Automating The Evolution Of Antimicrobial Resistance

To deepen their understanding of antimicrobial resistance, researchers have developed a robotic system that could evolve bacteria in bulk after exposure to different antibiotics.

AsianScientist (Dec. 17, 2020) – By evolving Escherichia coli bacteria in the laboratory with the help of robots, researchers from Japan have revealed some of the mechanisms underlying drug resistance. Their findings were published in Nature Communications.

Though all eyes may be on COVID-19 for now, antimicrobial resistance (AMR) remains one of the world’s most pressing public health threats. With AMR’s emergence, even common infections can become impossible to treat. Given the urgency of the situation, scientists are racing to understand how exactly resistance evolves in the first place.

However, the evolution of resistance is a tricky process—involving countless changes in genome sequences and cellular states. Consequently, there has been no comprehensive report of resistance dynamics for many antibiotics to date.

“Laboratory evolution combined with genomic analyses is a promising approach for understanding antibiotic resistance dynamics,” explained lead researcher Dr. Tomoya Maeda from the RIKEN Center for Biosystems Dynamics Research. “However, laboratory evolution is highly labor-intensive, requiring the serial transfer of cultures over a long period and a large number of parallel experiments.”

To overcome these limitations, Maeda and his colleagues developed an automated robotic culture system that could evolve over 250 generations of E. coli exposed to 95 different antibiotics.

To conveniently study the dynamics of antimicrobial resistance in bacteria, researchers from RIKEN used the robotics culture system shown above. Credit: RIKEN

From here, the team monitored changes in the bacteria’s gene expression following antibiotic exposure, eventually producing resistance profiles for 192 of the evolved strains. They also tested 2,162 pairs of drug combinations using the robotic system, discovering 157 pairs with the potential to suppress the evolution of antibiotic resistance in E. coli.

Given the large amounts of data involved, the researchers developed a machine learning method to analyze the dataset and identify genes that shape the evolution of AMR. Quantifying and understanding these genes could help scientists and clinicians predict, and potentially even control resistance evolution later on.

“We believe that our results can be applied to the development of alternative strategies for suppressing the emergence of drug-resistant bacteria,” concluded Maeda.

The article can be found at: Maeda et al. (2020) High-throughput Laboratory Evolution Reveals Evolutionary Constraints in Escherichia coli.


Source: RIKEN; Photo: NIH Image Gallery.
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