WSU Scientists develop software to identify drug-resistant bacteria
An easy to use software program to recognize drug-resistant genes in bacteria has been developed by researchers at Washington State University. The program could make it easier to identify the deadly antimicrobial-resistant bacteria that exist in the environment. The identification of deadly antimicrobial-resistant bacteria existing in the environment is now made easier by the program. More than 2.8 million cases of pneumonia, bloodstream, and other infections caused by these microbes are reported every year, causing 35,000 deaths in the U.S. The paper got published in the journal Scientific Reports.
The new software can analyze metagenomic data in greater depth than by simple sequence matching algorithms and identify clinically important novel antimicrobial resistance genes.
Bacteria or other microorganisms acquire antimicrobial resistance (AMR) by evolving and gaining genes that encode drug-resistance mechanisms. Bacteria causing strep or staph diseases or infections like pneumonia and tuberculosis have developed drug-resistant strains that make them very difficult to treat. In the future, the problems are expected to worsen in terms of deaths, infections, and health costs as bacteria evolve to outsmart most of the antibiotic treatments.
“We need to develop tools to easily and efficiently predict antimicrobial resistance that increasingly threatens health and livelihoods around the world,” said Chowdhury, lead author on the paper.
Researchers are looking for AMR genes in the environment as large-scale genetic sequencing has become easier. They are interested in studying where microbes live in water and soil and how they affect human health and spread. But they miss those unique genes of resistance from a protein sequence perspective, even though they were able to identify genes similar to AMR-resistant 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 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 to identify novel antimicrobial resistance genes that eventually could become clinically important.”
The WSU team considered resistance genes found in species of Clostridium, Enterococcus, Staphylococcus, Streptococcus, and Listeria. These bacteria are the cause of many major infections and infectious diseases including staph infections, food poisoning, pneumonia, and life-threatening colitis due to C. 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.
Resistant genes found in species of Enterococcus, Clostridium, Streptococcus, Staphylococcus, and Listeria were considered by the WSU team. These microbes cause many major infectious diseases like food poisoning, staph infections, life-threatening colitis, and pneumonia. Resistant genes were classified with up to 90% accuracy using the tool.
