The first report of the vanC1 gene in Enterococcus faecium isolated from a human clinical specimen

The vanC1 gene, which is chromosomally located, confers resistance to vancomycin and serves as a species marker for Enterococcus gallinarum. Enterococcus faecium TJ4031 was isolated from a blood culture and harbours the vanC1gene. Polymerase chain reaction (PCR) assays were performed to detect vanXYc and vanTc genes. Only the vanXYc gene was found in the E. faecium TJ4031 isolate. The minimum inhibitory concentrations of vancomycin and teicoplanin were 2 µg/mL and 1 µg/mL, respectively. Real-time reverse transcription-PCR results revealed that the vanC1and vanXYc genes were not expressed. Pulsed-field gel electrophoresis and southern hybridisation results showed that the vanC1 gene was encoded in the chromosome. E. faecalis isolated from animals has been reported to harbour vanC1gene. However, this study is the first to report the presence of the vanC1gene in E. faecium of human origin. Additionally, our research showed the vanC1gene cannot serve as a species-specific gene of E. gallinarum and that it is able to be transferred between bacteria. Although the resistance marker is not expressed in the strain, our results showed that E. faecium could acquire the vanC1gene from different species.

Gene ordering in partitive clustering using microarray expressions.

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions.Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution.