Lifesaving as it may be, using genomic sequencing to identify mutations in emerging strains of bacteria responsible for or at risk of causing an outbreak is a time consuming and labor-intensive process.
However, according to a press release published May 8 by the Wellcome Trust Sanger Institute in the U.K., a group of researchers have recently developed a new machine learning tool that can detect which emerging strains of bacteria are fatal before causing a widespread outbreak.
The new tool can rapidly identify genetic mutations in new invasive types of Salmonella and detect whether they're more likely to cause bloodstream infections rather than food poisoning, according to the research published online in PLOS Genetics on May 8.
"Using this tool, we can tackle massive data sets and get results in seconds. Ultimately, this work will have a big impact on the surveillance of dangerous bacteria in a way we haven't been able to before, not only in hospital wards, but at a global scale," said the study's co-lead author Nicole Wheeler, PhD, from the Wellcome Sanger Institute, in a prepared statement.
Wheeler and her colleagues trained the machine learning model using old Salmonella lineages that are "evolutionary distinct," according to the press release. The tool then could identify more than 200 genes responsible for determining whether the bacterium will cause food poisoning or invasive infection.
Since its development, the tool has been applied to strains of Salmonella currently emerging in Sub-Saharan Africa responsible for bloodstream infection, according to the release. However, the tool isn't exclusive to analyzing Salmonella, as it can also be used to detect emerging antibiotic resistance in bacterium.
"The machine learning tool is an advance compared to other methods as it not only searches for genes and mutations, it looks for the functional impacts mutations have in these bugs," said co-lead author Lars Barquist, PhD, from the Helmholtz Institute for RNA-based Infection Research in Germany, in a prepared statement. "It can tell us which mutations make pathogens better at spreading beyond the gut and causing a life-threatening disease rather than food poisoning. This will help in designing more effective treatments in the future."