Effects of chlortetracycline and copper supplementation on antimicrobial resistance of fecal Escherichia coli from weaned pigs

Agga, Getahun E.; Scott, H. M.; Amachawadi, Raghavendra G.; Nagaraja, T. G.; Vinasco, Javier; Bai, Jianfa; Norby, Bo; Renter, David G.; Dritz, S. S.; Nelssen, Jim L.; Tokach, Mike D

doi:10.1016/j.prevetmed.2014.02.010

Cited by 54 publications

(69 citation statements)

References 37 publications

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“…A full description of the study design, study population, sampling scheme, and determination of phenotypic antimicrobial resistance of the E. coli isolates is available elsewhere (Agga et al, 2014). Briefly, the study design was a full factorial cluster randomized trial in which 32 pens (each with five pigs) were randomly allocated to control, CTC, copper or copper plus CTC groups.…”

Section: Phenotypic Antimicrobial Resistance Datamentioning

confidence: 99%

“…The antimicrobial classes were: aminoglycosides, ␤-lactam/␤-lactamase inhibitor combinations, cephems, folate pathway inhibitors, penicillins, phenicols, quinolones and tetracyclines. The macrolides class, represented by azithromycin, was excluded since all the isolates were not tested for azithromycin resistance (Agga et al, 2014).…”

Section: Phenotypic Antimicrobial Resistance Datamentioning

confidence: 99%

“…Epidemiological studies of antimicrobial resistance (AMR) typically involve testing of isolated bacterial strains against a panel of various antimicrobial agents which results in a multivariate outcome (Agga, 2013). However, statistical analysis of phenotypic AMR data are largely univariate focusing on individual outcomes of the antimicrobials tested (Scott et al, 2005;Alali et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate analysis on the other hand accounts for such co-dependences among multiple AMR outcomes. Bivariate and multivariate probit models (Agga et al, 2014(Agga et al, , 2015 for multivariate analysis of multiple binary outcomes, and multivariate linear regression model for multiple quantitative outcomes (Agga et al, 2015) were previously applied for the analysis of AMR data. Other multivariate approaches (Agga, 2013) that were used for the analysis of AMR data include cluster analysis (Berge et al, 2003;Alali et al, 2010), factor analysis (Wagner et al, 2003) and more recently Bayesian networks (Ludwig et al, 2013;Ward and Lewis, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore the objective of this paper was to show the applicability of generalized ordinal logistic regression model for the analysis of MDR data. The MDR data used for the present analysis is based on susceptibility results of Escherichia coli isolates from a previously reported trial conducted to investigate the impact of copper and CTC supplementation of weaned pigs on antimicrobial resistance of fecal bacteria (Agga et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Agga

Scott

2015

Preventive Veterinary Medicine

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a b s t r a c tStatistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR] = 1.03; 95% CI = 0.73-1.47) and CTC (Prop OR = 1.1; 95% CI = 0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated.Published by Elsevier B.V.

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Section: Phenotypic Antimicrobial Resistance Datamentioning

confidence: 99%

Section: Phenotypic Antimicrobial Resistance Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Agga

Scott

2015

Preventive Veterinary Medicine

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Beef cattle feedlot runoff impacts on soil antimicrobial resistance

Speicher,

Miller,

Durso

et al. 2024

Agrosystems Geosci & Env

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Field application of beef cattle feedlot runoff may transport manure‐borne microbes and antibiotic resistant bacteria to agricultural soils eventually impacting deeper soils and groundwater. To evaluate this potential, total soil, antibiotic resistance (AR), and fecal indicator bacteria (Escherichia coli and Enterococcus) and the presence/abundance of AR genes were examined to a depth of 1.8 m in an agricultural field receiving long‐term application of feedlot runoff and compared to a nearby pasture receiving no runoff. While plate counts of total soil bacteria and cefotaxime‐resistant, erythromycin‐resistant, and tetracycline‐resistant bacteria decreased with depth on both fields (p < 0.001) by an average 2‐log10 colony forming unit g−1 to 1.8‐m depth, field differences were only observed with greater abundances of total soil and erythromycin‐resistant bacteria (p ≤ 0.026) in the runoff‐amended versus control field soils. Soil bacterial and fecal indicator bacterial isolates evaluated phenotypically for resistance to 12 antibiotics varied in range and sensitivity. Using a culture‐independent approach, erm(C) and tet(Q) were detected using polymerase chain reaction in 31% and 58% of runoff‐field samples throughout the soil profile. Detection of erm(C) and tet(Q) in the control field soil profile was less frequent (0% and 11%, respectively). Two other genes, erm(A) and tet(X) were not detected in any soil samples. Based upon these results, long‐term applications of beef cattle feedlot runoff may increase the total abundance of microorganisms in the surface and shallow soil, but the relative enrichment of AR was dependent upon the type of resistance evaluated and, more specifically, the genes targeted for analysis.

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Combination of antibiotics suppressed the increase of a part of ARGs in fecal microorganism of weaned pigs

Chu

et al. 2016

Environ Sci Pollut Res

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The presence of antibiotic resistance genes (ARGs) is one of the most important public health concerns. Six tetracycline resistance genes (TRGs-tetA, tetC, tetL, tetO, tetW, and tetX) were quantified using real-time quantitative polymerase chain reaction (qPCR) in the fecal microorganisms of weaned pigs. Two hundred 35-day-old weaned pigs were fed different dietary antibiotics for 28 days: (1) no antibiotic as the control treatment (CT); (2) chlortetracycline, bacitracin zinc and colistin sulfate (CBC); (3) bacitracin zinc and colistin sulfate (BC); and (4) chlortetracycline (CTC). The detection frequencies (DFs) of tetC, tetL, and tetW were 100 %; and the DFs of tetA, tetD, tetM, tetO, and tetX were 65 %. The relative abundances (tet/16S rRNA gene copy numbers) of six tet genes (tetA, tetC, tetL, tetO, tetW and tetX) were between 1.5 × 10(-4) and 2.0 × 10(-1). In the group CTC, the relative abundances of tetC (P < 0.01), tetL (P < 0.01), tetO (P < 0.05), tetW (P < 0.01), and tetX (P < 0.01) were greater than those of the group CT. Compared with the group CTC, the relative abundances of tetC (P < 0.01), tetL (P < 0.01), and tetW (P < 0.01) were decreased in the CBC and BC groups. These results indicate that a combination of different antibiotics suppressed the abundance increase of a part of tet genes, which suggests that a combination of antibiotics produces multiple selection pressures on fecal microorganism of pigs.

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Effects of chlortetracycline and copper supplementation on antimicrobial resistance of fecal Escherichia coli from weaned pigs

Cited by 54 publications

References 37 publications

Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Beef cattle feedlot runoff impacts on soil antimicrobial resistance

Combination of antibiotics suppressed the increase of a part of ARGs in fecal microorganism of weaned pigs

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