Our starting point was the observation that more-fit genotypes are often less adaptable than less-fit genotypes. This is at least in part explained by “diminishing returns” epistasis: adaptive mutations provide smaller benefits in more-fit strains than in less-fit strains.
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We wondered: Are more-fit strains more or less robust to deleterious mutations than less-fit strains? And does the fitness of the strain modulate the effects of deleterious mutations in a predictable way?
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It might seem easy to answer these questions with existing data from double-deletion studies. However, unavoidable normalization in these data prevents us from measuring the distribution of fitness effects of mutations. Plus, these measurements are not very accurate.
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So, we developed an accurate and easy method for measuring the effects of the same set of mutations across many strains. We generated plasmid libraries with ~100 or ~1000 yeast genome fragments with barcoded transposon insertions in them.
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When we transform a yeast strain with such library, we get a pool of uniquely barcoded mutants each of which carries a mutation at one of ~100 or ~1000 genomic loci. We measure fitness of all these mutants simultaneously by observing how barcode counts change over time.
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We applied this method to a collection of yeast “founder” strains that we got from
@leonidkruglyak (thanks!). These founders are offspring of a cross between lab and wine strains. Founders differ from each other at thousands of loci and span a range of fitness in our media.Prikaži ovu nit -
We made three main observations. Observation 1. More-fit strains are less mutationally robust in the sense that the distribution of fitness effects of mutations (DFE) has a lower mean and is skewed more towards deleterious effects than the DFE in less-fit strains.pic.twitter.com/ZWJkzaxsTr
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Observation 2. An individual deleterious mutation is typically more deleterious in a more-fit strain than in a less-fit strain. We call this “increasing cost” epistasis. This is not universal though: some mutations show other interesting patterns.pic.twitter.com/dX2mH7okCg
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Observation 3. Fitness of the background strains alone explains about 27% of variation in the fitness effect of a typical deleterious mutation, but for some mutations it is as much as 90% or as little as 0%.
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We also find QTLs that explain variation in the effects of some mutations. When we take background fitness and these QTLs into account, we can explain 50-90% of variance in the fitness effects of most non-neutral mutations.
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Our main conclusion is that climbing up a fitness landscape is difficult because, as fitness increases, furthers uphill steps become flatter and/or change to downhill, and many downhill steps become steeper.
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I think this supports the idea (not ours) that true fitness peaks are unattainable. Populations stop adapting not because they reach a tall or flat fitness peak but because at an intermediate level of fitness beneficial and deleterious mutations balance each other out.
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