Researchers develop software to find drug-resistant bacteria

Date:
July 6, 2020
Source:
Washington State University
Summary:
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 US.



FULL STORY ========================================================================== 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.

The work was funded in part by the Carl M. Hansen Foundation.


========================================================================== Story Source: Materials provided by Washington_State_University. Original written by Tina Hilding. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Abu Sayed Chowdhury, Douglas R. Call, Shira L. Broschat. PARGT: a
software tool for predicting antimicrobial resistance in bacteria.

Scientific Reports, 2020; 10 (1) DOI: 10.1038/s41598-020-67949-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200706140843.htm

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