Evolution of virulence in a novel family of transmissible mega-plasmids

Some Serratia entomophila isolates have been successfully exploited in biopesticides due to their ability to cause amber disease in larvae of the Aotearoa (New Zealand) endemic pasture pest, Costelytra giveni. Anti-feeding prophage and ABC toxin complex virulence determinants are encoded by a 153-kb single-copy conjugative plasmid (pADAP; amber disease-associated plasmid). Despite growing understanding of the S. entomophila pADAP model plasmid, little is known about the wider plasmid family. Here, we sequence and analyse mega-plasmids from 50 Serratia isolates that induce variable disease phenotypes in the C. giveni insect host. Mega-plasmids are highly conserved within S. entomophila, but show considerable divergence in Serratia proteamaculans with other variants in S. liquefaciens and S. marcescens, likely reflecting niche adaption. In this study to reconstruct ancestral relationships for a complex mega-plasmid system, strong co-evolution between Serratia species and their plasmids were found. We identify 12 distinct mega-plasmid genotypes, all sharing a conserved gene backbone, but encoding highly variable accessory regions including virulence factors, secondary metabolite biosynthesis, Nitrogen fixation genes and toxin-antitoxin systems. We show that the variable pathogenicity of Serratia isolates is largely caused by presence/absence of virulence clusters on the mega-plasmids, but notably, is augmented by external chromosomally encoded factors

Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis. METHODS: High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group. RESULTS: Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis. CONCLUSIONS: We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification