Abstract Loss of gene function is common throughout evolution, even though it often leads to reduced fitness. In this study, we systematically evaluated how an organism adapts after deleting genes that are important for growth under oxidative stress. By evolving, sequencing, and phenotyping over 200 yeast lineages, we found that gene loss can enhance an organism’s capacity to evolve and adapt. Whereas gene loss often led to an immediate decrease in fitness, many mutants rapidly acquired suppressor mutations that restored fitness. Depending on the strain’s genotype, some ultimately even attained higher fitness levels than similarly adapted wild-type cells. Further, cells with deletions in different modules of the genetic network followed distinct and predictable mutational trajectories. Finally, losing highly connected genes increased evolvability by facilitating the emergence of a more diverse array of phenotypes after adaptation. Together, our findings show that loss of specific parts of a genetic network can facilitate adaptation by opening alternative evolutionary paths.

The regulatory processes in cells are typically organized into complex genetic networks. However, it is still unclear how this network structure modulates the evolution of cellular regulation. One would expect that mutations in central and highly connected modules of a network (so-called hubs) would often result in a breakdown and therefore be an evolutionary dead end. However, a new study by Koubkova-Yu and colleagues finds that in some circumstances, altering a hub can offer a quick evolutionary advantage. Specifically, changes in a hub can induce significant phenotypic changes that allow organisms to move away from a local fitness peak, whereas the fitness defects caused by the perturbed hub can be mitigated by mutations in its interaction partners. Together, the results demonstrate how network architecture shapes and facilitates evolutionary adaptation.

Single-cell RNA sequencing (scRNA-seq) is emerging as an essential technique for studying the physiology of individual cells in populations. Although well-established and optimized for mammalian cells, research of microorganisms has been faced with major technical challenges for using scRNA-seq, because of their rigid cell wall, smaller cell size and overall lower total RNA content per cell. Here, we describe an easy-to-implement adaptation of the protocol for the yeast Saccharomyces cerevisiae using the 10× Genomics platform, originally optimized for mammalian cells. Introducing Zymolyase, a cell wall–digesting enzyme, to one of the initial steps of single-cell droplet formation allows efficient in-droplet lysis of yeast cells, without affecting the droplet emulsion and further sample processing. In addition, we also describe the downstream data analysis, which combines established scRNA-seq analysis protocols with specific adaptations for yeast, and R-scripts for further secondary analysis of the data.

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Experimental evolution has grown to be a powerful and versatile tool to study the evolutionary dynamics of genetics. The principle is simple: cells are grown under a specific selective pressure, mutations arise over time, and lineages carrying mutations that increase fitness can outcompete others. Here we discuss how experimental evolution has allowed us to study various evolutionary processes, from clonal interference, to diminishing returns epistasis and genetic hitchhiking. Next, we discuss how experimental evolution can reveal how an organism's genotype affects evolutionary processes, how adaptation can sometimes fix even the most severe fitness defects, and how the approach can be used to learn more about the genetic architecture of complex traits. Finally, we look ahead at how experimental evolution can be used to study genetic networks and, conversely, how the structure of such networks influences evolution. Understanding adaptation by experimental evolutionAdaptation is the process by which organisms adjust to their environment and improve their chance of producing fecund offspring. During adaptation, mutations that increase fitness can get selected and fix themselves in the evolving population, a process that is fundamental in shaping the genetic architecture of complex traits. The historical view of adaptation starts with Darwin himself, and was one of gradualism, where adaptation takes place through many small mutations of minor effect. Fisher consolidated this view in 1930 in the form of a coherent but simplified model: the geometric model of adaptation [13]. In this model, Fisher argued that the probability that a random mutation has a beneficial effect drops quickly when the effect size of the mutation increases, according to 1 − φ(x), with x proportional to the effect size and φ the cumulative distribution function of a normal distribution.He concluded that small-effect mutations would dominate adaptation. Kimura pointed out in the 60s that fitness effects that were too small would not spread fast enough to fix efficiently in a population, pointing to the importance of intermediate effect mutations [26,27]. While these models have proven to be very useful, they were in fact based on indirect observations. This changed in the 1980s, when experimental evolution gained traction and allowed us to

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