Getting an EDGE on new way to engineer function into microbes

May 04, 2020

Imagine if you were editing an email and every time you tried to change a word, three other words would randomly change as well. That probably sounds like a frustrating and time-consuming way to compose anything useful. And it’s equally frustrating and time-consuming for biologists trying to study adaptation or edit the genes of organisms. Conventional approaches to mutation offer little opportunity to target individual genes without altering others as well.

“So many other things change around it at the same time, you can’t focus on how a single gene changes,” says Michael Travisano, McKnight Distinguished University professor in the Department of Ecology, Evolution, and Behavior and the BioTechnology Institute. “This was a major limitation for understanding evolutionary biology and leveraging it to make products.”

Now, Travisano, along with Romas Kazlauskas, professor in the Department of Biochemistry, Molecular Biology, and Biophysics, and postdoctoral fellow Xiao Yi, both also affiliated with the BioTechnology Institute, have developed a tool that makes it possible to mutate a single gene at a time — opening the door not only to a better understanding of evolution, but also better ways to modify the genes of microbes to give them the ability to mass-produce molecules, such as biofuel-generating enzymes, for human use.

Called Experimental Designed Genic Evolution, or EDGE, the approach, reported in April at PLOS ONE, makes it possible to mutate a single, specific gene in E. coli bacteria without altering others. “That means we can get a much better idea of how the adaptation process works in a much more complex system,” Travisano says.

EDGE is based on a trait that bacteria naturally have—the ability to fix their DNA after it’s disrupted by a stressor. Usually this happens across the genome, but the team figured out how to target it to a specific gene using what Travisano calls a “focusing agent.” And because it alters the gene but does not specify the location of the alteration within the gene as the gene editing system CRISPR does, it allows for faster and more efficient emergence of a desired trait.

“Improving [a gene through gene editing] is hard. You usually have to pick a winner. In our system, we don’t pick the winner. The winner wins. We can look at a lot more variation,” Travisano says.

The researchers tested the function and utility of EDGE by applying it to three tasks: evolving resistance to the antibiotic tetracycline, reversing tetracycline resistance in E. coli that had previously evolved it, and developing resistance to a new antibiotic. “We were able to go forward, backward and novel — all selection regimes we know about and are well known to be important, so we could understand why we got the results we got and could demonstrate the utility of the system,” Travisano says.

Travisano anticipates that other researchers will be interested in applying the results of this fusion of biotechnology and evolutionary biology to understanding how antibiotic resistance arises and how to overcome it. It also holds potential for commercial applications aimed at producing proteins with desirable traits to enhance the production of renewable fuels, improve health care and more.

In the process of developing EDGE, the researchers also were able to demonstrate why single-gene mutations don’t happen in nature. Essentially, they showed mathematically that, while too many mutations can just plain kill an organism, too few don’t give them the genetic diversity they need to adapt to unpredictable changes in their environment.

Travisano credits much of the effort’s success to its interdisciplinary nature, and in particular, to Yi’s passion for bridging basic and applied science. “[He’s] fascinated by applying evolutionary biology to real problems,” Travisano says. “Romas is more applied, and I’m more of a basic scientist. So the three of us make a good team.” – Mary Hoff

“Improving [a gene through gene editing] is hard. You usually have to pick a winner. In our system, we don’t pick the winner. The winner wins. We can look at a lot more variation.” Michael Travisano