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April 27, 2008

Evolution on a chip

Filed under: Shkence, teknologji --- Science — halfevil @ 10:27 am

Researchers have created a computer-controlled system that harnesses ‘survival of the fittest’ to generate more efficient enzymes. The method has so far been used to improve an enzyme made of RNA, but could also be employed to study evolution in proteins, viruses and bacteria.

Scientists have previously demonstrated evolution in a test tube, and have used the technique to create molecules with novel or improved activity. A drug called Macugen, for example, which slows some types of vision loss, consists of an RNA molecule created in part by test-tube evolution. Industry scientists have also used the method to boost the activity of a herbicide-neutralizing enzyme and then used that enzyme to create herbicide-resistant plants.

The new method does the same, but under automated computer control. This means the experimental protocol can be followed more rigidly, without the sloppiness caused by human error. And the experiment can run and run tirelessly, giving the molecules more time to evolve. “What’s potentially really cool about it is the prospect of making the process automated and more rigorous,” says Robert Keenan, a biochemist at the University of Chicago in Illinois, who was not affiliated with the study. “It’s limitless what you could do.”

Fertile molecules

Biochemists Brian Paegel and Gerald Joyce of the Scripps Research Institute in La Jolla, California, demonstrated their system by improving the capability of an RNA enzyme to stitch itself to another RNA fragment.

Paegel and Joyce created a population of these RNA enzymes containing mutations at different sites. They then added protein enzymes that would copy any RNA fragments that had successfully sewn together. Joyce refers to such RNA fragments as ‘fertile’ because they are capable of being reproduced.

The researchers loaded their solution onto a chip with tiny chambers that hold minute amounts of liquid, which contained RNA fragments for the enzyme to stitch onto. A computer diluted the sample automatically when it reached a set concentration, supplying fresh liquid containing fewer and fewer RNA fragments. Over time, this meant that only those enzymes that were particularly efficient could continue to generate ‘fertile’ molecules in the RNA-poor environment.

After 500 rounds of growth and dilution, the solutions contained an enzyme that had accumulated 11 mutations and performed 90-times better than the starting molecule2. The end result was unpredictable, says Joyce. Some of the mutations diminished enzyme performance on their own, but became advantageous when combined with other mutations.

Why stop at 500 iterations? Joyce gives two reasons. First, the researchers realized they were approaching a theoretical limit on enzyme performance. But the experiment was also nearing its 500th iteration the night that Trevor Hoffman, a baseball player for the San Diego Padres, earned his 500th ‘saved’ win. A local newspaper printed a picture of Padres celebrating on the field with a large screen behind him reading “Trevor Hoffman 500”. Joyce, a Padres fan, altered the image to read “On-Chip Dilutions 500”, posted it in his lab, and decided to stop the experiment there.

Precision control

Overall, the experiment is similar to one performed in Joyce’s lab more than a decade ago3, but automated control of the system represents a significant improvement, says Joyce. Manually diluting the solutions was a source of aggravation and error, he notes. “You’d grow and dilute, grow and dilute, but you didn’t know for sure what was going on in the tube,” he says. “You’d wonder: ‘Should I transfer it now? It’s about 10pm. I think I’ll just stick it in the freezer and pick this up again in the morning.’”

The chips will be useful for studying RNA evolution, agrees Jack Szostak of Harvard Medical School in Boston, Massachusetts. “This is great work,” he says. “It shows in a very clear way that a population of replicating sequences will inevitably evolve as more fit sequences arise.”

Although the system works well for RNA, Keenan notes that it will not address one of the most difficult steps in creating commercial enzymes: getting the enzyme to work in a living organism. Conditions in a test tube or on a chip are unlikely to replicate the environment of a human or a plant, he notes. “Sometimes you go to put your winning molecule in the plant and it just doesn’t work,” says Keenan.

But the method could aid the search for enzymes that do not exist in nature. In that case, says Keenan, generating an enzyme with any activity at all is a huge challenge. “If you can find any activity, then you’ve got your foot in the door,” he says.


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