Researchers Develop Software to Find Drug-Resistant 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 microbes annually cause more than 2.8 million difficult-to-treat pneumonia, bloodstream and other infections and 35,000 deaths in the U.S. The researchers, including PhD 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 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.

"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.

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.

"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 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 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.

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

Abu Sayed Chowdhury, Douglas R Call, Shira L Broschat.
PARGT: a software tool for predicting antimicrobial resistance in bacteria.
Scientific Reports volume 10, Article number: 11033, 2020. doi: 10.1038/s41598-020-67949-9

Most Popular Now

New AI Tool Predicts Protein-Protein Int…

Scientists from Cleveland Clinic and Cornell University have designed a publicly-available software and web database to break down barriers to identifying key protein-protein interactions to treat with medication. The computational tool...

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...

New Research Shows Promise and Limitatio…

Published in JAMA Network Open, a collaborative team of researchers from the University of Minnesota Medical School, Stanford University, Beth Israel Deaconess Medical Center and the University of Virginia studied...

G-Cloud 14 Makes it Easier for NHS to Bu…

NHS organisations will be able to save valuable time and resource in the procurement of technologies that can make a significant difference to patient experience, in the latest iteration of...

Start-Ups will Once Again Have a Starrin…

11 - 14 November 2024, Düsseldorf, Germany. The finalists in the 16th Healthcare Innovation World Cup and the 13th MEDICA START-UP COMPETITION have advanced from around 550 candidates based in 62...

Hampshire Emergency Departments Digitise…

Emergency departments in three hospitals across Hampshire Hospitals NHS Foundation Trust have deployed Alcidion's Miya Emergency, digitising paper processes, saving clinical teams time, automating tasks, and providing trust-wide visibility of...

MEDICA HEALTH IT FORUM: Success in Maste…

11 - 14 November 2024, Düsseldorf, Germany. How can innovations help to master the great challenges and demands with which healthcare is confronted across international borders? This central question will be...

A "Chemical ChatGPT" for New M…

Researchers from the University of Bonn have trained an AI process to predict potential active ingredients with special properties. Therefore, they derived a chemical language model - a kind of...

Siemens Healthineers co-leads EU Project…

Siemens Healthineers is joining forces with more than 20 industry and public partners, including seven leading stroke hospitals, to improve stroke management for patients all over Europe. With a total...

MEDICA and COMPAMED 2024: Shining a Ligh…

11 - 14 November 2024, Düsseldorf, Germany. Christian Grosser, Director Health & Medical Technologies, is looking forward to events getting under way: "From next Monday to Thursday, we will once again...

In 10 Seconds, an AI Model Detects Cance…

Researchers have developed an AI powered model that - in 10 seconds - can determine during surgery if any part of a cancerous brain tumor that could be removed remains...

Does AI Improve Doctors' Diagnoses?

With hospitals already deploying artificial intelligence to improve patient care, a new study has found that using Chat GPT Plus does not significantly improve the accuracy of doctors' diagnoses when...