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GEN News Highlights : Apr 10, 2014
Super Sequencing Predicts Toxicity of Superbug Strains
As the cost and speed of genome sequencing decreases, the technique promises myriad clinical applications, including the sequencing of infecting organisms. With an infecting organism’s entire gene sequence in hand, a clinician could select an appropriate treatment—or even personalize it, maximizing the benefit for an individual patient who may, for example, be fighting a particularly virulent bacterial strain, perhaps even a strain of the dreaded MRSA, or methicillin-resistant Staphylococcus aureus.
In the case of MRSA, multiple lineages have developed, some more virulent than others. Since virulence depends, in part, on toxicity, or the bacterium’s ability to damage a host’s tissue, clinicians have been interested in finding ways to assess the toxicity of MRSA and other bacteria. To date, the standard approach used to assess MRSA’s toxicity has focused on a single or small number of genes and proteins. This approach, however, has had only mixed success. Toxicity, it turns out, is a complex trait, one that is encoded by many genetic loci.
To better grapple with MRSA’s toxicity, researchers at the University of Bath and the University of Exeter applied the technique of whole-genome sequencing, as they explained in an article they published April 9 in Genome Research. According to this article, which carries the title “Predicting the virulence of MRSA from its genome sequence,” the researchers used whole-genome sequences from 90 MRSA isolates to identify over 100 genetic loci that made an individual isolate either high or low toxicity. The researchers were surprised to find that isolates from the same ST239 clone varied hugely in toxicity.
Besides identifying a large number of loci, the researchers also uncovered a putative network of epistatically interacting loci that significantly associated with toxicity. “Despite this apparent complexity in toxicity regulation,” the authors wrote, “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.”
In short, the researchers identified a common genetic signature shared by all the highly toxic strains. By looking for this signature, they were able to predict which isolates were the most toxic and therefore would cause severe disease.
Lead author of the study, Ruth Massey, Ph.D., a senior lecturer at the University of Bath, remarked that in the future “it will become feasible to take a swab from a patient, sequence the genome of the bacterium causing the infection, and then use this to predict the toxicity of the infection.”
“Clinicians will then be able to tailor the treatment to the specific infection,” Dr. Massey added. “This technique can tell them which combination of antibiotics will be most effective, or tell them which drugs to administer to dampen the toxicity of the infection.”
Mario Recker, Ph.D., associate professor in applied mathematics at the University of Exeter and co-author of the paper, said the research could provide pivotal insight into the virulence of MRSA. “By using whole-genome sequences, we have been able to predict which would be most toxic and so therefore would be more likely to cause severe disease. Having identified these novel genetic loci will also shed more light upon the complex machinery regulating bacterial virulence.”
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