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Researchers Describe DNA Barcoding-based Tool to Study Yeast Population Dynamics

NEW YORK (GenomeWeb) – With a new DNA barcoding-based approach, researchers were able to follow up to 25,000 yeast lineages within a larger population over time to trace beneficial mutations as they cropped up.

Researchers led by Stanford University's Gavin Sherlock tacked DNA barcodes onto yeast genomes and let the yeast grow, as they reported in Nature today. Every eight generations, the investigators performed next-generation sequencing on the yeast population to see which lineages and mutations were on the rise and which were in decline.

"At first, we saw large numbers of mutations that each provided a relatively modest fitness benefit to individual families, but that together increased the overall fitness of the population as a whole," said Sherlock, an associate professor of genetics at Stanford, in a statement. "Later, we began to see more rare mutations with a larger fitness benefit; families with these mutations tend to then drive to extinction others with less advantageous mutations."

To follow the lineages, Sherlock and his colleagues inserted a so-called landing pad in a neutral region of the yeast genome where they could then integrate randomly generated 20-nucleotide barcodes.

They split the barcoded clonal yeast population into two replicates and let them grow for about 168 generations in a serial batch culture, taking a sample every eight generations or so for analysis.

Plotting the relative frequencies of the barcodes — and thus the lineages — over time, the researchers found that most lineages eventually declined in number, though a few acquired a beneficial mutation that helped boost its numbers and, eventually, the overall fitness of the population.

Sherlock and his colleagues calculated that about 25,000 beneficial mutations with a fitness effect of more than 2 percent were established by generation 112.

By this generation, they also noted that the mean fitness of the population was more than 5 percent higher than the fitness of the ancestral population, though some lineages had a fitness advantage of more than 10 percent.

Sherlock and his colleagues further estimated that such beneficial mutations cropped up in about one cell out of every 100,000 cells each generation.

Based on their data, the researchers were also able to determine the time it took for those beneficial mutations to become established in the population, though they noted that was dependent upon the mutation's influence on fitness. Still, beneficial mutations took at most 48 generations to take hold. Beyond that, they said a beneficial mutation would be unlikely to take hold and expand beyond background mutation levels.

Both yeast population replicates followed the same general population dynamics model, the researchers said. Early on, beneficial mutations of small effect drove the fitness of the population in both replicates, but then the mean fitness around the 112th generation began to be driven by about a hundred mutations of slightly larger beneficial effect on fitness. Then, by the 132nd generation, cells belonging to the low fitness class shrank to a small portion of the population.

In addition to tracking yeast populations, this high-resolution lineage-tracking tool could also be used to track pathogenic microbes, cancer cell lines, and animal tumor models, the researchers said.

"Cancer and microbial infections can have population sizes up to 1012 cells in a single individual, suggesting that massive clonal interference and complex population dynamics are likely to characterize disease progression and drug resistance," Sherlock and his colleagues wrote in their paper.

They added that the approach also could be applied to identify a treatment that slows the rate of adaptation.

"In combination with whole-genome sequencing, lineage tracking therefore offers a powerful method by which to characterize the mutational spectrum underlying evolution, disease progression, and drug resistance," they said.

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