brush border epithelial cells (enterocytes), starting the process of enteric infection. After this colonization, the bacteria produce one or more enterotoxins inducing diarrhoea, such as the heat stable toxin a (STa), the heat stable toxin b (STb), and the heat labile toxin (LT). A role in the pathogenesis of the disease was demonstrated for these toxins. The diagnosis of enteric colibacillosis is based on the isolation and quantification of the pathogenic E.coli coupled with the demonstration by PCR of the genes encoding for virulence factors (fimbriae and toxins). The diagnostic approach to enteric colibacillosis must consider the differential diagnosis and the potential different causes that can be involved in the outbreak. Among the different methods of control of colibacillosis, the use of antimicrobials is widely practiced and antibiotics are used in two main ways: as prophylactic or metaphylactic treatment to prevent disease and for therapeutic purposes to treat diseased pigs. An accurate diagnosis of enteric colibacillosis needs an appropriate sampling for the isolation and quantification of the ETEC responsible for the outbreak by using semi-quantitative bacteriology. Definitive diagnosis is based on the presence of characteristic lesions and results of bacteriology along with confirmation of appropriate virulence factors to identify the isolated E.coli. It is important to confirm the diagnosis and to perform antimicrobial sensitivity tests because antimicrobial sensitivity varies greatly among E. coli isolates. Growing concern on the increase of antimicrobial resistance force a more rational use of antibiotics and this can be achieved through a correct understanding of the issues related to antibiotic therapy and to the use of antibiotics by both practitioners and farmers.

Project description:The overall aim of the current study was to test the hypotheses that (i) antibiotic resistance in bacteria were more frequent in clinically health pigs in intensified company owned, medium-scale farms (MSFs) (100-500 sows) than in pigs in family-owned, small-scale farms (SSFs) (1-50 sows) and (ii) that farmers working at the MSFs were more prone to attain antibiotic resistant bacteria than farmers working at SSFs. The study was conducted in North-Eastern Thailand, comprising fecal Escherichia coli isolates from pigs, farmers working with the pigs (contact humans) and persons living in the same household as the farmer (non-contact humans) at 51 MSFs and 113 SSFs. Samples from all farms were also screened for methicillin-resistant staphylococcus aureus (MRSA), which was not detected in pig samples, but was found in one human sample. Susceptibility was tested by disc-diffusion for seven antibiotics commonly used in the study area. Resistance in pig isolates from MSFs were more frequent for chloramphenicol which (P < 0.001), trimethoprim/sulfamethoxazole (P < 0.001) and gentamicin (P < 0.05) compared with isolates from SSFs, whereas the opposite was true for tetracycline (P < 0.01). Resistance in the human isolates was lower than those in the isolates from pigs for tetracycline, trimethoprim/sulfamethoxazole and chloramphenicol (P < 0.001). The frequency of resistance in the contact human samples from SSFs and MSFs did not differ. There was no difference between isolates from contact and non-contact humans for any of the tested antibiotics. Multidrug resistance in isolates from pigs was 26%, significantly higher (P < 0.01) than the 13% from humans. The data indicate that (i) resistance to antibiotics, including those critical and highly important for human medicine, were more common in fecal E. coli from pigs at the MSFs than at the SSFs, whereas (ii) the resistance in fecal E. coli from pig farmers seemed not to be influenced by the level of intensification of the farm they were working at.

Project description:The need to reduce antimicrobial use (AMU) in livestock production has led to the establishment of national AMU data collection systems in several countries. However, there is currently no consensus on which AMU indicator should be used and many of the systems have defined their own indicators. This study sought to explore the effect of using different internationally recognized indicators on AMU data collected from Irish pig farms and to determine if they influenced the ranking of farms in a benchmarking system. AMU data for 2016 was collected from 67 pig farms (c. 35% of Irish pig production). Benchmarks were defined using seven AMU indicators: two based on weight of active ingredient; four based on the defined daily doses (DDD) used by the European Medicines Agency and the national monitoring systems of Denmark and the Netherlands; and one based on the treatment incidence (TI200) used in several published studies. An arbitrary "action zone," characterized by farms above an acceptable level of AMU, was set to the upper quartile (i.e., the top 25% of users, n = 17). Each pair of indicators was compared by calculating the Spearman rank correlation and assessing if farms above the threshold for one indicator were also above it for the comparison indicator. The action zone was broadly conserved across all indicators; even when using weight-based indicators. The lowest correlation between indicators was 0.94. Fifteen farms were above the action threshold for at least 6 of the 7 indicators while 10 farms were above the threshold for all indicators. However, there were important differences noted for individual farms between most pairs of indicators. The biggest discrepancies were seen when comparing the TI200 to the weight-based indicators and the TI200 to the DDDANED (as used by Dutch AMU monitoring system). Indicators using the same numerator were the most similar. All indicators used in this study identified the majority of high users. However, the discrepancies noted highlight the fact that different methods of measuring AMU can affect a benchmarking system. Therefore, careful consideration should be given to the limitations of any indicator chosen for use in an AMU monitoring system.

Dataset Information

Antimicrobial Resistance in Swine Fecal Specimens Across Different Farm Management Systems.

Publications

Antimicrobial Resistance in Swine Fecal Specimens Across Different Farm Management Systems.

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