Can Scientists More Accurately Predict Evolution? Gregory Lang develops a mechanistic understanding of genome evolution through experiments with yeast. Story: Kelly Hochbein Illustrations: Matthew Richardson When modern-day flms tackle the concept of time travel, the scenario often plays out like this: Our hero goes back in time, does something slightly diferent than the frst time around and then returns to a future changed by that action. Gregory Lang compares this popular element of science fction to a thought experiment of renowned evolutionary biologist Stephen Jay Gould: “If you can rewind the tape of life and let evolution play out for a second time, would it produce anything like what we see today, or would you get these chance events that cause you to veer of in diferent directions?” We have no good scientifc answer to time travel, says Lang, because “you can’t do that experiment [in nature]. You can't rewind the tape of life and let it play the second time. But we can do that in the lab, essentially, with experimental evolution.” Experimental evolution, a laboratory-controlled technique that allows for the simultaneous evolution of initially identical populations of an organism for thousands of generations, allows researchers to study evolution in real time. Lang, an associate professor of biological sciences, applies this technique to experiments with yeast. Developing an understanding of how complex traits evolve in yeast can better equip researchers to understand the molecular basis of evolution, the adaptive evolution of disease-causing pathogens and cancers, and the development of genetic traits in humans. “We use yeast to study evolution, but at the same time, we’re using evolution as a stick to just poke this organism and select for diferent things,” he says. “We’re using evolution to learn more about how yeast is wired to do what it does.” Yeast & Evolution Saccharomyces cerevisiae is the same type of yeast used to produce bread and beer. Lang uses this simple microorganism to understand how phenotypic selection, which occurs when organisms with particular traits produce more ofspring than those without those traits, drives genotypic change, or changes in an organism’s genetic makeup. Yeast have a small genome and replicate quickly, and labs can grow large population sizes in small volumes. The Lang Labmixes “themodern with the classic,” applying cutting-edge techniques, some of which Lang and his team have developed, as well as more traditional approaches. They grow yeast in 96-well plates and use a robot to semi-automate the process. Next, they set up the same evolution experiment with hundreds of yeast replicates and watch each unfold in a short period of time. “We can actually watch evolution in the lab and ask questions,” Lang says. Lang and his team freeze the yeast populations following these evolution experiments, creating a “frozen fossil record” of a range of populations at any point in time. This allows them to return to any population, resurrect it and sequence its genome to determine why its evolution made a detour at a particular time point, Lang explains. The team extracts DNA from the evolved yeast samples, makes sequencing libraries of the genomes and sends them to Princeton University, where Lang worked as a postdoctoral researcher before his arrival at Lehigh in 2013. Princeton returns massive amounts of data, which the team then analyzes on Lehigh’s servers to determine how each “ancestor” strain changed both genotypically and phenotypically. If in their examination of the data, the researchers observe a mutation represented more than what might be expected by chance, they will put the mutation from the evolved strain into the previously frozen ancestor strain. They then verify whether the mutation increases the strain’s growth rate or ftness, which is the quantitative representation of natural and sexual selection within evolutionary biology. This approach allows the team to address questions about adaptive evolution, including its predictability. In a recent paper published in the journal eLife, Lang, former postdoctoral researcher Sean W. Buskirk, now an assistant professor of biology at West Chester University, and Alecia B. Rokes ’19, currently a doctoral student in microbiology at the University of Pittsburgh, challenge the misconception that evolution is a linear process, presenting evidence of its nontransitive nature. Unlike a transitive evolutionary sequence, in which each successive generation is more fit than its predecessor, in a nontransitive sequence, a less-ft generationmight follow a ftter previous generation. The team, Lang explains, was doing fitness assays, a process through which they compete evolved yeast populations head to head against ancestral strains to determine which is more ft. Evolved strains tend to always beat the ancestor FA L L 2 0 2 2 | 3 7
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