Understanding the step-by-step development of E. coli resistance to drugs

Antibiotic resistance, which occurs when infection-causing bacteria evolve to the point where they are no longer affected by conventional antibiotics, is a worldwide concern. A new study from the University of Tokyo has documented the evolution and natural selection process of Escherichia coli (E. coli) bacteria in the laboratory.

These maps, known as fitness landscapes, assist us in better understanding the progression and characteristics of E. coli resistance to eight different drugs, including antibiotics. Researchers anticipate that their findings and approaches will be beneficial in the future for predicting and managing E. coli and other bacteria.

Ever felt sick after eating a burger that was not even cooked through? Or when leftovers from yesterday’s meal were left out in the fridge for an extended period of time? Food poisoning can take various forms, but one common cause is the growth of bacteria like E. coli.

Most E. coli infections, while unpleasant, can be treated at home with rest and rehydration. However, in some cases, it can result in potentially fatal infections. Antibiotics can be a powerful and effective treatment for bacterial infection. However, antibiotic resistance, or the potential of bacteria to grow resistant to drugs, is a critical global concern.

If antibiotics become ineffective, individuals will once again be at risk of severe illness as a result of minor injuries and common ailments.

The development of methods that could predict and control bacterial evolution is crucial to find and suppress the emergence of resistant bacteria. Thus, we have developed a novel method to predict drug resistance evolution by using data obtained from laboratory evolution experiments of E. coli.”

Junichiro Iwasawa, Researcher and Doctoral Student, Graduate School of Science

The researchers utilized a technique known as adaptive laboratory evolution, or ALE, to “replay the tape” of drug-resistant E. coli’s adaptation to eight different medications, including antibiotics.

The method allowed the researchers to explore in the laboratory the evolution of bacterial strains with specified observable traits (referred phenotypes). This gave them insight into what alterations the bacteria would undergo over 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. 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.”

Junichiro Iwasawa, Researcher and Doctoral Student, Graduate School of Science

The fitness landscapes resemble 3D topographic maps. The mountains and valleys on the map indicate the evolution of an organism. Organisms at the summits have developed to be more “fit,” or capable of surviving in their environment.

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.”

Junichiro Iwasawa, Researcher and Doctoral Student, Graduate School of Science

The researchers anticipate that the fitness landscapes they have mapped in this study, as well as the methods they have developed, will be beneficial for anticipating and managing not only E. coli, but also other forms of microbial development.

The researchers expect that this will pave the way for future studies into strategies to reduce drug-resistant bacteria and contribute to the production of valuable microbes for bioengineering and agriculture.

Iwasawa concludes, “the next important step is to actually try using the fitness landscapes to control drug resistance evolution and see how far we can control it. This can be done by designing laboratory evolution experiments based on the information from the landscapes. We can’t wait to see the upcoming results.”