Dataset Information

Urinary tract infections in a South American population: dynamic spread of class 1 integrons and multidrug resistance by homologous and site-specific recombination.

ABSTRACT: One hundred four bacterial strains mediating urinary tract infections in separate individuals from a Uruguayan community were isolated. Forty-six strains conferred a multidrug resistance phenotype. All 104 strains were examined for the presence of class 1, 2, and 3 integrons. Class 1 integrons were found in 21 isolates across four distinct bacterial genera. A large class 1 integron in a Klebsiella pneumoniae strain was fully sequenced and was 29,093 bp in length. This integron probably arose by homologous recombination since it was embedded in a hybrid Tn21-like transposon backbone which comprised a Tn5036-like tnp transposition module at the IRi integron end and a Tn21 mer module at the IRt integron end. The parent integron/transposon that contributed the Tn5036 module was not related to Tn1696 since the integron insertion points in the transposon backbones were 16 bases apart. Examination of the other 20 class 1 integron-containing strains revealed further evidence of genetic exchange. This included a strain that possessed a Tn5036 module at the IRt end but not at the IRi end and another that possessed a tnp module beyond IRi that was a hybrid of Tn21 and Tn5051 and that is presumed to have arisen by site-specific recombination. This study highlights the ability of different genetic elements to act cooperatively to spread and rearrange antibiotic resistance in a community.

SUBMITTER: Marquez C

PROVIDER: S-EPMC2566090 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Urinary tract infections in a South American population: dynamic spread of class 1 integrons and multidrug resistance by homologous and site-specific recombination.

Márquez Carolina C Labbate Maurizio M Raymondo Claudia C Fernández Jimena J Gestal Alicia M AM Holley Marita M Borthagaray Graciela G Stokes H W HW

Journal of clinical microbiology 20080827 10

One hundred four bacterial strains mediating urinary tract infections in separate individuals from a Uruguayan community were isolated. Forty-six strains conferred a multidrug resistance phenotype. All 104 strains were examined for the presence of class 1, 2, and 3 integrons. Class 1 integrons were found in 21 isolates across four distinct bacterial genera. A large class 1 integron in a Klebsiella pneumoniae strain was fully sequenced and was 29,093 bp in length. This integron probably arose by ...[more]

PMID: 18753343

Similar Datasets

Project description:BACKGROUND:The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS:We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS:the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.

Dataset Information

Urinary tract infections in a South American population: dynamic spread of class 1 integrons and multidrug resistance by homologous and site-specific recombination.

Publications

Urinary tract infections in a South American population: dynamic spread of class 1 integrons and multidrug resistance by homologous and site-specific recombination.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure