Study Hits Replay on E. coli Evolution

Researchers from the University of Tokyo have mapped the evolution and process of natural selection of Escherichia coli (E. coli) bacteria in the lab and say the “fitness landscapes” will help better understand the development of antibiotic resistance. The creation of such resources could also be useful for predicting and controlling E. coli outbreaks.

E. coli is a common cause of food poisoning. Most cases can be managed with rest and rehydration, and more serious cases can be treated with antibiotics.

However, the ability of bacteria to become strong enough that it does not respond to the medication is a growing global concern. Without effective antibiotics, humans could once again be at risk of serious illness from small injuries and common ailments like E coli. 

“The development of methods that could predict and control bacterial evolution is crucial to find and suppress the emergence of resistant bacteria,” says researcher Junichiro Iwasawa, a doctoral student in the Graduate School of Science at the time of the study. “Thus, we have developed a novel method to predict drug resistance evolution by using data obtained from laboratory evolution experiments of E. coli.”

The researchers used a method called adaptive laboratory evolution (ALE) to “replay the tape” on the evolution of drug-resistant E. coli to eight different drugs, including antibiotics. The method enabled the researchers to study the evolution of bacterial strains with specific phenotypes in the lab, helping gain insight into what changes might occur to the bacteria during the longer-term process of natural selection.

“While conventional laboratory evolution experiments have been labor intensive, we mitigated this problem by using an automated culture system that was previously developed in our lab. This allowed us to acquire sufficient data on the phenotypic changes related to drug resistance evolution,” says Iwasawa. “By analyzing the acquired data, using principal component analysis (a machine-learning method), we have been able to elucidate the fitness landscape which underlies the drug resistance evolution of E. coli.”

Fitness landscapes look like 3D topographic maps. The mountains and valleys on the map represent an organism’s evolution. Organisms at the peaks have evolved to have better “fitness,” or ability to survive in their environment. Iwasawa explained, “The coordinates of the fitness landscape represent inner states of the organism, such as gene mutation patterns (genotypes) or drug resistance profiles (phenotypes), etc. Thus, the fitness landscape describes the relation between the inner states of the organism and its corresponding fitness levels. By elucidating the fitness landscape, the progression of evolution is expected to be predictable.”

Iwasawa and colleagues hope their fitness landscapes and the methods developed in the process will be useful for predicting and controlling not only E. coli, but also other forms of microbial evolution. They also hope their findings will lead to future studies that can find ways to suppress drug-resistant bacteria and contribute to the development of useful microbes for bioengineering and agriculture.

“[T]he next important step is to actually try using the fitness landscapes to control drug resistance evolution and see how far we can control it,” Iwasawa says. “This can be done by designing laboratory evolution experiments based on the information from the landscapes.”

The findings were published recently in PLoS Biology.