Biology subjects

Ballard, S.

Publications and source records attributed to Ballard, S..

2 recordsLinked to original sources

bioRxiv2018

The changing landscape of VREfm in Victoria, Australia: a state-wide genomic snapshot

Vancomycin-resistant Enterococcus faecium (VREfm) represent a major source of nosocomial infection worldwide. In Australia, the vanB genotype is dominant; however there has been a recent increase in the predominantly plasmid-encoded vanA genotype, prompting investigation into the genomic epidemiology of VREfm in this context.\n\nMaterials and MethodsA cross-sectional study of VREfm in Victoria, Australia (Nov.10th - Dec.9th, 2015). A total of 321 VREfm isolates (from 286 patients) were collected and whole-genome sequenced with Illumina NextSeq. Single nucleotide polymorphisms (SNPs) were used to assess relatedness. Multi-locus sequence types (STs), and genes associated with resistance and virulence were identified. The vanA-harbouring plasmid from an isolate from each ST was assembled using long-read data.\n\nResultsvanA-VREfm comprised 17.8% of isolates. ST203, ST80 and a pstS(-) clade, ST1421, predominated (30.5%, 30.5% and 37.2% of vanA-VREfm, respectively). Most vanB-VREfm were ST796 (77.7%). vanA-VREfm isolates were closely-related within hospitals vs. between them (core SNPs 10 [interquartile range 1-357] vs. 356 [179-416] respectively), suggesting discrete introductions of vanA-VREfm, with subsequent intra-hospital transmission. In contrast, vanB-VREfm had similar core SNP distributions within vs. between hospitals, due to widespread dissemination of ST796. Overall, vanA-harbouring plasmids differed across STs, and with exception of ST78 and ST796, Tn1546 transposons also varied.\n\nConclusionsvanA-VREfm in Victoria is associated with multiple STs, and is not solely mediated by a single shared plasmid/Tn1546 transposon; clonal transmission appears to play an important role, predominantly within, rather than between, hospitals. In contrast, vanB-VREfm appears to be well-established and widespread across Victorian healthcare institutions.

genomics↗

bioRxiv2017

A Supervised Statistical Learning Approach For Accurate Legionella pneumophila Source Attribution During Outbreaks

Public health agencies are increasingly relying on genomics during Legionnaires disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on\n\nL. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia between 1994 and 2014. This collection included one of the largest reported Legionnaires disease outbreaks, involving 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures, and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires disease within a UK hospital and observed model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.

microbiology↗