Researchers at Washington State University have developed a new software that enables them to find drug-resistant bacteria – specifically the genes within bacteria that enables them to become drug-resistant.
According to the team behind this software, their easy to use software will enable identification of deadly antimicrobial resistant bacteria that exist in the environment. Data indicates that as many as 2.8 million difficult-to-treat pneumonia and other infections are caused by these drug-resistant bacteria and cause around 35,000 deaths in the U.S.
Findings are published in the journal Scientific Reports.
Antimicrobial resistance (AMR) occurs when bacteria or other microorganisms evolve or acquire genes that encode drug-resistance mechanisms. Bacteria that cause staph or strep infections or diseases such as tuberculosis 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 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 known AMR-resistant genes, they are probably missing genes for resistance that look very unique from a protein sequence perspective.
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, which in turn helps identify 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 and composition properties of protein sequences rather than simply sequence similarity.
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.