“By understanding how resistance in bacteria arises, we can better combat its spread. This is crucial to protect public health and the healthcare system’s ability to treat infections,” says Erik Kristiansson, Professor at the Department of Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg.

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Almost a million bacteria

In the new study, published in Nature Communications, the researchers developed an AI model to analyze historical gene transfers between bacteria using information about the bacteria’s DNA, structure, and habitat. The model was trained on the genomes of almost a million bacteria, an extensive dataset compiled by the international research community over many years.

The unique thing about our study is, among other things, the very large amount of data used to train the model, which shows what a powerful tool AI and machine learning is for describing the complex, biological processes that make bacterial infections difficult to treat.

David Lund. Photo: Chalmers

“AI can be used to the best of its ability in complex contexts, with large amounts of data,” says David Lund, doctoral student at the Department of Mathematical Sciences at Chalmers and the University of Gothenburg. “The unique thing about our study is, among other things, the very large amount of data used to train the model, which shows what a powerful tool AI and machine learning is for describing the complex, biological processes that make bacterial infections difficult to treat.”

Conclusions about when antibiotic resistance arises

The study shows in which environments the resistance genes were transferred between different bacteria, and what it is that makes some bacteria more likely than others to swap genes with each other.

We see that bacteria found in humans and water treatment plants have a higher probability of becoming resistant through gene transfer.

“We see that bacteria found in humans and water treatment plants have a higher probability of becoming resistant through gene transfer. These are environments where bacteria carrying resistance genes encounter each other, often in the presence of antibiotics,” says Lund.

Another important factor that increases the likelihood that resistance genes will “jump” from one bacterium to another is the genetic similarity of the bacteria. When a bacterium takes up a new gene, energy is required to store the DNA and produce the protein that the gene codes for, which means a cost for the bacterium.

“Most resistance genes are shared between bacteria with a similar genetic structure. We believe that this reduces the cost of taking up new genes. We are continuing the research to understand the mechanisms that control this process more precisely,” says Kristiansson.

Improve molecular diagnostics

The model’s performance was tested by evaluating it against bacteria, where the researchers knew that the transfer of resistance genes had occurred, but where the AI model was not told in advance. This was used as a kind of exam, where only the researchers had the answers. In four cases out of five, the model could predict whether a transfer of resistance genes would occur. Erik Kristiansson says that future models will be able to be even more accurate, partly by refining the AI model itself and partly by training it on even larger data.

Erik Kristiansson. Photo: Chalmers

“AI and machine learning make it possible to efficiently analyse and interpret the enormous amounts of data available today. This means that we can really work data-driven to answer complex questions that we have been wrestling with for a long time, but also ask completely new questions”, says Kristiansson.

The researchers hope that in the future, the AI model can be used in systems to quickly identify whether a new resistance gene is at risk of being transferred to pathogenic bacteria, and translate this into practical measures.

“For example, AI models could be used to improve molecular diagnostics to find new forms of multi-resistant bacteria or for monitoring wastewater treatment plants and environments where antibiotics are present,” says Kristiansson.

Source: Chalmers University of Technology