Phenotypic antimicrobial resistance in Escherichia coli strains isolated from swine husbandries in North Western Germany – temporal patterns in samples from laboratory practice from 2006 to 2017

Moennighoff, Clara; Thomas, Nele; Nienhaus, Franziska; Hartmann, Maria; Menrath, Andrea; Merkel, Jörg; Detlefsen, H.; Kreienbrock, Lothar; Hennig-Pauka, Isabel

doi:10.1186/s12917-020-2268-z

Cited by 15 publications

(13 citation statements)

References 43 publications

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“…The data was mostly collected in the North West, both for the different human populations and different food-producing animal populations. This is in line with the high density of livestock production in the North West ( 18 ) and high number of participating health care facilities in the human surveillance system ( 1 – 3 ) ( Supplementary Table 3 ). We studied only resistance to four antibiotics that were routinely included in all three monitoring and surveillance systems in one country.…”

Section: Discussionsupporting

confidence: 58%

A Joint Regional Analysis of Resistance Combinations in Escherichia coli in Humans and Different Food-Producing Animal Populations in Germany Between 2014 and 2017

Suwono

Eckmanns

Kaspar

et al. 2022

Front. Public Health

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A joint comparative regional analysis of different resistance combinations across human and veterinary medicine has not been previously conducted in Germany. This study analyses 16 resistance combinations from four antibiotics in E. coli from different human and food-producing animal populations in three German regions: East, North West and South West. The E. coli data were collected from the three national surveillance and monitoring systems for antimicrobial resistance (AMR) bacteria in humans (ARS), food-safety (Zoonosis Monitoring) and animal pathogens (GERM-Vet) from January 2014 to December 2017. Analyses were performed using cluster analysis (hierarchical clustering, average linkage) in R. We included data from 537,215 E. coli isolates from human clinical isolates, from clinical as well as non-clinical isolates from food-producing animals and from food. The majority of the data originated from the North West region. There were two main clusters built on 54 different human and animal populations. We observed close similarities of resistance combinations in human isolates from the different regions within the same human populations from outpatient cares, general wards and ICUs. These resistance combinations clustered separately from non-clinical isolates from broilers, turkeys, cattle and pigs; except for some of clinical isolates from these populations which clustered closely to isolates from human populations. Frequently, the resistance combinations in E. coli isolates from farms clustered closely to the resistance combinations in isolates from slaughterhouses from broilers and turkeys over all regions. However, the resistance combinations in E. coli isolates from retail meat populations tended to cluster separately within their respective populations in between all regions.

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Section: Discussionsupporting

confidence: 58%

A Joint Regional Analysis of Resistance Combinations in Escherichia coli in Humans and Different Food-Producing Animal Populations in Germany Between 2014 and 2017

Suwono

Eckmanns

Kaspar

et al. 2022

Front. Public Health

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“…Studies from Vietnam have reported lower MIC value of clinical isolates as 4 (µg/ml) [45] and MICs range of 4 to 16 μg/ml in E. coli isolates were reported from Chinese University Hospital [46]. The greater MIC value revealed these E. coli isolates as non-wild type [47]. The presence of multiple systems of resistance and multiple copy number of plasmids carrying mcr-1 gene may have played role in increasing the MIC value of colistin [48].…”

Section: Discussionmentioning

confidence: 97%

Plasmid mediated colistin resistant mcr-1 and co-existence of OXA-48 among Escherichia coli from clinical and poultry isolates: first report from Nepal

et al. 2020

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Background Plasmid-mediated resistance to the last-resort drugs: carbapenems and colistin is an emerging public health threat. The studies on the prevalence and co-expression of resistant genes among livestock and human pathogens are rare in Nepal. This is the first study in Nepal exploring the prevalence and co-existence of colistin resistance gene, mcr-1 along with carbapenemase resistance gene, OXA-48 in Escherichia coli isolated from poultry and clinical specimens. Methods A total of 240 rectal swabs from chickens of five different poultry farms of Kathmandu valley and 705 mid-stream urine samples from human subjects attending Kantipur Hospital, Kathmandu were collected between August, 2018 and March, 2019. Rectal swabs and urine specimens were cultured. E. coli isolated from the specimens were screened for antimicrobial susceptibility testing (AST) using disk diffusion method’. Minimum inhibitory concentration (MIC) of colistin was determined by agar dilution method using 0.5 µg/ml to 32 µg/ml. The E. coli isolates were first screened for mcr-1 followed by screening for OXA-48 genes using conventional Polymerase chain reaction (PCR). Results Of the total samples analyzed, E. coli was isolated from 31.7% (76/240) of poultry and 7.9% (56/705) of clinical specimens. In AST, 80% (61/76) of E. coli from poultry and 79% (44/56) from clinical specimens were MDR. The phenotypic prevalence of colistin resistance in poultry specimens were 31.6% (24/76) and clinical specimens were 21.4% (12/56). In PCR assay, 27.6% (21/76) of poultry and 19.6% (11/56) of clinical isolates had colistin resistant mcr-1 gene. MICs value of E. coli isolates ranged from 4 to 32 (µg/ml) in both clinical and poultry isolates. Prevalence of co-existing carbapenem resistance gene, OXA-48, among colistin resistant mcr-1 positive isolates was 38% (8/21) in poultry specimens and 18.2% (2/11) in clinical specimens. Conclusions The high prevalence of colistin and carbapenem resistant genes, and their co-existence in plasmid DNA of E. coli isolates in this study suggests the possible spread to other animal, human and environmental pathogens. Molecular methods in addition to the conventional diagnostics in laboratories can help in early diagnosis, effective management and control of their potential transmission.

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“…Finally, we excluded penicillin, clindamycin, and tilmicosin from the analysis as all isolates had the maximum MIC value. This could be due to natural resistance in E. coli to these substances as other studies have demonstrated 100% resistance to these drugs ( 44 , 45 ).…”

Section: Discussionmentioning

confidence: 93%

Identifying Associations in Minimum Inhibitory Concentration Values of Escherichia coli Samples Obtained From Weaned Dairy Heifers in California Using Bayesian Network Analysis

2022

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ObjectiveMany antimicrobial resistance (AMR) studies in both human and veterinary medicine use traditional statistical methods that consider one bacteria and one antibiotic match at a time. A more robust analysis of AMR patterns in groups of animals is needed to improve on traditional methods examining antibiotic resistance profiles, the associations between the patterns of resistance or reduced susceptibility for all isolates in an investigation. The use of Bayesian network analysis can identify associations between distributions; this investigation seeks to add to the growing body of AMR pattern research by using Bayesian networks to identify relationships between susceptibility patterns in Escherichia coli (E. coli) isolates obtained from weaned dairy heifers in California.MethodsA retrospective data analysis was performed using data from rectal swab samples collected from 341 weaned dairy heifers on six farms in California and selectively cultured for E. coli. Antibiotic susceptibility tests for 281 isolates against 15 antibiotics were included. Bayesian networks were used to identify joint patterns of reduced susceptibility, defined as an increasing trend in the minimum inhibitory concentration (MIC) values. The analysis involved learning the network structure, identifying the best fitting graphical mode, and learning the parameters in the final model to quantify joint probabilities.ResultsThe graph identified that as susceptibility to one antibiotic decreases, so does susceptibility to other antibiotics in the same or similar class. The following antibiotics were connected in the final graphical model: ampicillin was connected to ceftiofur; spectinomycin was connected with trimethoprim-sulfamethoxazole, and this association was mediated by farm; florfenicol was connected with tetracycline.ConclusionsBayesian network analysis can elucidate complex relationships between MIC patterns. MIC values may be associated within and between drug classes, and some associations may be correlated with farm of sample origin. Treating MICs as discretized variables and testing for joint associations in trends may overcome common research problems surrounding the lack of clinical breakpoints.

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Phenotypic antimicrobial resistance in Escherichia coli strains isolated from swine husbandries in North Western Germany – temporal patterns in samples from laboratory practice from 2006 to 2017

Cited by 15 publications

References 43 publications

A Joint Regional Analysis of Resistance Combinations in Escherichia coli in Humans and Different Food-Producing Animal Populations in Germany Between 2014 and 2017

A Joint Regional Analysis of Resistance Combinations in Escherichia coli in Humans and Different Food-Producing Animal Populations in Germany Between 2014 and 2017

Plasmid mediated colistin resistant mcr-1 and co-existence of OXA-48 among Escherichia coli from clinical and poultry isolates: first report from Nepal

Identifying Associations in Minimum Inhibitory Concentration Values of Escherichia coli Samples Obtained From Weaned Dairy Heifers in California Using Bayesian Network Analysis

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