Novel Computational Method Used to Identify Antibiotic-Resistance Genes

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Several previously unknown genes that make bacteria resistant to last-resort antibiotics have been found by researchers at Chalmers University of Technology and the University of Gothenburg. The findings were published last week in Microbiome.

By analyzing large volumes of DNA data, with a new computational method that uses an optimized hidden Markov model, the research team found 76 new types of resistance genes. Several of these genes can provide bacteria with the ability to degrade carbapenems, the most powerful class of antibiotics used to treat multi-resistant bacteria.

"Our study shows that there are lots of unknown resistance genes. Knowledge about these genes makes it possible to more effectively find and hopefully tackle new forms of multi-resistant bacteria," says Erik Kristiansson, professor in biostatistics at Chalmers University and principal investigator of the study.

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"The more we know about how bacteria can defend themselves against antibiotics, the better are our odds for developing effective, new drugs," explains co-author Joakim Larsson, professor in environmental pharmacology and director of the Centre for Antibiotic Resistance Research at the University of Gothenburg.

Identifying a resistance gene is also challenging if it has not previously been encountered. The research group solved this by developing new computational methods to find patterns in DNA that are associated with antibiotic resistance. By testing the genes they identified in the laboratory, they could then prove that their predictions were correct.

"The novel genes we discovered are only the tip of the iceberg. There are still many unidentified antibiotic resistance genes that could become major global health problems in the future," Kristiansson adds.

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