The emergence and spread of antibiotic resistance among pathogenic bacteria has been a rising problem for public health in recent decades. It is becoming increasingly recognized that not only antibiotic resistance genes (ARGs) encountered in clinical pathogens are of relevance, but rather, all pathogenic, commensal as well as environmental bacteria—and also mobile genetic elements and bacteriophages—form a reservoir of ARGs (the resistome) from which pathogenic bacteria can acquire resistance via horizontal gene transfer (HGT). HGT has caused antibiotic resistance to spread from commensal and environmental species to pathogenic ones, as has been shown for some clinically important ARGs. Of the three canonical mechanisms of HGT, conjugation is thought to have the greatest influence on the dissemination of ARGs. While transformation and transduction are deemed less important, recent discoveries suggest their role may be larger than previously thought. Understanding the extent of the resistome and how its mobilization to pathogenic bacteria takes place is essential for efforts to control the dissemination of these genes. Here, we will discuss the concept of the resistome, provide examples of HGT of clinically relevant ARGs and present an overview of the current knowledge of the contributions the various HGT mechanisms make to the spread of antibiotic resistance.
Our study highlights the potential of PCR-based targeted metagenomics as an unbiased and sensitive method to screen for the carriage of the mcr-1 gene and suggests that mcr-1 is widespread in various parts of the world. The observation that one participant was found to be positive before travel suggests that mcr-1 may already have disseminated to the microbiomes of Dutch residents at a low prevalence, warranting a more extensive investigation of its prevalence in the general population and possible sources.
Vancomycin-resistant enterococci (VRE) can rapidly spread through hospitals. Therefore, our hospital employs a screening program whereby rectal swabs are screened for the presence of vanA and vanB , and only PCR-positive broths are cultured on VRE selection agar. Early November 2016, a clinical vanA- / vanB -negative VRE isolate was detected in a vanA/vanB -screening-negative patient, giving the possibility that an undetected VRE might be spreading within our hospital. Whole-genome-sequencing of the isolate showed that resistance was vanD -mediated and core genome multilocus sequence typing showed it was a rare type: ST17/CT154. To determine the prevalence of vanA/B/C/D -carrying enterococci, we designed a real-time PCR for vanC1/2/3 and vanD and screened rectal swabs from 360 patients. vanD was found in 27.8% of the patients, yet culture demonstrated only E . faecium from vanA -positive broths and E . gallinarum from vanC1 -positive broths. No vanD -positive VRE were found, limiting the possibility of nosocomial spread of this VRE. Moreover, the high prevalence of non-VRE vanD in rectal swabs makes it unfeasible to include the vanD PCR in our VRE screening. However, having validated the vanC1/2/3 and vanD PCRs allows us to rapidly check future vanA/B -negative VRE for the presence of vanC and vanD genes.
Background Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigates how patient movements between hospital wards and their antibiotic use can explain the colonisation of patients with VRE. Methods Intrahospital patient movements, antibiotic use and PCR screening data were used from a hospital in the Netherlands. The PageRank algorithm was used to calculate two daily centrality measures based on the spatiotemporal graph to summarise the flow of patients and antibiotics at the ward level. A decision tree model was used to determine a simple set of rules to estimate the daily probability of patient VRE colonisation for each hospital ward. The model performance was improved using a random forest model and compared using 30% test sample. Results Centrality covariates summarising the flow of patients and their antibiotic use between hospital wards can be used to predict the daily colonisation of VRE at the hospital ward level. The decision tree model produced a simple set of rules that can be used to determine the daily probability of patient VRE colonisation for each hospital ward. An acceptable area under the ROC curve (AUC) of 0.755 was achieved using the decision tree model and an excellent AUC of 0.883 by the random forest model on the test set. These results confirms that the random forest model performs better than a single decision tree for all levels of model sensitivity and specificity on data not used to estimate the models. Conclusion This study showed how the movements of patients inside hospitals and their use of antibiotics could predict the colonisation of patients with VRE at the ward level. Two daily centrality measures were proposed to summarise the flow of patients and antibiotics at the ward level. An early warning system for VRE can be developed to test and further develop infection prevention plans and outbreak strategies using these results.
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