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Unraveling antimicrobial resistance genes and phenotype patterns among Enterococcus faecalis isolated from retail chicken products in Japan.

Hidano A, Yamamoto T, Hayama Y, Muroga N, Kobayashi S, Nishida T, Tsutsui T - PLoS ONE (2015)

Bottom Line: Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed.Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance.The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.

View Article: PubMed Central - PubMed

Affiliation: Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture, and Food Research Organization, Tsukuba, Ibaraki, Japan; EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.

ABSTRACT
Multidrug-resistant enterococci are considered crucial drivers for the dissemination of antimicrobial resistance determinants within and beyond a genus. These organisms may pass numerous resistance determinants to other harmful pathogens, whose multiple resistances would cause adverse consequences. Therefore, an understanding of the coexistence epidemiology of resistance genes is critical, but such information remains limited. In this study, our first objective was to determine the prevalence of principal resistance phenotypes and genes among Enterococcus faecalis isolated from retail chicken domestic products collected throughout Japan. Subsequent analysis of these data by using an additive Bayesian network (ABN) model revealed the co-appearance patterns of resistance genes and identified the associations between resistance genes and phenotypes. The common phenotypes observed among E. faecalis isolated from the domestic products were the resistances to oxytetracycline (58.4%), dihydrostreptomycin (50.4%), and erythromycin (37.2%), and the gene tet(L) was detected in 46.0% of the isolates. The ABN model identified statistically significant associations between tet(L) and erm(B), tet(L) and ant(6)-Ia, ant(6)-Ia and aph(3')-IIIa, and aph(3')-IIIa and erm(B), which indicated that a multiple-resistance profile of tetracycline, erythromycin, streptomycin, and kanamycin is systematic rather than random. Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed. Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance. The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.

No MeSH data available.


Related in: MedlinePlus

Final globally optimal additive Bayesian network model after adjustment for over-fitting.Final additive Bayesian network model after removing arcs that appeared in <50% of bootstrappings for the interrelationships between selected antimicrobial resistance genes and phenotypes. Solid lines and dashed lines represent positive and negative associations between variables, respectively. Fig. 2 lists the variable names.
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pone.0121189.g003: Final globally optimal additive Bayesian network model after adjustment for over-fitting.Final additive Bayesian network model after removing arcs that appeared in <50% of bootstrappings for the interrelationships between selected antimicrobial resistance genes and phenotypes. Solid lines and dashed lines represent positive and negative associations between variables, respectively. Fig. 2 lists the variable names.

Mentions: Next, an ABN model was constructed to analyze the interrelationships between resistance genes and phenotypes in E. faecalis. We excluded these resistance genes that were detected in <10% of E. faecalis isolates: aac(6’)-Ie-aph(2”)-Ia, erm(A), mef, and cfr. The resistance phenotypes for dihydrostreptomycin, erythromycin, and oxytetracycline were only included in the model because other phenotypes occurred in <10% of isolates. Each resistance phenotype for dihydrostreptomycin and oxytetracycline was further categorized as high (dihydrostreptomycin, >512 μg/mL; oxytetracycline, >64 μg/mL) or otherwise low. Erythromycin resistance could not be categorized, because the MICs of very few isolates were between the upper boundary (128 μg/mL) and the breakpoint (8 μg/mL). Therefore, 5 nodes representing resistance phenotypes were included in the model. We determined that a maximum of 3 parents per node was sufficient to maximize the fit of the model. Subsequently, we performed an exact search—with the upper limit set at 3 parent nodes for each node—in order to identify the tentative associations between genes and phenotypes (Fig. 2). After adjustment for over-fitting, the association between ant(6)-Ia and erm(B) was not robust and was therefore excluded (Fig. 3). This removed unstable marginal densities related to the node ant(6)-Ia (S1 and S3 Figs). Table 3 shows the posterior marginal log odds for each arc. The presence of tet(L) was positively associated with high-level oxytetracycline resistance (>64 μg/mL), whereas the presence of tet(O) and tet(M) was positively associated with low-level oxytetracycline resistance. These analyses revealed complex interrelationships between genes, as discussed in the next section.


Unraveling antimicrobial resistance genes and phenotype patterns among Enterococcus faecalis isolated from retail chicken products in Japan.

Hidano A, Yamamoto T, Hayama Y, Muroga N, Kobayashi S, Nishida T, Tsutsui T - PLoS ONE (2015)

Final globally optimal additive Bayesian network model after adjustment for over-fitting.Final additive Bayesian network model after removing arcs that appeared in <50% of bootstrappings for the interrelationships between selected antimicrobial resistance genes and phenotypes. Solid lines and dashed lines represent positive and negative associations between variables, respectively. Fig. 2 lists the variable names.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4363150&req=5

pone.0121189.g003: Final globally optimal additive Bayesian network model after adjustment for over-fitting.Final additive Bayesian network model after removing arcs that appeared in <50% of bootstrappings for the interrelationships between selected antimicrobial resistance genes and phenotypes. Solid lines and dashed lines represent positive and negative associations between variables, respectively. Fig. 2 lists the variable names.
Mentions: Next, an ABN model was constructed to analyze the interrelationships between resistance genes and phenotypes in E. faecalis. We excluded these resistance genes that were detected in <10% of E. faecalis isolates: aac(6’)-Ie-aph(2”)-Ia, erm(A), mef, and cfr. The resistance phenotypes for dihydrostreptomycin, erythromycin, and oxytetracycline were only included in the model because other phenotypes occurred in <10% of isolates. Each resistance phenotype for dihydrostreptomycin and oxytetracycline was further categorized as high (dihydrostreptomycin, >512 μg/mL; oxytetracycline, >64 μg/mL) or otherwise low. Erythromycin resistance could not be categorized, because the MICs of very few isolates were between the upper boundary (128 μg/mL) and the breakpoint (8 μg/mL). Therefore, 5 nodes representing resistance phenotypes were included in the model. We determined that a maximum of 3 parents per node was sufficient to maximize the fit of the model. Subsequently, we performed an exact search—with the upper limit set at 3 parent nodes for each node—in order to identify the tentative associations between genes and phenotypes (Fig. 2). After adjustment for over-fitting, the association between ant(6)-Ia and erm(B) was not robust and was therefore excluded (Fig. 3). This removed unstable marginal densities related to the node ant(6)-Ia (S1 and S3 Figs). Table 3 shows the posterior marginal log odds for each arc. The presence of tet(L) was positively associated with high-level oxytetracycline resistance (>64 μg/mL), whereas the presence of tet(O) and tet(M) was positively associated with low-level oxytetracycline resistance. These analyses revealed complex interrelationships between genes, as discussed in the next section.

Bottom Line: Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed.Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance.The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.

View Article: PubMed Central - PubMed

Affiliation: Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture, and Food Research Organization, Tsukuba, Ibaraki, Japan; EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.

ABSTRACT
Multidrug-resistant enterococci are considered crucial drivers for the dissemination of antimicrobial resistance determinants within and beyond a genus. These organisms may pass numerous resistance determinants to other harmful pathogens, whose multiple resistances would cause adverse consequences. Therefore, an understanding of the coexistence epidemiology of resistance genes is critical, but such information remains limited. In this study, our first objective was to determine the prevalence of principal resistance phenotypes and genes among Enterococcus faecalis isolated from retail chicken domestic products collected throughout Japan. Subsequent analysis of these data by using an additive Bayesian network (ABN) model revealed the co-appearance patterns of resistance genes and identified the associations between resistance genes and phenotypes. The common phenotypes observed among E. faecalis isolated from the domestic products were the resistances to oxytetracycline (58.4%), dihydrostreptomycin (50.4%), and erythromycin (37.2%), and the gene tet(L) was detected in 46.0% of the isolates. The ABN model identified statistically significant associations between tet(L) and erm(B), tet(L) and ant(6)-Ia, ant(6)-Ia and aph(3')-IIIa, and aph(3')-IIIa and erm(B), which indicated that a multiple-resistance profile of tetracycline, erythromycin, streptomycin, and kanamycin is systematic rather than random. Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed. Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance. The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.

No MeSH data available.


Related in: MedlinePlus