Single nucleotide mapping of locally accessible trait space in evolving yeast

Tradeoffs constrain the improvement of performance of all traits simultaneously. Such tradeoffs define a Pareto optimality front that represents a set of optimal individuals that cannot be improved in any one trait without reducing performance in another trait. While widely assumed, direct experimental evolutionary approaches often fail to detect tradeoffs with many experiments generating individuals that improve performance in all measured traits. Moreover, even when an improvement in one trait is found to be associated with the loss of performance in another, it is hard to establish that such a negative correlation is not induced by the specific features of sampled mutations and that other possible adaptive mutations cannot escape such apparent tradeoffs. Here we detect tradeoffs and define the Pareto optimality front in the context of short-term evolution of S. cerevisiae in glucose-limited media. We have evolved barcoded yeast populations under several conditions, with each condition selecting for improved performance in different parts of the yeast growth cycle. By isolating hundreds of adaptive clones and quantifying their performances in each growth part of the cycle, we defined tradeoffs between performances in fermentation and respiration and respiration and stationary phase. Importantly, due to the large numbers of the studied clones we were able to claim that no single point mutation in the yeast genome can improve the performance beyond either of the defined optimality fronts. We found that in both cases the shape of the optimality front is convex suggesting the possibility of short term evolution to select for generalists. Finally, by sequencing hundreds of adaptive clones, we identified the molecular basis underlying identified trade-offs and revealed novel targets of adaptation.