New Information About Fitness Landscapes And Antibiotic Resistance Is Provided By This Study

E. coli Bacteria's Surprising Ability to Evolve Antibiotic Resistance: New Study | The Lifesciences Magazine

According to a recent study, E. coli bacteria may be more capable than previously thought of evolving antibiotic resistance. Seventy-five percent of evolutionary pathways led to significant antibiotic resistance, defying established beliefs about fitness landscapes in evolutionary biology. The researchers analysed potential mutations in a key E. Coli protein involved in antibiotic resistance. This finding might have wider ramifications for our comprehension of evolution and adaptation in many other contexts.

Fitness Landscapes And Antibiotic Resistance

Under the direction of SFI External Professor Andreas Wagner, the scientists conducted an experimental mapping of over 260,000 potential mutations in a protein that is crucial for the survival of E. coli in the presence of the antibiotic trimethoprim.

The researchers then discovered that 75% of all potential evolutionary paths of the E. coli protein ultimately gave the bacteria such a high level of antibiotic resistance that a clinician would no longer give the antibiotic trimethoprim to a patient. This was discovered over the course of thousands of extremely lifelike digital simulations.

Fitness landscapes show the various genotypes of an organism as points on the landscape, with the height of each point indicating how well-adapted the genotype is to its surroundings.

The objective is to locate the tallest peak, which represents the genotype that is most fit, in terms of evolutionary biology.

The prevailing hypothesis of fitness landscapes states that most evolving populations will stay caught at lower peaks and never achieve the pinnacle of evolutionary adaptation in extremely difficult settings, or those with several fitness peaks.

Unfortunately, until now, proving this theory has proven quite challenging since there are insufficient experimental data on big enough fitness landscapes.

Wagner and colleagues tackled this problem by developing one of the most combinatorially full fitness landscapes for the E. coli dihydrofolate reductase (DHFR) protein to date using CRISPR gene editing technology.

They discovered something unexpected. There were numerous peaks in the terrain, but the majority had poor fitness, which reduced their appeal for adaption.

Nevertheless, over 75% of the populations they simulated achieved high fitness peaks, granting E. coli significant resistance to antibiotics, even in this harsh environment.

There are important real-world ramifications. Such rough environments could indicate that many adaptation processes, including antibiotic resistance, are more accessible than previously believed in biological systems.

The findings may eventually force a reassessment of theoretical models across a range of disciplines and stimulate additional investigation into the ways in which evolutionary processes are influenced by natural environments.

“This has profound implications not only in biology but beyond, prompting us to reevaluate our understanding of landscape evolution across various fields,” Wagner explains. “We need to shift from abstract theoretical models to data-informed, realistic landscape models.”

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