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https://www.quantamagazine.org/yeast-study-suggests-genetics-are-random-but-evolution-is-not-20140911/ September 11, 2014

Evolution’s Random Paths Lead to One PlaceA massive statistical study suggests that the final evolutionary outcome — fitness — is predictable.

By Emily Singer

Different strains of yeast grown under identical conditions develop different mutations butultimately arrive at similar evolutionary endpoints.

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https://www.quantamagazine.org/yeast-study-suggests-genetics-are-random-but-evolution-is-not-20140911/ September 11, 2014

In his fourth-floor lab at Harvard University, Michael Desai has created hundreds of identical worldsin order to watch evolution at work. Each of his meticulously controlled environments is home to aseparate strain of baker’s yeast. Every 12 hours, Desai’s robot assistants pluck out the fastest-growing yeast in each world — selecting the fittest to live on — and discard the rest. Desai thenmonitors the strains as they evolve over the course of 500 generations. His experiment, which otherscientists say is unprecedented in scale, seeks to gain insight into a question that has long bedeviledbiologists: If we could start the world over again, would life evolve the same way?

Michael Desai, a biologist at HarvardUniversity, uses statistical methods to study basic questions in evolution.

Many biologists argue that it would not, that chance mutations early in the evolutionary journey of aspecies will profoundly influence its fate. “If you replay the tape of life, you might have one initialmutation that takes you in a totally different direction,” Desai said, paraphrasing an idea first putforth by the biologist Stephen Jay Gould in the 1980s.

Desai’s yeast cells call this belief into question. According to results published in Science in June, allof Desai’s yeast varieties arrived at roughly the same evolutionary endpoint (as measured by theirability to grow under specific lab conditions) regardless of which precise genetic path each straintook. It’s as if 100 New York City taxis agreed to take separate highways in a race to the PacificOcean, and 50 hours later they all converged at the Santa Monica pier.

The findings also suggest a disconnect between evolution at the genetic level and at the level of thewhole organism. Genetic mutations occur mostly at random, yet the sum of these aimless changessomehow creates a predictable pattern. The distinction could prove valuable, as much geneticsresearch has focused on the impact of mutations in individual genes. For example, researchers often

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https://www.quantamagazine.org/yeast-study-suggests-genetics-are-random-but-evolution-is-not-20140911/ September 11, 2014

ask how a single mutation might affect a microbe’s tolerance for toxins, or a human’s risk for adisease. But if Desai’s findings hold true in other organisms, they could suggest that it’s equallyimportant to examine how large numbers of individual genetic changes work in concert over time.

“There’s a kind of tension in evolutionary biology between thinking about individual genes and thepotential for evolution to change the whole organism,” said Michael Travisano, a biologist at theUniversity of Minnesota. “All of biology has been focused on the importance of individual genes forthe last 30 years, but the big take-home message of this study is that’s not necessarily important.”

To efficiently analyze many strains of yeast simultaneously, scientists grow them on plates like thisone, which has 96 individual wells.

The key strength in Desai’s experiment is its unprecedented size, which has been described byothers in the field as “audacious.” The experiment’s design is rooted in its creator’s background;Desai trained as a physicist, and from the time he launched his lab four years ago, he applied astatistical perspective to biology. He devised ways to use robots to precisely manipulate hundreds oflines of yeast so that he could run large-scale evolutionary experiments in a quantitative way.Scientists have long studied the genetic evolution of microbes, but until recently, it was possible toexamine only a few strains at a time. Desai’s team, in contrast, analyzed 640 lines of yeast that hadall evolved from a single parent cell. The approach allowed the team to statistically analyzeevolution.

“This is the physicist’s approach to evolution, stripping down everything to the simplest possibleconditions,” said Joshua Plotkin, an evolutionary biologist at the University of Pennsylvania who wasnot involved in the research but has worked with one of the authors. “They could partition how muchof evolution is attributable to chance, how much to the starting point, and how much tomeasurement noise.”

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Fluid-handling robotslike this one make it possible to study hundreds of lines of yeast over many generations.

Desai’s plan was to track the yeast strains as they grew under identical conditions and then comparetheir final fitness levels, which were determined by how quickly they grew in comparison to theiroriginal ancestral strain. The team employed specially designed robot arms to transfer yeast coloniesto a new home every 12 hours. The colonies that had grown the most in that period advanced to thenext round, and the process repeated for 500 generations. Sergey Kryazhimskiy, a postdoctoralresearcher in Desai’s lab, sometimes spent the night in the lab, analyzing the fitness of each of the640 strains at three different points in time. The researchers could then compare how much fitnessvaried among strains, and find out whether a strain’s initial capabilities affected its final standing.They also sequenced the genomes of 104 of the strains to figure out whether early mutationschanged the ultimate performance.

Previous studies have indicated that small changes early in the evolutionary journey can lead to bigdifferences later on, an idea known as historical contingency. Long-term evolution studies in E. colibacteria, for example, found that the microbes can sometimes evolve to eat a new type of food, butthat such substantial changes only happen when certain enabling mutations happen first. Theseearly mutations don’t have a big effect on their own, but they lay the necessary groundwork for latermutations that do.

Diminishing Returns

Desai’s study isn’t the first to suggest that the law of diminishing returns applies to evolution. A famousdecades-long experiment from Richard Lenski’s lab at Michigan State University, which has tracked E. coli forthousands of generations, found that fitness converged over time. But because of limitations in genomicstechnology in the 1990s, that study didn’t identify the mutations underlying those changes. “The 36populations we had then would have been much more expensive to sequence than the hundred they didhere,” said Michael Travisano of the University of Minnesota, who worked on the Michigan State study.

More recently, two papers published in Science in 2011 mixed and matched a handful of beneficial mutationsin different types of bacteria. When the researchers engineered those mutations into different strains ofbacteria, they found that the fitter strains enjoyed a smaller benefit. Desai’s study examined a much broadercombination of possible mutations, showing that the rule is much more general.

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But because of the small scale of such studies, it wasn’t clear to Desai whether these cases were theexception or the rule. “Do you typically get big differences in evolutionary potential that arise in thenatural course of evolution, or for the most part is evolution predictable?” he said. “To answer thiswe needed the large scale of our experiment.”

As in previous studies, Desai found that early mutations influence future evolution, shaping the paththe yeast takes. But in Desai’s experiment, that path didn’t affect the final destination. “Thisparticular kind of contingency actually makes fitness evolution more predictable, not less,” Desaisaid.

Desai found that just as a single trip to the gym benefits a couch potato more than an athlete,microbes that started off growing slowly gained a lot more from beneficial mutations than their fittercounterparts that shot out of the gate. “If you lag behind at the beginning because of bad luck, you’lltend to do better in the future,” Desai said. He compares this phenomenon to the economic principleof diminishing returns — after a certain point, each added unit of effort helps less and less.

Scientists don’t know why all genetic roads in yeast seem to arrive at the same endpoint, a questionthat Desai and others in the field find particularly intriguing. The yeast developed mutations in manydifferent genes, and scientists found no obvious link among them, so it’s unclear how these genesinteract in the cell, if they do at all. “Perhaps there is another layer of metabolism that no one has ahandle on,” said Vaughn Cooper, a biologist at the University of New Hampshire who was notinvolved in the study.

It’s also not yet clear whether Desai’s carefully controlled results are applicable to more complexorganisms or to the chaotic real world, where both the organism and its environment are constantlychanging. “In the real world, organisms get good at different things, partitioning the environment,”Travisano said. He predicts that populations within those ecological niches would still be subject todiminishing returns, particularly as they undergo adaptation. But it remains an open question, hesaid.

Nevertheless, there are hints that complex organisms can also quickly evolve to become more alike.A study published in May analyzed groups of genetically distinct fruit flies as they adapted to a newenvironment. Despite traveling along different evolutionary trajectories, the groups developedsimilarities in attributes such as fecundity and body size after just 22 generations. “I think manypeople think about one gene for one trait, a deterministic way of evolution solving problems,” saidDavid Reznick, a biologist at the University of California, Riverside. “This says that’s not true; youcan evolve to be better suited to the environment in many ways.”

This article was reprinted on Wired.com.