Antibiotic resistance, when infection-causing bacteria evolve in order that they aren’t any longer affected by typical antibiotics, is a world concern. Recent research on the University of Tokyo has mapped the evolution and technique of natural choice of Escherichia coli (E. coli) bacteria within the lab. These maps, called fitness landscapes, help us higher understand the step-by-step development and characteristics of E. coli resistance to eight different drugs, including antibiotics. Researchers hope their results and methods will likely be useful for predicting and controlling E. coli and other bacteria in the long run.

Have you ever ever felt queasy after eating an undercooked burger? Or when leftovers from yesterday’s dinner were neglected of the fridge a bit too long? There are various different sorts of food poisoning, but one common cause is the expansion of bacteria resembling E. coli. Most cases of E. coli, though unpleasant, might be managed at home with rest and rehydration. Nonetheless, in some instances, it might probably result in life-threatening infections. If you could have a bacterial infection, antibiotic medication generally is a powerful and effective treatment. But antibiotic resistance, the flexibility of bacteria to turn into strong enough that it doesn’t reply to the medication, is a serious global concern. If antibiotics aren’t any longer effective, then we’ll once more be vulnerable to serious illness from small injuries and customary ailments.

The event of methods that would predict and control bacterial evolution is crucial to search out and suppress the emergence of resistant bacteria. Thus, we now have developed a novel method to predict drug resistance evolution through the use of data obtained from laboratory evolution experiments of E. coli.”

Junichiro Iwasawa, researcher, doctoral student within the Graduate School of Science on the time of the study

The researchers used a way called adaptive laboratory evolution, or ALE, to “replay the tape” on the evolution of drug-resistant E. coli to eight different drugs, including antibiotics. The strategy enabled the researchers to review the evolution of bacterial strains with specific observable characteristics (called phenotypes) within the lab. This helped them gain insight into what changes might occur to the bacteria in the course of the longer-term technique of natural selection.

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

Fitness landscapes seem like 3D topographic maps. The mountains and valleys on the map represent an organism’s evolution. Organisms on the peaks have evolved to have higher “fitness,” or ability to survive of their environment. Iwasawa explained, “The coordinates of the fitness landscape represent inner states of the organism, resembling 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 anticipated to be predictable.”

The team believes the fitness landscapes it has mapped on this study and the methods developed in the method will likely be useful for predicting and controlling not only E. coli, but additionally other types of microbial evolution. The researchers hope it will result in future studies that may find ways to suppress drug-resistant bacteria and contribute to the event of useful microbes for bioengineering and agriculture. Iwasawa concluded that “the subsequent vital step is to really try using the fitness landscapes to manage drug resistance evolution and see how far we will control it. This might be done by designing laboratory evolution experiments based on the knowledge from the landscapes. We won’t wait to see the upcoming results.”

Source:

Journal reference:

Iwasawa, J., et al. (2022) Evaluation of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape. PLOS Biology. doi.org/10.1371/journal.pbio.3001920.