Easy-to-use software developed to identify drug-resistant genes in bacteria

Washington State University researchers have developed an easy-to-use software program to identify drug-resistant genes in bacteria.

The program could make it easier to identify the deadly antimicrobial resistant bacteria that exist in the environment. Such superbugs annually cause more than 2.8 million difficult-to-treat pneumonia or bloodstream infections and 35,000 deaths in the U.S.

The researchers, including Ph.D. computer science graduate Abu Sayed Chowdhury, Shira Broschat in the School of Electrical Engineering and Computer Science, and Douglas Call in the Paul G. Allen School for Global Animal Health, report on their work in the journal Scientific Reports.

Antimicrobial resistance (AMR) occurs when bacteria or other microorganisms evolve their genetic make-up to overcome the drugs that are used to treat infections. Bacteria that cause staph or strep infections or diseases such as tuberculosis, malaria, and pneumonia have developed drug-resistant strains that make them increasingly difficult and sometimes impossible to treat. The problem is expected to worsen in future decades in terms of increased infections, deaths, and health costs as bacteria evolve to outsmart a limited number of antibiotic treatments.

As anti-microbial resistance becomes a threat worldwide, both economically and to public health, there is an urgent need to develop a tool for efficient prediction."

Abu Sayed Chowdhury, lead author on the paper

As large-scale genetic sequencing has become easier, researchers are looking for AMR genes in the environment. Researchers are interested in where microbes are living in soil and water and how they might spread and affect human health. While they are able to identify genes that are similar to AMR-resistant genes, they have been missing genes for resistance that aren't similar.

The WSU research team developed a machine-learning algorithm that uses features of AMR proteins rather than the similarity of gene sequences to identify AMR genes. The researchers used game theory, a tool that is used in several fields, especially economics, to model strategic interactions between game players, to help identify the AMR genes.

Using their machine learning algorithm and game theory approach, the researchers looked at the interactions of several features of the genetic material, including its structure and the physiochemical, evolutionary, and composition properties of protein sequences rather than simply sequence similarity.

"Our software can be employed to analyze metagenomic data in greater depth than would be achieved by simple sequence matching algorithms," Chowdhury said. "This can be an important tool for better understanding the emergence of new antimicrobial resistance genes that eventually become clinically important."

"The virtue of this program is that we can actually detect AMR in newly sequenced genomes," Broschat said. "It's a way of identifying AMR genes and their prevalence that might not otherwise have been found. That's really important."

The WSU team looked for resistance to two common antibiotics, bacitracin and vancomycin, that are used to treat a variety of infections, ranging from staph infections to Clostridium difficile. They were able to accurately classify resistant genes with up to 90 percent accuracy.

They have developed a software package that can be easily downloaded and used by other researchers to look for AMR in large pools of genetic material. The software can also be improved over time. While it's trained on currently available data, researchers will be able to re-train the algorithm as more data and sequences become available.

"You can bootstrap and improve the software as more positive data becomes available," Broschat said.