Epidemic multidrug-resistant (MDR-AmpC) Salmonella enterica serovar Newport strains contain three phage regions and a MDR resistance plasmid

Multidrug-resistant (MDR-AmpC) Salmonella enterica serovar Newport has caused serious disease in animals and humans in North America, whereas in the UK S. enterica serovar Newport is not associated with severe disease and usually sensitive to antibiotics; MDR S. Newport (not AmpC) strains have only been isolated from poultry. We found that UK poultry strains belonged to MLST type ST166 and were distinct from cattle isolates for being able to utilize D-tagotose and when compared by pulsed-field gel electrophoresis (PFGE), comparative genomic hybridization (CGH) and diversity arrays technology (DArT). Cattle strains belonged to the ST45 complex differing from ST166 at all seven loci. PFGE showed that 19 out of 27 cattle isolates were more than 85% similar to each other and some UK and US strains were indistinguishable. Both CGH and DArT identified genes (including phage-related ones) that were uniquely present in the US isolates and two such genes identified by DArT showed sequence similarities with the pertussis-like (artAB) toxin. This work demonstrates that MDR-AmpC S. Newport from the USA are genetically closely related to pan-susceptible strains from the UK, but contained three extra phage regions and a MDR plasmid.

Identification of genetic and phenotypic differences associated with prevalent and non-prevalent Salmonella Enteritidis phage types: analysis of variation in amino acid transport

In this study, differences at the genetic level of 37 Salmonella Enteritidis strains from five phage types (PTs) were compared using comparative genomic hybridization (CGH) to assess differences between PTs. There were approximately 400 genes that differentiated prevalent (4, 6, 8 and 13a) and sporadic (11) PTs, of which 35 were unique to prevalent PTs, including six plasmid-borne genes, pefA, B, C, D, srgC and rck, and four chromosomal genes encoding putative amino acid transporters. Phenotype array studies also demonstrated that strains from prevalent PTs were less susceptible to urea stress and utilized L-histidine, L-glutamine, L-proline, L-aspartic acid, gly-asn and gly-gln more efficiently than PT11 strains. Complementation of a PT11 strain with the transporter genes from PT4 resulted in a significant increase in utilization of the amino acids and reduced susceptibility to urea stress. In epithelial cell association assays, PT11 strains were less invasive than other prevalent PTs. Most strains from prevalent PTs were better biofilm formers at 37 degrees C than at 28 degrees C, whilst the converse was true for PT11 strains. Collectively, the results indicate that genetic and corresponding phenotypic differences exist between strains of the prevalent PTs 4, 6, 8 and 13a and non-prevalent PT11 strains that are likely to provide a selective advantage for strains from the former PTs and could help them to enter the food chain and cause salmonellosis.

A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

Background: Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity. Results: The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data. Conclusion: After carrying out the analysis and validation for three sequenced Escherichia coli strains, CGH microarray data from 19 E. coli O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes.