Wednesday 9 May 2018 9:21am
A University of Otago expert is part of an international collaboration that has created a new machine learning tool that can detect whether emerging strains of the bacterium Salmonella are more likely to cause life-threatening bloodstream infections rather than only food poisoning.
The software tool, created by a scientist at the Wellcome Sanger Institute in the United Kingdom, the University of Otago, and the Helmholtz Institute for RNA-based Infection Research in Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are a greater concern to public health.
A study published in PLOS Genetics today, details how the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale.
The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as Gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi, which causes typhoid fever.
Dr Paul Gardner of the University of Otago's Department of Biochemistry says the method correctly classifies which Salmonella types are going to be invasive and which ones are not, by analysing genome sequences.
“Essentially we can answer the question of: Do we need to worry about this as it could cause quite a lot of people to become sick, or is this not going to trouble the community too much,” Dr Gardner says.
To understand the genetic changes that determine whether an emerging strain of Salmonella will cause food poisoning versus a more severe infection, researchers built the machine learning model that analyses which mutations play an important role.
The team trained the model using old lineages of Salmonella that are evolutionarily distinct, including six Salmonella bacteria that caused invasive infections, and seven gastrointestinal strains of the bacteria. The machine learning model identified almost 200 genes involved in determining whether the bacterium will cause food poisoning or is better adapted to causing invasive infection.
The study was co-lead by Dr Nicole Wheeler of the Wellcome Sanger Institute.
“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,” Dr Wheeler says.
Developers say the machine learning tool, which produces an invasiveness index based on a “Random Forest” model, is not limited to Salmonella and could be used to study other factors such as emerging antibiotic resistance in any bacterium, and could be used in real time to identify a dangerous strain of bacteria before it spreads to cause an outbreak.
For further information, contact:
Dr Paul Gardner
Department of Biochemistry
University of Otago
Senior Communications Adviser
University of Otago
Mob 21 279 5016
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