Predicting the virulence of MRSA from its genome sequence

Abstract

Microbial virulence is a complex and often multifactorial phenotype, intricately linked to a pathogen's evolutionary trajectory. Toxicity, the ability to destroy host cell membranes, and adhesion, the ability to adhere to human tissues, are the major virulence factors of many bacterial pathogens, including Staphylococcus aureus. Here, we assayed the toxicity and adhesiveness of 90 MRSA (methicillin resistant S. aureus) isolates and found that while there was remarkably little variation in adhesion, toxicity varied by over an order of magnitude between isolates, suggesting different evolutionary selection pressures acting on these two traits. We performed a genome-wide association study (GWAS) and identified a large number of loci, as well as a putative network of epistatically interacting loci, that significantly associated with toxicity. Despite this apparent complexity in toxicity regulation, a predictive model based on a set of significant single nucleotide polymorphisms (SNPs) and insertion and deletions events (indels) showed a high degree of accuracy in predicting an isolate's toxicity solely from the genetic signature at these sites. Our results thus highlight the potential of using sequence data to determine clinically relevant parameters and have further implications for understanding the microbial virulence of this opportunistic pathogen.

Large-scale genomic sequencing of extraintestinal pathogenic Escherichia coli strains

“…It is now appreciated that large numbers of microbial genome sequences can be used for robust genome-wide association studies (GWAS), enabling the detection of genetic factors underlying phenotypic variation (Falush and Bowden 2006;Farhat et al 2013;Sheppard et al 2013;Alam et al 2014;Laabei et al 2014). Here, in light of the open nature of the E. coli pan-genome, we observed a significant number of novel sequences present neither in reference genomes nor previous isolates with each additional strain that was sequenced.…”

Harmonized terms, concepts and metadata for microbiological risk assessment models: The basis for knowledge integration and exchange

“…However in the future, other areas, like chemical risk assessment, and other modelling approaches, as e.g. Bayesian networks (Beaudequin et al, 2015) or machine learning (Laabei et al, 2014), should be addressed. As this research has been initiated as an open community effort it can take up suggestions on future development goals from the scientific community.…”

Bacterial genomic epidemiology, from local outbreak characterization to species-history reconstruction

“…These difficulties have not discouraged pioneering projects that use genome-wide association studies to link genotype to phenotype, in an effort to obtain diagnostic informations from the now quick and cheap whole-genome sequences. Laabei and colleagues 74 applied such an approach to MRSA, developing a model that can predict with a high degree of accuracy the toxicity of an isolate based on the sequence of signature sites. Another study integrated a genomic approach with gene expression analysis, performed with RNA-seq, to determine antibiotic resistance profiles in E. coli.…”