Biology subjects

Baines, S.

Publications and source records attributed to Baines, S..

3 recordsLinked to original sources

bioRxiv2018

Genomic Exploration of Within-Host Microevolution Reveals a Distinctive Molecular Signature of Persistent Staphylococcus aureus Bacteraemia

BackgroundLarge-scale genomic studies of within-host evolution during Staphylococcus aureus bacteraemia (SAB) are needed to understanding bacterial adaptation underlying persistence and thus refining the role of genomics in management of SAB. However, available comparative genomic studies of sequential SAB isolates have tended to focus on selected cases of unusually prolonged bacteraemia, where secondary antimicrobial resistance has developed. To understand the bacterial genomic evolution during SAB more broadly, we applied whole genome sequencing to a large collection of sequential isolates obtained from patients with persistent or relapsing bacteraemia.\n\nResultsWe show that, while adapation pathways are heterogenous and episode-specific, isolates from persistent bacteraemia have a distinctive molecular signature, characterised by a low mutation frequency and high proportion of non-silent mutations. By performing an extensive analysis of structural genomic variants in addition to point mutations, we found that these often overlooked genetic events are commonly acquired during SAB. We discovered that IS256 insertion may represent the most effective driver of within-host microevolution in selected lineages, with up to three new insertion events per isolate even in the absence of other mutations. Genetic mechanisms resulting in significant phenotypic changes, such as increases in vancomycin resistance, development of small colony phenotypes, and decreases in cytotoxicity, included mutations in key genes (rpoB, stp, agrA) and an IS256 insertion upstream of the walKR operon.\n\nConclusionsThis study provides for the first time a large-scale analysis of within-host evolution during invasive S. aureus infection and describes specific patterns of adaptation that will be informative for both understanding S. aureus pathoadaptation and utilising genomics for management of complicated S. aureus infections.

genomics↗

bioRxiv2018

Comprehensive antibiotic-linked mutation assessment by Resistance Mutation Sequencing (RM-seq)

Acquired mutations are a major mechanism of bacterial antibiotic resistance generation and dissemination, and can arise during treatment of infections. Early detection of sub-populations of resistant bacteria harbouring defined resistance mutations could prevent inappropriate antibiotic prescription. Here we present RM-seq, a new amplicon-based DNA sequencing workflow based on single molecule barcoding coupled with deep-sequencing that enables the high-throughput characterisation and sensitive detection of resistance mutations from complex mixed populations of bacteria. We show that RM-seq reduces both background sequencing noise and PCR amplification bias and allows highly sensitive identification and accurate quantification of antibiotic resistant sub-populations, with relative allele frequencies as low as 10-4. We applied RM-seq to identify and quantify rifampicin resistance mutations in Staphylococcus aureus using pools of 10,000 in vitro selected clones and identified a large number of previously unknown resistance-associated mutations. Targeted mutagenesis and phenotypic resistance testing was used to validate the technique and demonstrate that RM-seq can be used to link subsets of mutations with clinical resistance breakpoints at high-throughput using large pools of in vitro selected resistant clones. Differential analysis of the abundance of resistance mutations after a selection bottleneck detected antimicrobial cross-resistance and collateral sensitivity-conferring mutations. Using a mouse infection model and human clinical samples, we also demonstrate that RM-seq can be effectively applied in vivo to track complex mixed populations of S. aureus and another major human pathogen, Mycobacterium tuberculosis during infections. RM-seq is a powerful new tool to both detect and functionally characterise mutational antibiotic resistance.

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↗