We isolated two clones from each of six timepoints from six haploid populations evolved in rich media at a permissive temperature (YPD 30°C) and from six haploid populations evolved in a defined media environment at a high temperature (SC 37°C), a total of 144 clones. We measured the fitness of each of these clones in the environment to which they adapted, finding that fitness increases steadily through time in both the YPD 30°C and SC 37°C environments, and displays a general pattern of declining adaptability (Figure 1A).

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We next created a library containing each of 91 insertion mutations in each of our 144 clones. This set of mutations was identified previously as a subset of random insertion mutations that have measurable effects in the strains from Johnson et al., 2019. These mutations have a similar spectrum of effects in the clones isolated for this study (Figure 1—figure supplement 8), suggesting that they are also a broad sample of insertion mutations with measurable fitness effects in these strains. We measured the fitness effect of each mutation in each clone using barcode-based competition assays as described in Johnson et al., 2019. Because the molecular dynamics of evolution in these haploid populations are characterized by successive selective sweeps, we expect the two clones isolated from each population at each timepoint to have very similar genetic backgrounds. When we compare the average fitness effect measurement for each insertion mutation between these clones, we generally see strong agreement, with a few exceptions (Figure 1—figure supplements 1–3). These exceptions likely represent rare but important genetic differences between clones from the same population-timepoint. Given this, we chose to analyze our data in two ways. First, we improve the reliability of our fitness effect measurements for each population-timepoint by using measurements from combined barcodes (cBCs) from both clones, treating them as we treated biological replicates in Johnson et al., 2019. Second, we treat each clone independently. Figure 1—figure supplement 7, Figure 2—figure supplement 5, Figure 3—figure supplement 5, and Figure 4—figure supplement 1 show that our qualitative conclusions are unchanged when using this second approach.

We find that the distribution of fitness effects (DFE) of our 91 insertion mutations changes over the course of the evolution experiment. In the YPD 30°C environment, the mean of the DFE decreases over time as fitness increases during evolution (Figure 1B), consistent with a general pattern of both diminishing returns and increasing costs. The negative relationship between generations evolved and the mean of the DFE is significant in the entire set of population-timepoints (p=2.0 × 10^–6, Wald test) and in four of our six individual populations (p<0.05, Wald test). In contrast, although the DFE does shift in individual populations evolved in our SC 37°C environment, we do not see any consistent patterns (Figure 1B).

In both environments, we find that the changes in the mean of the DFE are modest compared to our previous experiment (Figure 1—figure supplement 6 shows that the slope of the regression between the DFE mean and background fitness in YPD 30°C is less than half the magnitude of the corresponding slope in Johnson et al., 2019). The reasons for this are unclear, but may reflect the fact that fitness differences between the strains we study here are caused by a smaller number of mutations. We note that because of the modest differences in the DFE between clones, the noise in our measurements contributes significantly to the variation. One potentially biased source of this noise is missing measurements: not all mutations have fitness effects measured in each population-timepoint due to differences in transformation efficiency or, most commonly, mutations that had too few read counts in the first two timepoints of the fitness assay. As in Johnson et al., 2019, missing measurements of strongly deleterious mutations are more common in more-fit strains (clones from later timepoints) in YPD 30°C, suggesting that the negative correlation between generations evolved and the mean of the DFE would be stronger if more of these deleterious mutations had been successfully measured in clones from later timepoints. Indeed, if we look at a limited set of mutations with measurements in every population-timepoint or if we ‘fill in’ missing measurements using their average fitness effect across population-timepoints, we see similar or stronger patterns of change (p<5 × 10^–7, Wald test) in the DFE mean in YPD 30°C (Figure 1—figure supplement 5). This relationship between the DFE mean and generations evolved also holds (p<5 × 10^–8, Wald test) in our analysis where clones are treated independently (Figure 1—figure supplement 7).

We next turn to the components of these distribution-level patterns: epistatic patterns for individual mutations. To look at these patterns, we focus on mutations that have fitness measurements in at least 20 population-timepoints in each environment (77 mutations in YPD 30°C, 74 in SC 37°C, 70 in both). We start by looking for patterns of fitness-correlated epistasis. We classify each mutation in each environment as correlated negatively or positively with background fitness if the correlation is significant at the p<0.05 level (Wald test) and the absolute value of the slope is greater than 0.05, and classify them as not significantly correlated if they do not meet these criteria. The cutoff of 0.05 for the slope was chosen to filter for mutations with an effect size across the range of background fitness that is larger than the typical standard error for our fitness effect measurements (see ‘Materials and methods’). For both environments, we find numerous examples of both negative and positive correlations, corresponding to increasing costs or decreasing costs, respectively, for deleterious mutations (and to diminishing returns or increasing returns, respectively for beneficial mutations).

In Figure 2, we show examples illustrating how the fitness effect of specific mutations vary across clones isolated from each environment, plotted as a function of the fitness of each clone in that environment (i.e., the background fitness). We find numerous examples of mutations that exhibit negative, positive, and nonsignificant correlations with background fitness in both environments (panels along the diagonal). We also find examples where a specific mutation exhibits a nonsignificant correlation in one environment and either a positive or negative correlation in the other (off-diagonal panels). Overall, consistent with our previous results, we observe about twice as many negative correlations as positive correlations. As we would expect from the DFE-level results, this imbalance is more pronounced in the YPD 30°C environment: 33/77 (~43%) mutations have fitness effects that decline significantly as background fitness increases in YPC 30°C compared to 17/74 (~23%) in SC 37°C. We also find that only 9/77 (~12%) mutations display the opposite pattern (fitness effects increase significantly as background fitness increases) in YPD 30°C, while 13/74 (~18%) display this pattern in SC 37°C. Because we are primarily focused on comparing the frequency of each pattern across environments, we report these values before multiple-hypothesis-testing correction here and in Figure 2; after a Benjamini–Hochberg multiple-hypothesis correction, these values fall to 24/77 (~31%), 15/74 (~20%), 9/77 (~12%), and 11/74 (~15%), respectively.

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The examples in Figure 2 demonstrate that correlations between fitness effects and fitness are common but often do not explain the bulk of epistasis. By definition, the fitness-correlated patterns we observe are the result of interactions between our insertion mutations and mutations that fix during the evolution experiment. If these interactions are all strictly ‘fitness-mediated,’ the fitness effects of mutations will be fully explained by a background fitness effect. Alternately, correlations between fitness effects and fitness could arise based on the average effect of a number of idiosyncratic effects that are more likely to be negative versus positive. To understand the relative contribution of these determinants of epistasis, we compare three linear models used to explain the fitness effects of a single mutation in each of our two environments:

1. The fitness model (XM): the fitness effect of the mutation is a linear function of background fitness.

2. The idiosyncratic model (IM): the fitness effect of the mutation can change in any population at any timepoint (and all subsequent timepoints) when an interacting mutation fixes in that population (see below for our constraints on fitting these parameters).

3. The full model (FM): the fitness effect of the mutation is affected by both a linear effect of background fitness and the idiosyncratic interactions of fixed mutations, as in the idiosyncratic model.

We fit each model by ordinary least squares (OLS). We define the fitness effect of each mutation in the ancestral strain as the mean fitness effect measured among clones from the first timepoint, and fix the intercept of each of our models accordingly. For the idiosyncratic and full models, we add idiosyncratic parameters iteratively, choosing the parameter that improves the Bayesian information criteria (BIC) the most at each step. These coefficients represent epistasis between the insertion mutation in question and one or more unknown mutations that fix during evolution in one population. Because mutations generally fix between every pair of timepoints during evolution, there could in principle be one idiosyncratic parameter for each timepoint in each population, but allowing all of these parameters would constitute overfitting. To combat this possibility, we do not allow parameters that fit a single point (e.g., a parameter for an effect at the final timepoint), and we only allow one parameter per population. We stop this iterative process of adding parameters if the BIC improves by less than 2 during a step (or when there is one parameter per population). Note that because of this iterative parameter adding procedure, the full model may have a different set of idiosyncratic parameters and will sometimes have less explanatory power than the idiosyncratic model (i.e., IM is not nested in FM).

Figure 3A shows how well each model explains the fitness effect data for each mutation in each environment. We find that the fitness model often explains a modest amount of variance (mean R²: 18% in YPD 30°C, 19% in SC 37°C), but the idiosyncratic model (mean R²: 54% in YPD 30°C, 41% in SC 37°C) and the full model (mean R²: 53% in YPD 30°C, 44% in SC 37°C) usually offer more explanatory power. The examples in Figure 3B also demonstrate that epistasis is not strictly fitness-mediated; we commonly observe a stepwise change in the fitness effect of an insertion mutation in one evolving population, likely indicating epistasis between the insertion mutation and one or more mutations that fix in that population at a particular timepoint.

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We can also ask which model best explains the data using the BIC, which penalizes models based on the number of parameters. The small squares below the bars in Figure 3A indicate which model has the lowest BIC for each mutation. In YPD 30°C, the full model has the lowest BIC for 40/77 (~52%) mutations and the idiosyncratic model has the lowest BIC for 37/77 (~48%). In SC 37°C, the full model has the lowest BIC for 49/73 (~67%) mutations and the idiosyncratic model has the lowest BIC for 24/73 (~33%). When we assess how well each model fits the entire dataset in each environment, the full model has a lower BIC than the idiosyncratic model in both environments.

Positive and negative coefficients in the idiosyncratic model represent positive and negative epistasis between mutations that fix during evolution and our insertion mutations. While these coefficients can arise in our modeling procedure due to noise alone, we find far more coefficients in our empirical dataset than in a simulated or shuffled dataset (see ‘Materials and methods,’ Figure 3—figure supplement 4). The bulk of the coefficients we find in YPD 30°C are negative (214/291, ~74%), while both positive (131/245, ~53%) and negative (114/245, ~47%) coefficients are common in our idiosyncratic model in SC 37°C (Figure 3A insets). Note that many of the positive epistatic terms in our idiosyncratic model in SC 37°C are the result of a consistent reduction in the fitness costs of some deleterious mutations in the first 2000 generations of evolution in all populations (e.g., see the mutation in VAM6 in Figure 3B and others in Figure 3—figure supplements 6–37, and see Figure 3—figure supplement 3 for a breakdown of coefficients by individual mutations).

The differences we observe in epistatic patterns between environments could be caused by interactions between epistasis and the environment (‘G×G×E’ effects), differences in the adaptive targets (i.e., the functional modules subject to selection) in each environment, or a combination of the two. To tease apart these possibilities, we measured the fitness effects of mutations in clones evolved in YPD 30°C in the SC 37°C environment. We assayed the background fitness of each of these clones in SC 37°C and found that populations evolved in YPD 30°C sometimes experience large fitness declines in the SC 37°C environment (Figure 4C). We also observe widespread epistasis in this alternate environment, but do not observe any overall trends in the mean of the DFE in SC 37°C over the course of evolution in YPD 30°C (Figure 4A and B). Instead, the variance in our data is dominated by one particularly low fitness clone (from population P1C05, generation 7550) in which several insertion mutations were strongly beneficial (Figure 4C).

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When we apply the same set of models to this dataset, we again find that both background fitness and idiosyncratic effects can explain how many mutations’ fitness effects vary, with the latter again outperforming the former (Figure 4A). Notably, we observe relatively more positive epistatic coefficients (109/241, ~45%) than in YPD 30°C (77/291, ~26%), though we also observe a heavy tail of strongly negative coefficients. These patterns of idiosyncratic epistasis are not due to outsized contributions from a few populations; the distributions of IM coefficients for YPD 30°C populations are different in the two assay environments across populations (Figure 3—figure supplement 2). Overall, these results support the hypothesis that G×G×E effects underlie the differences we observe between environments (see also Hall et al., 2019), though they do not rule out the possibility that differences in adaptive targets between environments may also contribute.

By using our barcode-based mutagenesis system to assay the fitness effects of 91 specific gene disruption mutations across numerous genetic backgrounds spanning 8000–10,000 generations of laboratory evolution, we have described how overall mutational robustness (defined in terms of the average effect of this type of insertion mutation) changes during evolution. We then dissected these overall effects in terms of how the fitness effects of individual mutations change during evolution. We find that populations adapting to our YPD 30°C environment become less robust to deleterious mutations over time. This shift in the mean of the DFE in YPD 30°C is not caused by strictly fitness-mediated shifts in the fitness effects of mutations, but is instead the result of an excess of negative idiosyncratic epistatic effects (Figure 3). In contrast, in clones isolated from populations evolved in SC 37°C, epistatic interactions are more evenly divided between negative and positive effects.

The fact that populations evolved in YPD 30°C lost robustness as they increased in fitness over time is broadly consistent with our earlier work showing that the DFE becomes more strongly deleterious in more-fit genetic backgrounds (Johnson et al., 2019). However, the loss of robustness we observe here was not as strong as in this previous work (see Figure 1—figure supplement 6 for a comparison to the effect size in Johnson et al., 2019), and we did not observe any predictable change in the DFE mean in SC 37°C. One potential explanation for the weaker patterns we observe at the DFE level in this experiment is that there are simply less mutations involved compared to the genetic backgrounds from our earlier work: the genotypes in our previous experiment were derived from a cross between two yeast strains that differed at tens of thousands of loci, so the pool of mutations was both larger and more balanced (each mutation present in ~50% of clones) than in this study. If the overall patterns of fitness-correlated epistasis arise due to the collective effect of numerous idiosyncratic interactions, rather than a genuine fitness-mediated effect, we would therefore expect weaker trends here (Lyons et al., 2020; Reddy and Desai, 2021).

Our results also illustrate a form of hidden evolutionary unpredictability, despite the fact that our populations increased in fitness over time along a predictable trajectory (Johnson et al., 2021). As these populations adapted, they accumulated mutations that carry with them epistatic interactions with potential future mutations across the rest of the genome. The patterns of epistasis we observe among the insertion mutations in our study demonstrate that the predictability of these second-order effects varies widely: some potential future mutations show strong fitness-correlated effects in every population, while others are affected by a small number of idiosyncratic interactions with mutations that fix during evolution. These kinds of unpredictable patterns of epistasis could lead to fundamentally unpredictable evolutionary outcomes, with changes in the fitness effects of mutations dynamically closing off or opening up evolutionary pathways during adaptation.

Most of the insertion mutations we analyze in this experiment are deleterious across most or all genetic backgrounds. We find that these deleterious mutations tend to become more strongly deleterious over time in populations evolved in the YPD 30°C environment. This pattern results from an overabundance of negative epistatic interactions, which could involve either the beneficial mutations that drive fixation events or the neutral or weakly deleterious mutations that hitchhike to fixation during selective sweeps (McDonald et al., 2016). The strong predictive power of background fitness for the fitness effects of some mutations suggests that interactions with beneficial mutations are driving these patterns of epistasis, but idiosyncratic effects that deviate from these relationships hint at a role for interactions with hitchhiker mutations as well (Figure 3). Systematic backcrossing and mutagenesis experiments would be required to disentangle these patterns (along with any cases of higher-order epistasis involving multiple mutations that fix during evolution), but we suspect both types of interactions contribute to the epistasis we observe.

Among the relatively small number of beneficial insertion mutations we analyze, we find that the beneficial effects tend to decline over time, and almost universally shift to neutral or deleterious effects later in the experiment (Figure 3—figure supplement 1). These results provide additional examples of diminishing-returns epistasis among beneficial mutations, which can at least partially explain the pattern of declining adaptability often observed in microbial evolution experiments (Chou et al., 2011; Khan et al., 2011; Kryazhimskiy et al., 2014; Wünsche et al., 2017). One of these mutations has a clear functional story behind it: the beneficial effect of an insertion mutation in ADE5,7 is the result of breaking the adenine synthesis pathway upstream of a toxic intermediate, so when populations in SC 37°C fix other loss-of-function mutations that also break the pathway between the first two sampled timepoints in the evolution experiment, this mutation becomes neutral (we do not see this effect in YPD 30°C because most populations have fixed a mutation in the adenine pathway by the first sampled timepoint; for a more in-depth discussion of this case of epistasis and contingency, see Johnson et al., 2021).

The picture that emerges from our data is one in which idiosyncratic epistatic effects are largely unpredictable but also unbalanced (have a nonzero mean), which leads to correlations with background fitness. In both our own work and other studies of microbial evolution, the mean epistatic effect is negative: beneficial mutations tend to have negative epistatic interactions with both deleterious (Johnson et al., 2019, this study) and beneficial mutations (Chou et al., 2011; Hall et al., 2019; Karkare et al., 2021; Khan et al., 2011; Kryazhimskiy et al., 2014; Ono et al., 2017; Pearson et al., 2012; Perfeito et al., 2014; Rokyta et al., 2011; Wünsche et al., 2017).

At the broadest level, we can distinguish two potential sources of these unbalanced patterns of epistasis: the structure of biological systems and the set of mutations that fix during evolution. Both theoretical and experimental work has shown that genes within functional modules tend to have similar interaction profiles with other genes (Costanzo et al., 2016; Segrè et al., 2005). Given this kind of ‘monochromatic’ epistasis, all that is necessary for fitness-correlated epistasis to appear during evolution is for beneficial mutations to be clustered in a few modules. The strength and direction of fitness-correlated epistasis will then depend on the particular modules targeted by selection and how those modules interact with the rest of the cell. For example, the targets of adaptation in SC 37°C may be more related to heat stress than general components of growth (e.g., mutations in LCB3, which have been shown to reduce killing in certain heat stress conditions, are enriched in populations in SC 37°C) (Ferguson-Yankey et al., 2002; Johnson et al., 2021). If the strongly deleterious effects of some of our insertion mutations are exacerbated by heat stress, but a beneficial mutation reduces heat stress, we can expect a positive interaction between the two mutations. In contrast, we hypothesize that the deleterious effects of some insertion mutations become more pronounced when growth rate is increased by mutations in YPD 30°C (Johnson et al., 2019). In order to understand or predict these differences in epistasis specific to the evolution environment, we will need to better understand the functional structure of biological systems.

Each mutation that fixes during evolution has an immediate first-order effect on fitness, but also carries with it a second-order set of pairwise interactions whose strength and direction is determined by the structure of functional relationships between genes and biological modules. Through higher-order interactions, a mutation can also change the structure of these functional relationships, altering the complexity, redundancy, robustness, and evolvability of biological systems (reviewed in de Visser et al., 2003; Masel and Siegal, 2009; Masel and Trotter, 2010; Payne and Wagner, 2019). Our work here suggests that mutational robustness tends to decrease during evolution in some environments, but our data is limited to interactions with the set of ~200 mutations that fixed during 10,000 generations of laboratory adaptation. We expect the effects we observe here to dominate during rapid adaptation, but over longer evolutionary timescales, robustness may be more dependent on changes in the higher-order structure of biological systems.

An apparent contradiction emerges from our explanation for increasing-costs epistasis: if adaptation increases the control coefficients of core components of growth, why do we not see more positive epistasis between beneficial mutations in evolution experiments? Why do we instead usually see negative, diminishing-returns epistasis and declining adaptability? We propose that this discrepancy arises due to differences in the availability and form of beneficial and deleterious mutations. Based on previous work, we expect beneficial mutations in laboratory evolution experiments to be primarily loss-of-function mutations (Murray, 2020). In contrast to deleterious mutations, which can be spread across the genome, these types of beneficial mutations will rarely exist in well-adapted core components of growth, and will instead be clustered in a few adaptive targets. Within these targets, we believe loss-of-function beneficial mutations are often functionally redundant, meaning that they tend to decrease each other’s control coefficients for fitness. Beneficial mutations can be redundant by inactivating the same deleterious pathway (e.g., ADE pathway mutations discussed above), solving the same general problem (e.g., mutations shortening lag in Karkare et al., 2021), or changing a phenotype with a nonlinear fitness function (Chiu et al., 2012; Chou et al., 2014; Keren et al., 2016; Lunzer et al., 2005; Otwinowski et al., 2018). Nonmonotonic fitness functions can arise from phenotypes with both potential benefits and costs (Dekel and Alon, 2005), such that negative interactions between beneficial mutations and the benefit can lead to fitness-correlated epistasis that crosses neutrality, exhibiting diminishing returns, increasing costs, and sign epistasis (Figure 3—figure supplement 1).

This explanation provides a prediction: we will be more likely to see synergistic epistasis during evolution experiments when we observe beneficial gain-of-function mutations. Chou et al., 2009 provides a particularly strong example of a beneficial gain-of-function (promoter capture) mutation that is more beneficial in more-fit genetic backgrounds. In the long-term Escherichia coli evolution experiment, potentiating mutations acquired during adaptation in one population interacted positively with a beneficial gain-of-function mutation (also a promoter capture), enabling aerobic citrate utilization (Blount et al., 2012). Studies of evolutionary repair also provide examples of synergistic interactions between apparently nonredundant beneficial mutations (Fumasoni and Murray, 2020; Hsieh et al., 2020). These counterexamples underscore the fact that diminishing-returns epistasis is not a rule; it is a pattern that is overrepresented in evolution experiments due to a tendency for beneficial mutations to be loss-of-function mutations and to be clustered in a few adaptive targets. These tendencies may be weaker later in evolution experiments when mutations are spread more evenly across cellular modules, such that a period of declining adaptability caused by diminishing-returns epistasis early in an experiment gives way to a period of relatively constant fitness gains (Good and Desai, 2015).

Very few papers discuss epistasis between beneficial and deleterious mutations – most theoretical and experimental work has focused on epistasis between two beneficial mutations or two deleterious mutations. With these same-signed pairs of mutations, the terms negative and positive epistasis are consistent. Two beneficial mutations that interact negatively imply two deleterious reversions that also interact negatively. However, when we consider one of these beneficial mutations and the deleterious reversion of the other, they interact positively. We provide this note to clarify that increasing-costs epistasis, in which deleterious mutations exhibit negative epistasis with beneficial mutations, should not be considered the same as previous results demonstrating negative epistasis between two deleterious or two beneficial mutations. Instead, we should expect it in systems where more positive epistasis is observed between same-signed mutations. While epistasis is already a concept overladen with terminology, we submit that in some cases it may be more useful to classify interactions between mutations or cellular components as being functionally redundant or nonredundant in terms of fitness.

All strains used for this study were isolated from the evolution experiment described in Johnson et al., 2021. We isolated two clones from each of our focal populations at each sequencing timepoint. For this experiment, we used clones from 12 MATa populations, 6 from the YPD 30°C environment and 6 from the SC 37°C environment. We decided to include population P1B04, which exhibits a cell-clumping phenotype in preliminary imaging data, and to exclude population P1B03, which diploidized during evolution, and populations P3C04, P3F05, P3D05, and P3E02, which lost G-418 resistance during evolution (not being able to select on G-418 during transformation could allow the HygMX cassette to replace the KanMX cassette, leading to leakage during the selection step). Otherwise, we chose populations randomly. The ancestor of these populations is MJM361 (MATa, YCR043C:KanMX, STE5pr-URA3, ade2-1, his3Δ::3xHA, leu2Δ::3xHA, trp1-1, can1::STE2pr-HIS3 STE3pr-LEU2, HML::NATMX, rad5-535).

We used a previously created set of Tn7-based plasmid libraries to introduce the same set of ~100 mutations into each of our strains (Johnson et al., 2019). These plasmids contain a section of the yeast genome corresponding to one of these ~100 locations, interrupted by a Tn7 insertion containing a random DNA barcode and a HygMX cassette. Each barcode uniquely identifies the mutation and the plasmid library via a mapping established in earlier work (Johnson et al., 2019).

Our yeast transformation protocol is a scaled-up version of that used in Johnson et al., 2019, based on the method described in Gietz and Schiestl, 2007. We grew strains from freezer stocks overnight, diluted 750 μL into 15 mL YPD + ampicillin (100 g/mL), grew for 4 hr, pelleted the cells, and resuspended in 900 μL transformation mix and 100 μL plasmid DNA cut with NotI-HF (corresponds to 2 μg of plasmid; cut at 37°C for 3 hr, then heat-inactivated at 65°C for 10 min). We then heat-shocked this mixture at 42°C for 1 hr, recovered in 3 mL YPD + ampicillin for 2 hr, plated 25 μL on antibiotic selection plates to check efficiency, and then combined the rest with 40 mL YPD supplemented with antibiotics. For both agar and liquid-selective media, we included hygromycin (300 μg/mL), clonNat (20 μg/mL), and G-418 (200 μg/mL). We made frozen glycerol stocks of each transformation after ~48 hr of growth. All growth was conducted at 30°C either in a test tube on a roller drum (recovery) or in a baffled flask on an orbital shaker (all other steps).

We transformed 2 clones from 12 populations at 6 timepoints for a total of 144 transformations. We organized these transformations into three ‘VTn assays,’ each associated with 48 transformations using our 48 unique barcoded libraries.

Again, we followed the protocols established in Johnson et al., 2019 for our fitness assays. We used two types of media: rich YPD media (1% Bacto yeast extract [VWR #90000-726], 2% Bacto peptone [VWR #90000-368], 2% dextrose [VWR #90000-904]) and synthetic complete (SC) media (0.671% YNB with nitrogen [Sunrise Science #1501-250], 0.2% SC [Sunrise Science # 1300-030], 2% dextrose). We assayed our transformed libraries of clones from the YPD 30°C environment in both their evolution environment (YPD 30°C) and the SC 37°C environment, and clones from the SC 37°C environment in their evolution environment (SC 37°C). We first arrayed our transformation glycerol stocks into two 96-well plates corresponding to the two evolution environments, and then inoculated 8 μL from each well of these plates into four (for SC 37°C assays) or eight (for YPD 30°C assays) flat-bottom polypropylene 96-well plates containing 126 μL of media, supplemented with the same antibiotics as during the initial selection. To ensure efficacy of the antibiotics in the SC 37°C environment, we used media with MSG instead of ammonium sulfate (1.71 g/L YNB without amino acids or ammonium sulfate, 2 g/L SC, 1 g/L MSG). After this period of growth, we used YPD and SC supplemented with ampicillin (100 μg/mL) and tetracycline (25 μg/mL), matching the conditions of the evolution experiment. After 40 hr of growth in these plates, we started daily transfers.

At each daily transfer, we diluted YPD 30°C cultures 1/2¹⁰ and SC 37°C cultures 1/2⁸. During these transfers, we combined and mixed cultures from each well corresponding to the same clone/transformation to increase population size and reduce bottleneck noise. In the first (T0) transfer, we combined cultures from the eight plates that were initially inoculated from the freezer stock and diluted them into 20 96-well plates. In all subsequent transfers (T1–4), we combined cultures from all 20 plates and diluted them into 20 new plates. Specifically, for YPD 30°C, we diluted 3 μL from each well of 20 plates into 60 μL YPD (60 μL total, 1/2 dilution), mixed, then diluted 16 μL into 112 μL YPD (1/2³ dilution), mixed, and distributed 2 μL into 126 μL YPD in 20 plates (1/2⁶ dilution). For SC 37°C, we diluted 3 μL from each well of 20 plates into 60 μL SC (60 μL total, 1/2 dilution), mixed, then diluted 60 μL into 60 μL SC (1/2 dilution), mixed, and distributed 2 μL into 126 μL SC in 20 plates (1/2⁶ dilution).

Our fitness assays in YPD 30°C were originally performed alongside assays in SC 37°C that were later abandoned due to an issue with expired reagents (and repeated with appropriate reagents in our second round of assays). During these assays, we combined equal volumes of culture at the end of each transfer from every well corresponding to each of the three VTn assays in each environment. We can pool the cultures corresponding to each VTn assay because we know which barcodes correspond to which plasmid library/clone, so we can divide our barcode count data appropriately during sequencing analysis. We performed DNA extractions from two 1.5 mL pellets for each assay-timepoint from our YPD 30°C fitness assays and from four 1.5 mL pellets from our SC 37°C fitness assays using Protocol I from the Yeastar Genomic DNA Kit (Zymo Research), as described previously (Johnson et al., 2019). We then amplified barcodes using a two-step PCR protocol. We performed four first-round PCRs with 19 µL gDNA, 25 L 2X Kapa Hotstart Hifi MM, 3 µL 10 M TnRS1 primer, and 3 µL 10 M TnFX primer, and ran the PCR protocol: (1) 95°C 3:00, (2) 98°C 0:20, (3) 60°C 0:30, (4) 72°C 0:30, GO TO step 2 three times, and (5) 72°C 1:00. We purified these PCRs with PCRClean DX Magnetic Beads (Aline) using a 0.85× ratio. We then set up two second-round PCRs per sample by combining 25 µL purified PCR µ1 product, 1.5 µL ddH₂O, 10 µL Kapa Hifi Buffer, 1 µL KAPA HiFi HotStart DNA Polymerase, 5 µL 5 M N7XX primer (Nextera), and 5 µL 5 M S5XX primer (Nextera), and ran the PCR protocol: (1) 95°C 3:00, (2) 98°C 0:20, (3) 61°C 0:30, (4) 72°C 0:30, GO TO step 2 19 times, and (5) 72°C 2:00. We purified the resulting libraries with Aline beads, using a 0.7× ratio, then repeated the purification with a 0.65× ratio, and finally sequenced our pooled libraries on a NextSeq 550 (Illumina).

We process our sequencing data as described previously (Johnson et al., 2019). We first filter reads based on inline indices and quality scores, use regular expressions to extract barcode sequences, and combine barcode counts across timepoints for each VTn assay. Next, we use a single-bp-deletion-neighborhood method to correct errors in raw barcodes, assigning them to the set of known barcodes from each of our plasmid libraries. By associating barcodes with plasmid libraries, we associate them both with a fitness assay for a particular clone and with a particular insertion mutation, and we divide our barcode count data accordingly.

Again, we follow Johnson et al., 2019, with minor differences. First, we convert barcode counts to log-frequencies at each timepoint. After this preliminary step, we noticed a large number of log-frequency spikes, restricted largely to one timepoint in one of our VTn assays in SC 37C. These spikes in frequency very likely represent low-level sequencing library contamination from another timepoint due to primer cross-contamination. In Figure 1—figure supplement 9, we show that this is confined to this single timepoint and demonstrate how we can use a simple heuristic (excluding lineages whose log-frequency at timepoint 2 is 0.5 greater than both timepoint 1 and timepoint 3) to remove the barcoded lineages affected by this sequencing library contamination. This step excludes, on average, less than 1% of the reads from timepoint 2 in these assays. Next, we calculate fitness effects for each barcode, as described in Johnson et al., 2019. After excluding timepoints with less than 5000 total barcode counts, we measure the log-frequency slope for each barcode at each consecutive pair of timepoints, excluding timepoints in which the barcode has less than 10 counts. We scale each of these log-frequency slopes by the median log-frequency slope of barcodes associated with five neutral reference mutations, and then average these scaled values to get one fitness measurement for each barcode. As in Johnson et al., 2019, we observe a small fraction of outlier barcodes, which follow starkly different log-frequency trajectories than the other barcodes associated with the same insertion mutation, presumably due either to pre-existing mutations in the transformed culture or transformation artifacts (including two mutations being transformed together). We use a log-likelihood ratio test to identify barcodes whose read counts are inconsistent with barcodes near the median fitness measured for one insertion mutation. Based on iterative exclusion and exploration of frequency trajectories for this experiment, we chose a heuristic cutoff of 40 for the log-likelihood ratio required to exclude barcodes (for a detailed description of this method, see Johnson et al., 2019). Finally, to decrease noise from low-frequency barcode lineages while retaining the independent measurements unique barcodes provide, we randomly combine counts from individual barcodes into a maximum of five combined barcodes (‘cBCs’) per insertion mutation. Next, we repeat the fitness measurement process described above to get final fitness measurements for each cBC (s_cbc).

Again following Johnson et al., 2019, we calculate the mean and standard error of all cBCs fitness measurements for each insertion mutation (m) in each clone (c):

For each clone, we also calculate the standard error of our fitness measurements of our set of neutral barcodes (σ_neut,c). Since s_m,c is the difference between the measured cBC fitness for mutation m and the measurement of neutrality, we calculate the standard error for s_m,c as the square root of the sum of squares of these two errors:

To obtain the fitness effect of a mutation for a population-timepoint (s_m,p,t), we calculate an inverse-variance weighted average on the fitness effect measurements from each clone:

We only calculate s_m,p,t if there are at least three cBCs between the two clones. If one of the two clones has only one cBC, we use ${\sigma }_{m,c}$ from the other replicate as an estimate of the standard error to be used in inverse-variance weighted averaging. Note that because we only consider data with at least three cBCs during our analysis in which we treat clones independently, this estimate of the standard error is only used for the inverse-variance weighted averaging.

We calculate the standard error on this s_m,pt measurement as described above for individual clones using the s values for all cBCs associated with mutation m in both clones:

As described in Johnson et al., 2019, we use this conservative measure of standard error instead of the one given by inverse-variance weighting in order to better capture unspecified biological error between the two replicates. Again, we combine this standard error with the standard error of all neutral cBCs in both clones (σ_neut,pt):

To test whether mutations have effects significantly different from zero for each population-timepoint, we fit all cBC s values for a single mutation by OLS as described in Johnson et al., 2019. The OLS model includes a fitted term for the fitness effect of the mutation ($s_{mut}$), a fitted term (${\beta }_{mut}$) for differences in the effect between clones (indicated by r), and a normally distributed noise term ($e_{mut}$).

We use the t-statistic for the intercept to calculate p-values for whether $s_{mut}\ne 0$ for each mutation. We perform a Benjamini–Hochberg correction at the 0.05 level on the entire set of p-values we find.

For each population-timepoint or clone, in each condition, we calculate the mean of the fitness effects of all mutations with at least three cBCs, excluding distributions with less than 60 mutations with fitness effect measurements. We calculate the standard error of the mean of the distribution of fitness effects by combining variances from two sources: error in our measurement of mutation fitness effects and errors in our measurement of mean fitness. Since this second component of variance is shared across mutations, it is not scaled by the number of mutations:

where n is the number of mutations with measurements in the distribution (we use an analogous formula in our analysis on individual clones). Importantly, note that this measure of error for the DFE mean describes only noise from measured mutations and does not address the fact that some mutations do not have measurements in some clones or population-timepoints.

We examined the effect of these missing fitness effect measurements in several ways. First, we examined the mean of the DFE in sets of mutations shared across the set of population-timepoints we had assayed successfully in each condition. For the analysis of individual clone DFEs in each condition, we try to find the largest set of clones with at least 40 mutations with measurements in every clone. We do this by iterating through the list of clones, sorted by the number of measured mutations, and adding them to a set of clones until the number of mutations with measurements in every clone drops below 40. Next, we create ‘filled-in’ DFEs for each population-timepoint and each clone in which missing measurements are replaced with the mean fitness effect measured across all strains in a given condition. The pattern observed in YPD 30°C, in which the mean fitness effect decreases as populations evolve and gain fitness, is stronger (in terms of the p-value and R² of the regression) in both the set of shared mutations and the filled-in DFE than in our original analysis.

Finally, we examined the number of strongly deleterious mutations (defined as having a mean fitness effect <–0.05 across all population-timepoints in a given condition) that were not measured in each population-timepoint. In YPD 30°C, these strongly deleterious mutations are less likely to be measured in more fit strains, again suggesting that the relationship we observe would be stronger with complete data. The results of these analyses are plotted in Figure 1—figure supplement 5.

Because our modeling approach can be strongly influenced by outliers, we only consider fitness effect measurements for mutations with at least five cBCs across the two clones for these analyses (or three cBCs for the corresponding analysis in which clones are treated separately). First, we perform least-squares regression between background fitness and fitness effect for each mutation in each environment. As described in the main text, we classify mutations as being negatively or positively correlated with fitness based on both a statistical test (p<0.05, Wald test) and the effect size (|slope|>0.05). This heuristic slope cutoff is meant to filter for cases in which the range of fitness effects under consideration is larger than the typical noise for a single fitness effect measurement. In the YPD 30°C environment, a slope of 0.05 across an ~0.15 range of fitnesses will mean fitness effects should vary ~0.007, which is also the mean standard error of our fitness effect measurements in that environment (an analogous calculation in SC 37°C would yield a lower threshold, but we keep 0.05 for consistency).

Next, we fit our data with the three linear models described in the main text. To fix the intercepts of our models correctly, we first transform the fitness effect of each mutation by subtracting the mean fitness effect measured across all populations at the first timepoint of the evolution experiment (we denote each transformed fitness effect for mutation m in population p, timepoint t, measured in condition e as ${\tilde{s}}_{m,p,t,e}$ below). We similarly transform our background fitness variable by subtracting the average fitness measurement across all populations at the first timepoint (we denote each transformed background fitness in population p, timepoint t, measured in environment e as ${\tilde{x}}_{p,t,e}$ below). Then we fix the intercept at (0, 0) in the modeling described below. Note that our plots showing these model fits use the natural scales for fitness and fitness effects, not these transformed scales. The fitness model (XM) can be written as

where $e_{m,e}$ is a normally distributed noise term, and ${\beta }_{m,e}$ is a fitted parameter representing the slope between background fitness and the fitness effect of the mutation.

The idiosyncratic model (IM) can then be written as

$i_{p,t,e}$ is an indicator variable that is 1 at timepoints >= t in population p and zero in all other cases, and ${\alpha }_{m,p,t,e}$ is a fitted parameter associated with that indicator variable.

The full model (FM) can then be written as

In both idiosyncratic model and full model, the idiosyncratic epistasis terms (${\alpha }_{m,p,t,e}{i}_{p,t,e}$) are added iteratively. At each step, we add the parameter that decreases the BIC the most if that decrease is more than 2. We do not allow parameters that fit only a single point, and we do not allow more than one parameter per population (i.e., the maximum number of idiosyncratic parameters for a given mutation in a given environment is the number of populations, six). Importantly, the idiosyncratic terms in the idiosyncratic model and the full model for a given mutation in a given environment may be different, so IM is not nested within FM and IM may explain more variance than FM in some cases. Because we consider the fixed intercept term to be part of our models, we compute R² for each model as 1 – (the sum of squared residuals/the centered sum of squares). This R² value can be negative if the model explains less variance than a model with only a free intercept term, in which case we set R² to zero and do not plot the model in Figure 3 or Figure 4. All model fitting was performed by OLS using the Python package statsmodels (Seabold and Perktold, 2010).

To test how much noise affects our model fitting procedure, we ran our analysis on a simulated dataset and a shuffled dataset. In both cases, we focus on the sets of up to 36 fitness effect measurements associated with a mutation and a condition (with measurements in six populations and six timepoints). For the simulated dataset, we drew fitness effects for each set of mutations from a normal distribution with a mean of zero and a standard deviation equal to the mean empirical standard error for the fitness effects within the set of mutations (i.e., we drew all s_m,p,t,e values for a given m and e from a normal distribution with a standard deviation of $mean\left({\sigma }_{m,p,t,e}\right)$). For the shuffled dataset, we randomly shuffled within each set (i.e., we randomly shuffled all s_m,p,t,e values for a given m and e).

The distributions of the coefficients obtained from our modeling procedure for the empirical, shuffled, and simulated datasets are plotted in Figure 3—figure supplement 4. In YPD 30°C, we found 291, 91, and 87 IM coefficients in our empirical data, shuffled data, and simulated data, respectively. In SC 37°C, we found 245, 78, and 81 IM coefficients in our empirical data, shuffled data, and simulated data, respectively. In clones isolated from evolution in YPD 30°C and assayed in SC 37°C, we found 241, 60, and 60 IM coefficients in our empirical data, shuffled data, and simulated data, respectively. In YPD 30°C, we found 140, 45, and 36 FM coefficients in our empirical data, shuffled data, and simulated data, respectively. In SC 37°C, we found 88, 38, and 46 FM coefficients in our empirical data, shuffled data, and simulated data, respectively. In clones isolated from evolution in YPD 30°C and assayed in SC 37°C, we found 199, 63, and 57 FM coefficients in our empirical data, shuffled data, and simulated data, respectively.

We measured the background fitness of clones with fluorescence-based competitive fitness assays in duplicate for each clone in each environment using the reference strains strain 2490A-GFP1 and 11470A-GFP1 for the YPD 30°C and SC 37°C clones, respectively. We used the 2490A-GFP1 reference when we assayed the YPD-30°C-evolved clones in SC 37°C because some of these clones have very low fitness and 2490A-GFP1 has a lower fitness than 11470A-GFP1. We used the fitness difference measured between these two references in Johnson et al., 2021 to standardize the fitness measurements YPD-30°C-evolved clones in SC 37°C so that all fitness measurements in SC 37°C are on the same scale. Fitness assays were performed and data was analyzed as described in Johnson et al., 2021. Briefly, we maintained mixed cultures of our clones and fluorescent references for three daily growth cycles, as described above, and measured the frequency of fluorescent cells at each transfer using flow cytometry. We then calculated the fitness of each clone as the slope of the natural log of the ratio between the frequencies of the nonreference and reference cell populations over time. Finally, we calculated the mean and standard error of the fitness measurements for the two clones associated with each population-timepoint.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank Sergey Kryazhimskiy, Alena Martsul, Andrew Murray, and members of the Desai lab for useful discussions about experimental design and analysis. We thank Shreyas Gopalakrishnan, Juhee Goyal, and Megan E Dillingham for their help with isolating the clones used in this experiment. We thank Craig Miller and one anonymous reviewer for helpful discussion and comments during the revision process. This work was supported by an NSF Graduate Research Fellowship (to MSJ), the NSF (PHY-1914916), and the NIH (GM104239). Computational work was performed on the Cannon cluster supported by the Research Computing Group at Harvard University.

© 2022, Johnson and Desai

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