Study Reveals New Perspectives on Antibiotic Resistance
A recent Science publication suggests E. coli bacteria have greater potential for evolving antibiotic resistance than previously assumed. Led by SFI External Professor Andreas Wagner, researchers mapped over 260,000 potential mutations of an essential E. coli protein, crucial for bacterial survival against trimethoprim antibiotic exposure.
During numerous digital simulations, the researchers discovered that approximately 75% of potential evolutionary paths for the E. coli protein led to such significant antibiotic resistance that medical professionals would avoid prescribing trimethoprim to patients.
“This study suggests that bacteria like E. coli might be more proficient at developing antibiotic resistance than previously believed. This has wider implications for understanding evolutionary biology, chemistry, and other evolving systems,” explains Wagner, an evolutionary biologist at the University of Zurich in Switzerland.
Study reveals antibiotic resistance insights: antibiotic resistance
Aside from revealing concerning insights into antibiotic resistance, the study challenges a long-standing theory related to fitness landscapes.
Fitness landscapes depict how well an organism or a component, like a protein, adapts to its surroundings. In this context, different points represent various genotypes, and their height indicates how well each is adapted.
The conventional belief was that in complex landscapes with multiple fitness peaks, evolving populations would get stuck at lower peaks and fail to reach the highest level of adaptation. However, this theory was challenging to test due to a lack of experimental data on extensive fitness landscapes.
The landscape
To overcome this hurdle, Wagner and his team used CRISPR gene editing to construct one of the most comprehensive fitness landscapes for the E. coli dihydrofolate reductase (DHFR) protein.
Surprisingly, while the landscape had numerous peaks, most were of low fitness, making them less appealing for adaptation. Nevertheless, around 75% of the simulated populations reached high fitness peaks, resulting in heightened antibiotic resistance for E. coli.
These findings could hold significant implications: if such complex landscapes are common in biological systems, many adaptive processes, including antibiotic resistance, may be more achievable than previously assumed.
The outcome might prompt a reassessment of theoretical models across multiple domains and encourage deeper investigations into how actual landscapes influence evolutionary mechanisms.
“This goes beyond biology, urging us to reconsider our comprehension of landscape evolution across diverse domains,” Wagner highlights. “It’s time to move away from theoretical models and embrace data-driven, practical landscape models.”
Read the original article on sciencedaily.
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