Use of a LightCycler gyrA mutation assay for rapid identification of mutations conferring decreased susceptibility to ciprofloxacin in multiresistant Salmonella enterica serotype typhimurium DT104 isolates

A LightCycler-based PCR-hybridization gyrA mutation assay (GAMA) was developed to rapidly detect gyrA point mutations in multiresistant (MR) Salmonella enterica serotype Typhimurium DT104 with decreased susceptibility to ciprofloxacin (MIC, 0.25 to 1.0 mg/liter). Ninety-two isolates (49 human, 43 animal) were tested with three individual oligonucleotide probes directed against an Asp-87-to-Asn (GAC --> AAC) mutation, an Asp-87-to-Gly (GAC --> GGC) mutation, and a Ser-83-to-Phe (TCC --> TTC) mutation. Strains homologous to the probes could be distinguished from strains that had different mutations by their probe-target melting temperatures. Thirty-seven human and 30 animal isolates had an Asp-87-to-Asn substitution, 6 human and 6 animal isolates had a Ser-83-to-Phe substitution, and 5 human and 2 animal isolates had an Asp-87-to-Gly substitution. The remaining six strains all had mismatches with the three probes and therefore different gyrA mutations. The sequencing of gyrA from these six isolates showed that one human strain and two animal strains had an Asp-87-to-Tyr (GAC --> TAC) substitution and two animal strains had a Ser-83-to-Tyr (TCC --> TAC) substitution. One animal strain had no gyrA mutation, suggesting that this isolate had a different mechanism of resistance. Fifty-eight of the strains tested were indistinguishable by several different typing methods including antibiograms, pulsed-field gel gel electrophoresis, and plasmid profiling, although they could be further subdivided according to gyrA mutation. This study confirmed that MR DT104 with decreased susceptibility to ciprofloxacin from humans and food animals in England and Wales may have arisen independently against a background of clonal spread of MR DT104.

Detection of gyrA mutations in quinolone-resistant Salmonella enterica by denaturing high-performance liquid chromatography

Denaturing high-performance liquid chromatography (DHPLC) was evaluated as a rapid screening and identification method for DNA sequence variation detection in the quinolone resistance-determining region of gyrA from Salmonella serovars. A total of 203 isolates of Salmonella were screened using this method. DHPLC analysis of 14 isolates representing each type of novel or multiple mutations and the wild type were compared with LightCycler-based PCR-gyrA hybridization mutation assay (GAMA) and single-strand conformational polymorphism (SSCP) analyses. The 14 isolates gave seven different SSCP patterns, and LightCycler detected four different mutations. DHPLC detected 11 DNA sequence variants at eight different codons, including those detected by LightCycler or SSCP. One of these mutations was silent. Five isolates contained multiple mutations, and four of these could be distinguished from the composite sequence variants by their DHPLC profile. Seven novel mutations were identified at five different loci not previously described in quinolone-resistant salmonella. DHPLC analysis proved advantageous for the detection of novel and multiple mutations. DHPLC also provides a rapid, high-throughput alternative to LightCycler and SSCP for screening frequently occurring mutations.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.