Compensatory evolution to DNA replication stress is robust to nutrient availability

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1. Version published to 10.1038/s44320-025-00127-z

   Jun 26, 2025
2. 

   Review Commons

   Mar 21, 2025

   Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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   Reply to the reviewers

   Dear Review Commons editorial team,

   Thank you for coordinating the thorough and careful review of our manuscript. We are especially grateful to the four anonymous reviewers for recognizing the value of our work and for their constructive suggestions on how to improve it.

   We are encouraged by the positive reception of our main conclusions on the robustness of adaptation to DNA replication stress and its relevance to multiple fields. All reviewers provided insightful comments, with reviewers #2 and #4 emphasizing that further experimental validation of the hypothesized role of reduced dNTPs in alleviating fitness during constitutive DNA replication stress …

   Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

   Learn more at Review Commons

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   Reply to the reviewers

   Dear Review Commons editorial team,

   Thank you for coordinating the thorough and careful review of our manuscript. We are especially grateful to the four anonymous reviewers for recognizing the value of our work and for their constructive suggestions on how to improve it.

   We are encouraged by the positive reception of our main conclusions on the robustness of adaptation to DNA replication stress and its relevance to multiple fields. All reviewers provided insightful comments, with reviewers #2 and #4 emphasizing that further experimental validation of the hypothesized role of reduced dNTPs in alleviating fitness during constitutive DNA replication stress would strengthen the paper. While the precise molecular mechanisms underlying this suppression are not the primary focus of this manuscript, we are eager to perform additional experiments based on the reviewers’ suggestions.

   Below, we present a detailed revision plan in the form of a point-by-point response to their comments.

   Reviewer #1 (Evidence, reproducibility and clarity):

   This study investigates the compensatory evolutionary response of Saccharomyces cerevisiae to DNA replication stress, focusing on the influence of genotype-environment interactions (GXE). The authors used a range of experimental conditions with varying nutrient levels to assess evolutionary outcomes under replication stress. Their genomic analysis reveals that while glucose levels affect initial adaptation rates, the genetics of adaptation remain robust across all nutritional environments. The research offers new insights into the adaptability of S. cerevisiae, emphasizing the role of the nutritional environment in evolutionary processes related to DNA replication stress. It identifies recurrent advantageous mutations under different macronutrient availabilities and uncovers a novel role for the RNA polymerase II mediator complex in adaptation to replication stress. Overall, this well-designed study adds to the growing recognition of the complexity and robustness of evolutionary responses to environmental stressors. It provides strong evidence that compensatory evolution to replication stress is robust across varying nutritional conditions. It both challenges and reinforces previous findings regarding the resilience of the yeast genetic interaction network to environmental perturbations. The detailed analysis of specific compensatory mutations and their fitness impacts across different conditions offers valuable insights into adaptive dynamics over 1000 generations, contributing a clear empirical framework for understanding how replication-associated stress shapes evolutionary outcomes in diverse environments.

   Based on the analysis:

   1. The conclusions are generally well-supported by the presented data. The evolution experiments and genomic analyses are robust and provide convincing evidence for the study's main claims. The authors took steps to eliminate bias, such as maintaining an adequate Ne, which, if not done, could have compromised their conclusions by affecting genetic drift and limiting the population's access to beneficial mutations.

   2. The figures are well-designed and easy to understand.

   3. The methodology is well-described and appears reproducible. The authors provide sufficient details on experimental procedures. Experimental replication is adequate, with multiple evolutionary lines.

   4. They also made efforts to validate their observations, such as the validation of mutations, the prediction of interactions in the Med14 structure, and its potential implication in gene regulation, as well as the analysis of the cumulative fitness benefit and the reconstruction of the quadruple mutant.

   There are, however, a few results that would benefit from further clarification:

   1. The experimental design is strong, offering a diverse range of conditions. However, the high glucose condition (8%) stands out as significantly different from the neutral 2% condition, both in range and margin, compared to the low glucose conditions (0.25-0.5%). While this mainly affects growth profiles and evolvability in the early generations, a brief explanation in the discussion would strengthen the conclusions. Specifically, addressing:

   1. a) The rationale behind selecting these particular glucose concentrations.

   2. b) How other glucose concentrations might influence the outcomes. Providing this additional context would enhance the reader's understanding of the experimental setup and its potential implications, while also offering insights into the broader applicability of the findings and possible directions for future research.

   We thank the reviewer for pointing out the need to clarify the rationale behind the glucose concentrations used in our study, an aspect we agree should have been better explained. In response, we have added the following text detailing the chosen conditions and their established effects on cellular metabolism.

   Line 67: “Glucose is the most abundant monosaccharide in nature, and represents the preferred source of energy for most cells.”

   Line 110: “...we grew WT and ctf4Δ cells in varying glucose concentrations to induce distinct physiological states. Low glucose levels (0.25% and 0.5%) induce caloric restriction and ultimately glucose starvation (Lin et al 2000, Smith et al. 2009). These conditions elicit increased respiration (Lin et al., 2002), sirtuins expression (Guarente, 2013), autophagy (Bagherniya et al. 2018), DNA repair (Heydari et al., 2007), and reduced recombination at the ribosomal DNA locus (Riesen and Morgan, 2009) ultimately extending lifespan in several organisms (Kapahi et al., 2016). In contrast, standard laboratory conditions typically use 2% glucose, promoting a rapid proliferation environment to which strains have been adapted since laboratory domestication (Lindergren, 1949). Finally, elevated glucose concentrations (such as 8%) result in higher ethanol production (Lin et al., 2012) and reactive oxygen species (ROS) levels (Maslanka et al., 2017).

   1. In the discussion section, a more explicit comparison with similar studies in other model organisms would help contextualize the findings within the broader field of evolutionary biology. While the results appear robust, it would be beneficial to explore how they align with or contrast to previous studies on DNA damage, particularly in bacteria or highly complex eukaryotes.

   We appreciate this suggestion to better contextualize our findings within the broader literature, as it provides an opportunity to highlight the unique aspects of our work. While many studies have explored how environmental factors shape fitness landscapes and influence evolutionary strategies, to our knowledge, only a few have addressed this in the context of compensatory evolution, where cells must recover fitness lost due to intracellular perturbations. To address this point, we have added a discussion of additional examples involving other model organisms, highlighting their difference with the question asked in this work.

   Line 34: “Genotype-by-environment (GxE) interactions are well-documented. For example, several studies on E. coli have demonstrated how different environments influence fitness and epistatic interactions among adaptive mutations in the Lenski Long-Term Evolution Experiment (Ostrowski et al., 2005, 2008; Flynn et al., 2012; Hall et al., 2019). Adaptive mutations in viral genomes similarly exhibit variable fitness effects across different hosts (Lalic and Elena, 2012; Cervera, 2016). Furthermore, interactions between mutations in the Plasmodium falciparum dihydrofolate reductase gene have been shown to predict distinct patterns of resistance to antimalarial drugs (Ogbunugafor et al., 2016). However, the role of environmental factors in shaping evolution within the context of compensatory adaptation, when fitness defects primarily arise from intracellular perturbations, remains much less explored.”

   However, if the reviewer have particular additional studies in mind, we welcome further suggestions to include in the final manuscript.

   Minor comments:

   1. The presentation of data in the figures is clear and informative. However, some figure legends could benefit from more detailed explanations. For example, although the statistical tests used are mentioned in the methods section, it would be helpful to also include them in the figure legends, such as in legend 1acde, as well as in all other figures.

   We are now reporting the statistical test used for each comparison also in figure legends.

   1. In terms of broader conclusions, here are a few suggestions, though they are, of course, optional:

   a) The study could benefit from exploring the potential trade-offs of adaptive mutations in the hypothetical return to environments without replication stress, at least theoretically. This would provide a more comprehensive understanding of the evolutionary constraints.

   We thank the reviewer for the suggestion, we had performed the measurements but did not comment on them explicitly. We are now commenting on them as follows:

   Line 310: “In the WT background, all mutations were nearly neutral, with only minimal deleterious or advantageous effects on fitness depending on glucose concentrations (Fig S4A).”

   Line 468: “The nearly neutral effects on fitness of the core adaptive mutations in WT suggest that they are likely to persist even after the initial replication stress is resolved.”

   b) A brief discussion of the potential limitations of using lab strains versus wild isolates of S. cerevisiae would offer valuable context for the generalizability of the findings.

   This is an excellent point. While addressing it fully would warrant a separate manuscript, we provide our comments here, along with similar observations raised by this and other reviewers, as follows:

   Line 450: “How generalizable are our conclusions about the reproducibility of evolutionary repair to DNA replication stress across other organisms, species, or replication challenges? While dedicated future studies are needed to fully address these important questions, several lines of evidence are encouraging. A recent report demonstrated that the identity of suppressor mutations of lethal alleles was conserved when introduced into highly divergent wild yeast isolates (Paltenghi and van Leeuwen, 2024). Similarly, earlier work showed that even ploidy, which significantly alters the target size for loss- and gain-of-function mutations, affected only the identity of the genes targeted by selection, while the broader cellular modules involved remained consistent (Fumasoni and Murray, 2021). Moreover, divergent organisms experiencing different types of DNA replication stress exhibit some of the adaptive responses described here. For example, the yeast genus Hanseniaspora, which lacks the Pol32 subunit of the replisome, has also been reported to have lost the DNA damage checkpoint (Steenwyk et al., 2019). Human Ewing sarcoma cells carrying the fusion oncogene EWS-FLI1 frequently exhibit adaptive amplification of the cohesin subunit RAD21 (Su et al., 2021). Together, these findings suggest that while the specific details of DNA replication perturbations and the genomic features of organisms may shape the precise targets of compensatory evolution, the overarching principles and cellular modules affected are broadly conserved.”

   Furthermore, we plan to search a recently published database of variants found in natural isolates of S. cerevisiae to assess whether similar evolutionary processes to those described in this study may have occurred in wild strains.

   c) It would be valuable to present the differences in ploidy in the context of other studies, such as the nutrient-limitation hypothesis (e.g., 'The Evolutionary Advantage of Haploid Versus Diploid Microbes in Nutrient-Poor Environments' by Bessho, 2015), since, as previously demonstrated by the authors of this article that is being reviewed, ploidy may influence the evolutionary trajectories of DNA repair.

   d) Interrelating these three terms: nutrient-limitation, ploidy, and DNA repair could be an interesting avenue to explore in the discussion.

   In response to comments c and d, we have now commented on the intersection between ploidy and other types of DNA perturbation in the paragraph starting in line 491 (see response above)

   1. Specific details:

   a) Line 116: To improve clarity, it would be beneficial to refer to the figure right after the statement: 'However, their relative fitness improved compared to the WT reference as the initial glucose levels (Figure X).'

   b) Line 404: The statement about antibiotics and cancer progression is somewhat brief here; it might be helpful to provide more context on why this mechanism influences these processes (here or before).

   c) Line 418: "were re-suspended in water containing zymolyase (Zymo Research, Irvine, CA, US, 0.025 μ/μL), incubated at". Something is missing in the units.

   d) Line 459: "and G2 phases for each genotype was estimated by deriving the the relative cell distribution". The article "the" is repeated.

   e) 1a: The x-axis ticks appear misaligned, which makes it difficult to interpret the boxplots. For example, at 0.25, the tick is closer to the orange boxplot than to the black one. In contrast, at 2%, the tick seems well-centered."

   f) Figure 3 could benefit from a general legend at the top regarding the colors, as finding it in 2c was not intuitively easy.

   The typos and suggestions raised in points 3a-f have now been corrected in the manuscript.

   g) I didn't review the code on GitHub.

   Reviewer #1 (Significance):

   The main strength of the study is that it shows robustness of compensatory evolution across varying nutrient conditions. The study adds to the growing body of literature on DNA replication stress and evolutionary adaptation by showing that compensatory evolution can occur regardless of nutrient availability. This fundamental finding challenges prior assumptions that nutrient conditions significantly alter evolutionary outcomes, contributing to a more nuanced understanding of how cells respond to stress. Furthermore, the discovery of the RNA polymerase II mediator complex's role in this process is particularly novel and opens new lines of investigation.

   Advance in the field: The results advance our understanding of evolutionary biology, particularly in the context of DNA replication stress and compensatory evolution. The study demonstrates that evolutionary repair mechanisms are predictable, even under variable environmental conditions, which has key implications for evolutionary biology and therapeutic applications.

   Audience:

   This paper will be of interest to a specialized audience in evolutionary biology, genomics, and cell biology, particularly those interested in DNA replication stress and adaptive evolution. Researchers studying stress responses in model organisms, such as S. cerevisiae, will find the findings valuable, as will those working in applied fields where stress adaptation is a critical factor (e.g., industrial yeast fermentation, drug development, disease resistance, cancer research, or aging studies).

   Expertise:

   Evolutionary biology, genomic analysis, and cellular stress responses, with a particular focus on experimental evolution under DNA damage stress in Saccharomyces cerevisiae. Recently graduated and beginner reviewer.

   Reviewer #2 (Evidence, reproducibility and clarity):

   The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork progression, and/or reduced fork collapse at rDNA locus. Hence, wild types and ctf4-null show an opposite trend: in the mutant, the lowest concentration of glucose is the least affected by the mutation; in wild type, the highest concentration is the least affected. Adaptation rate is inversely related with the initial fitness. The effect on physiology and adaptation rate is a starting point for asking the key question: are evolutionary trajectories influnced by the growth conditions? The answer is negative: evolution experiments show the very same core of genetic changes at all sugar concentrations. The result is apparently at odds with previous publications, and the authors conclude that, in this particular setting, availability of carbon sources plays a minor role compared to impaired DNA replication. The different rates of adaptation in WT and mutant is rather explained by the initial fitness at the different glucose concentrations, which, as mentioned, is opposite in WT and ctf4-null mutants. The paper also reports a new mutation in MED14, component of the transcription mediator complex, which rescues the lack of Ctf4 activity. The study is interesting and asks a relevant question. The experiments are well executed and convincing, but the paper can be strengthened by testing some of the hypotheses which are put forward.

   Main points

   1- The raw data for evolutionary dynamics (Figure S2C) are fitted with the power law suggested by Wiser and Lenski, and return different values of the parameter 'b'. The authors say that the result depends greatly on the initial conditions ("due to the varying initial fitness of ctf4Δ cells across different glucose environments, they display an opposite trend to WT"). Around the initial values, however, the curves are non-monotonic, especially for low glucose availability. Both for WT and ctf4-null there is an initial drop in fitness, after which fitness increases. If one would neglect this initial dynamics, the value of the parameter 'b' would likely be different.

   The non-monotonic trend in fitness highlighted by the reviewer is likely due to technical factors: Fitness at Generation 0 was measured with high precision in a low-throughput manner early in the project. In contrast, fitness from Generation 100 to 1000 was measured later in the study in a high-throughput fashion, necessitated by the large number of competitions conducted (96 wells × 4 time points × 6 replicates = 2304 assays). This difference in methodologies may have introduced a slight offset when the datasets were combined at Generation 100. Following the reviewer’s suggestion, we have excluded the data point at Generation 100 responsible for this non-monotonic behavior and re-fitted the curves. While this adjustment has caused minor changes in the parameter ‘b’, the qualitative trends, particularly the opposing trends between WT and ctf4Δ as glucose increases, remain consistent (Figure_rev_only 1). To ensure transparency, we have retained all recorded fitness values in the original figure for reference.

   In general, one can question whether curves with this shape are best fitted by the power law proposed by Wiser and Lenski. For example, for the WT 0.25% glucose the linear fit gives a better R2 (why do the authors show the linear fit anyway?). This impression is further reinforced by the observation that Wiser and Lenski fit dynamics that last 50.000 generation, here the curves last 1/50th of it. In conclusion, I would question whether the parameter 'b' is a solid measurement of 'rate of adaptation'. Also, normalizations makes it difficult to appreciate the result shown in Figure 2B. I think the authors should look for a different way to show the different trend in adaptation dynamics for different glucose concentrations between wild types and mutants. For example, they could move Figure S2C in the main text to stress the result shown in Figure 2C, which already shows the difference between WT and mutant. This is especially true if what Figure 2C shows is (evo-anc)/evo. This is not fully clear to me: in the legend it refers to the delta, in the label of the y-axis I read that this is a percentage.

   We thank the reviewer for prompting us to clarify our methods for reporting fitness changes over time. The fitness values are reported, throughout the paper, as a percentage change relative to the reference WT strain. The gain in fitness during evolution (reported as Δ) represents the difference between the evolved strain (evo%) and the ancestral strain (anc%), calculated as Δ = evo% - anc%. This represents the absolute gain, rather than the relative gain. This value is still reported as a percentage as it’s the same scale and unit as the two values being subtracted. We have included additional details to clarify this aspect in the figure legend.

   “(C) Absolute fitness gains (Δ) at generation 1000 for evolved WT (upper panel, black) and ctf4Δ (lower panel, orange) populations. Box plots show median, IQR, and whiskers extending to 1.5×IQR, with individual data points beyond whiskers considered outliers. Absolute fitness gains were calculated by subtracting the ancestral relative fitness from the relative fitness of the evolved (Δ = evo% - anc%), both calculated as percentages relative to the same reference strain in the same glucose concentration.”

   To conclude: the data show a different trend between wild types and mutants, which is interesting. Fitting it with the power law seems to be neither required nor appropriate. I suggest the authors to show the WT vs mutant pattern differently.

   We followed the reviewer’s suggestion and moved Figure S2C, which depicts the detailed fitness trajectories over time, into the main manuscript as Figure 2D. We agree that presenting these trajectories alongside the absolute fitness gains (now in Figure S2C) provides a more intuitive and effective depiction of the evolutionary dynamics of WT and ctf4Δ strains without relying solely on the power-law fit. Additionally, we quantified the mean adaptation rate, calculated as the absolute fitness gain (Δ) divided by the total number of generations (now Figure 2B). While no individual method definitively captures the adaptation rates across the experiment, these complementary analyses consistently highlight the same trends noted by the reviewer. We have re-written the main text as follows:

   Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extent of the original defect (Fig S2C). ctf4Δ lines displayed an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global adaptation rate over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

   Overall, these results demonstrate that cells can recover from fitness defects caused by constitutive DNA replication stress regardless of the glucose environment. However, adaptation rates under DNA replication stress exhibit opposing trends compared to WT cells, with faster adaptation yielding greater fitness gains in higher glucose conditions.”

   2- In Figure S2C, the individual trajectories for WT at 2% glucose are strangely variable. In this case, plotting the average does not make too much sense. This result is strange, since this is the default condition, where cells are grown without any change of sugar concentration. Can the authors give any rationale? Are there other available results to replace those published in Figure S2C?

   We agree with the reviewer that the individual trajectories for WT at 2% glucose are intriguing. However, we do not find these results necessarily “strange” as they could be explained by the following rationale: WT cells have been cultivated in 2% glucose since the 1950s, likely fixing most beneficial mutations for this condition. When many isogenic strains are evolved in parallel, (a) some lines show no improvement due to the scarcity of available beneficial mutations, (b) others exhibit slight decreases in fitness due to genetic drift fixing deleterious mutations, and (c) a few lines discover rare beneficial mutations, leading to fitness increases. In contrast, other conditions represent “newer” environments with larger mutational target sizes, resulting in more consistent outcomes.

   Prompted by the reviewer’s comment, we look for other studies reporting detailed fitness measurements of evolved WT strains in standard laboratory media. We downloaded and plotted the fitness data from Johnson et al. 2021, where authors studied the evolution of WT strains over 10.000 generations. Interestingly, we see that in the early phase of the evolution (generations 500-1400) evolved lines show similar levels of variability in fitness as the one reported in our study (Figure_rev_only 2). Of note is that in Johnson et al. 2021 most of the adaptive mutations alleviate the toxicity of the ade2-1 allele. In our WT strain the gene was preemptively restored, furter reducing the target size for adaptation in YPD.

   We believe it is important to report these measurements and decided to leave the original data, with the appropriate quantifications of variability, in Figure 2.

   3- The molecular explanation given for the rescue of ctf4-null proposes a very relevant role for dNTPs downregulation. Particularly, both for Irx1 and med14-H919P, the authors propose that this happens via Rnr1 downregulation. At this stage, this is only a hypothesis. The molecular verification of the central role of Rnr1 downregulation would make the conclusion much stronger. For example, a preliminary test would imply that duplicating RNR1 in ctf4-null irx1-null and/or ctf4-null med14-H919P would revert the rescue. Any other experiment addressing this point would be useful to improve the paper.

   We agree that the experiment suggested by the reviewer, or similar tests, would substantiate our hypotheses and strengthen the paper. Specifically, we plan to perturb dNTP production in both ctf4Δ ixr1Δ and ctf4Δ med14-H919P mutants through genetic manipulation of known factors involved in dNTP synthesis. We will then compare the resulting fitness to the expectations based on our hypotheses: reduced fitness benefits of the double mutants upon increasing dNTP levels and/or increased fitness in ctf4Δ mutants by decreasing dNTP levels through alternative mechanisms.

   4- The authors propose from Figure S4B that the rescue of ixr1-null is less evident at low sugar concentration since both conditions trigger a reduction of dNTPs. I think this is interesting, since it would provide a link between glucose concentration and evolutionary trajectories to adaptation, which is what the authors wanted to study. In particular, one would predict that 0.25% glucose would see less ixr1-null than the other glucose conditions. I could not (was not able to) confute this hypothesis from the data shown in the paper. Likewise, for med14-H919P. If the authors have not tested it, it would be worth trying.

   We had reported the appearance and frequency of all ‘core adaptive mutations’ (Figure S6C) but did not explicitly test the likelihood of their appearance under different glucose conditions. Following the reviewer’s suggestion, we have now performed χ2 tests (on the presence or absence of mutations) and ANOVA tests (on their mean frequency) to determine whether any mutation is particularly enriched or depleted in a given glucose environment. At first glance, the results do not support the hypothesis proposed by the reviewer. However, we note that although ixr1 mutants are less beneficial in low glucose than in high glucose, they still confer an 8% fitness advantage, which is likely sufficient to drive clones to fixation. We believe the reviewer’s reasoning is correct but is potentially masked by the still elevated fitness advantage of ixr1 in low glucose.

   To better convey the results of this analysis, we have included a visual representation of the presence and frequency of the mutations in Figure 6A, and the results of the χ2 and ANOVA tests in Supplementary File 5. We also comment on the analysis as follows:

   Line 314: “Similarly, we did not detect differences in the frequency of occurrence (χ2 tests) or average fractions (ANOVA test) achieved by the mutations in the populations evolved under different glucose environments (Fig 6A, Fig S4C and Supplementary File 5. The presence of all mutations in the final evolved lines correlated with their fitness benefits, suggesting how their selection in all glucose conditions was mostly dictated by their relative fitness benefits, rather than the environment (Fig 6A).”

   5- The combination of the four genetic adaptation (Fig 6B) would benefit from an experimental verification to show that the different solutions are not mutually exclusive. This is not obvious: if more than one solution acts by reducing dNTPs, maybe their combined effect is less strong than what measured theoretically. The authors could derive some clones at the end of the experiment and Sanger sequencing some of the four genes, to confirm the co-presence of some of them in the same cell.

   The co-occurrence of nearly every combination of the four core adaptive mutations we identified can be inferred from their relative frequencies, as revealed by deep whole-genome sequencing of the evolved populations (Fig. S4C). In these data, we observe populations carrying each pairwise combination of mutations at frequencies exceeding 50%, implying their coexistence. Moreover, many combinations of mutations approach or reach fixation. A particularly striking example is ctf4Δ Population 11, evolved in 8% glucose, where all core adaptive mutations are present at 100% frequency. These findings provide robust evidence that the different adaptive solutions are not mutually exclusive and can coexist within the same genetic background.

   Nevertheless, we agree that experimentally verifying the compatibility and fitness of the four genetic adaptations described in Figure 6B (now Fig 6C) would further strengthen our conclusions. To this end, we plan to reconstruct all combinations of mutations observed at high frequency in the final evolved populations. We will then measure their fitness and compare it to that of the evolved populations, as well as to the theoretical expectations based on additivity currently presented in Figure 6C.

   Minor points

   Figures

   • S4B: in the legend it should be explained that it is compared to ctf4D

   We now report how the values were obtained in the figure legend:

   (D = |anc%|-|reconstraucted%|)

   -2A: the color code is not fully clear to me: what does green and blue indicate? higher and lower than 2%?

   We apogise for not having included an explicit description of the color code in Figure 2A. Throughout the paper blue refers to glucose starvation (light blue for 0,25%, dark blue for 0,5%), while green refers to glucose abundance (light blue for 2%, dark blue for 8%). We now include a detailed description of the color code when it first appears (Fig 1B) and make sure is properly reported in all figure legends.

   • S3A: the authors should show the statistical difference between WT and ctf4-null, which is mentioned as non-existent in p.6

   The p value is now represented in Fig S3A

   Text

   • RNR1 is not really the gene with the highest score in Figure 5D, not even close: can you give a rationale for pin-pointing it (see also main point 3)?

   The reviewer is correct. Perturbations of the mediator complex, which regulate the expression of most of RNA PolII transcripts, is expected to result in changes in the expression of a large set of genes. However, our focus on dNTPs and RNR1 is based on the following rationale:

   1. Gene Ontology Enrichment Analysis: The downregulated genes in our dataset are enriched for the 'nucleotide metabolism' term, which includes pathways critical for dNTP production and directly linked to DNA replication and repair.

   2. Role of RNR1: Among the downregulated genes, RNR1 stands out as it encodes the major subunit of ribonucleotide reductase, the rate-limiting enzyme in dNTP synthesis. This enzyme is essential for DNA replication, and cells experiencing constitutive DNA replication stress, as in our system, are particularly sensitive to changes in dNTP levels.

   To make this rationale more explicit to the reader, we are adding the following sentence in the discussion:

   Line 404: “Nucleotide metabolism, particularly ribonucleotide reductase, is essential for dNTP production. Given the role of dNTPs in regulating DNA replication and repair, the advantage of med14-H919P mutants in the ctf4Δ background may stem from reduced dNTP levels caused by the perturbed TID domain."

   In addition, following the reviewers’ suggestions, we are conducting additional experiments to investigate the role of med14-H919P mutants in enhancing fitness under conditions of constitutive DNA replication stress (See response to reviewer #4). We anticipate that the final revised manuscript will offer further insights into the role of dNTPs or present alternative explanations for the observed phenomena.

   • The med14-H919P mutation is observed in 22/48 wells. I guess the authors checked already: are some of these wells close to each other in the plate?

   Correct. We took significant precautions in our experimental design to prevent cross-contamination, as outlined in the Materials and Methods section. Specifically, rows of ctf4Δ samples were alternated with rows of WT samples. Daily dilutions were then performed row by row using a 12 channels pipette. This approach ensured that any potential carry-over of cells would result in them being placed in wells containing a different genotype, where they would be eliminated by the consistent use of genotype-specific drugs.

   As a result of these measures, we do not observe any distinct pattern of core genetic adaptation corresponding to the plate layout (Figure_rev_only 3). The only exception are mutations in IXR1, which appear in all ctf4Δ strains (albeit with different alleles, see supplementary File 3). Moreover, we reasoned that if a highly fit strain had invaded other wells, all the pre-existing mutations from its lineage would have been detected in those wells. However, apart from the recurrent ixr1 and rad9 mutations, which are also strongly adaptive, we find no evidence of shared mutations in wells carrying the med14-H919P allele (Figure_rev_only 4).

   • Compensatory evolution of ctf4-null in 2% glucose is the experiment published by Fumasoni and Murray in eLife. In that paper, there is no trace of mutations in MED14. I think the authors should comment on this (different method for detecting putative compensatory mutations?).

   We also noticed the absence of MED14 mutations in the eLife study by Fumasoni and Murray and find this discrepancy intriguing. One possible explanation lies in methodological differences. Our current study employed an improved version of the mutational analysis pipeline. However, we have not yet reanalyzed the original data from the previous study to determine whether MED14 mutations were present but undetected.

   Interestingly, in the current study, we observed that in 2% glucose, MED14 mutations arose in only 3 out of 12 populations, a frequency lower than in other glucose conditions (Figure S6C). Assuming a similar frequency occurred in the 8 populations evolved in 2% glucose by Fumasoni and Murray (2020), one would expect only 2 populations to carry the mutation. This number falls below the threshold required for our algorithm to detect statistically significant parallelism.

   Additionally, two significant experimental differences may also contribute to the observed discrepancy. First, the culture volumes and vessels differed: 10 mL cultures in tubes were used previously, whereas 1.5 mL cultures in 96-well plates were used in the current study.

   • I may be mistaken, but Szamecz et al do not actually investigate whether different conditions result in different evolutionary trajectories (i.e., different genetics), and so their results may not be at odds with those presented here.

   The reviewer is correct that Szamecz et al. do not explicitly test whether different conditions result in different evolutionary trajectories. However, in the section titled “Compensatory Evolution Generates Diverse Growth Phenotypes across Environments,” they examine how lines evolved in 2% YPD perform across various environments. They report how in roughly 50% of the cases tested, evolved lines showed either no improvement or even some lower fitness than the ancestor (Figure 5A).

   While this could be explained by the accumulation of detrimental non-adaptive mutations in specific contexts, it likely implies that the adaptive strategies compensating for the original mutation in one environment do not confer similar benefits in other environments. This observation contrasts with our findings in Figure 6D, where we demonstrate that the main adaptive strategies provide a consistent benefit across diverse environments, including those with glucose, nitrogen, or phosphate abundance or starvation.

   We have now modified the introduction, results and discussion to avoid misleading interpretations:

   Line 42: “Szamecz and colleagues examined the evolutionary trajectories of 180 haploid yeast gene deletions over 400 generations (Szamecz et al., 2014). They found that, while fitness recovery occurred in the environment where evolution took place, the evolved lines often showed no improvement over their ancestors in other environments. This suggests that compensatory mutations beneficial in one environment often fail to restore fitness in others.”

   Line 327: “A previous study in yeast showed how evolved lines which compensate for detrimental defects of gene deletions in standard laboratory conditions often failed to show fitness benefits compared to their ancestor when tested in other environments (Szamecz et al., 2014). We thus investigated the extent to which the core genetic adaptation to DNA replication stress was beneficial under alternative nutrient conditions.”

   Line 422: “What could explain the discrepancies between our results, and previous studies on evolutionary repair highlighting the role of the environment in shaping evolutionary trajectories (Filteau et al., 2015), and the heterogeneous behavior of evolved lines in various environments (Szamecz et al., 2014)?”

   typos

   p.18, line 564 preformed -> performed

   1. 6 line 189 with a strongly skew -> with a strong skew ?

   Typos are now corrected in the main text

   Reviewer #2 (Significance):

   This is a well-done paper that could be of interest for the community of evolutionary biologists, scientists working on metabolism and cell division. It addresses an interesting problem, how metabolism affects compensatory evolution. Among the strengths: experiments are well done, the results are novel, the cross-talk between metabolism and evolutionary repair is intriguing. Among the weaknesses, the fact that the molecular explanations for the observations are only hypothesized and not tested experimentally. This is where the authors could improve the manuscript.

   Reviewer #3 (Evidence, reproducibility and clarity):

   This paper combines phenotypic and genomic data from an experimental evolution study in yeast to assess how repeatable evolution is in response to DNA replication stress. Importantly, the authors ask whether genotype by environment interactions influence repeatability of their evolved lines. To this end, the authors have constructed an elegant highly-replicated experiment in which two yeast genotypes (WT and CTF4 KO) were evolved under a variety of glucose levels for 1,000 generations. Recurrent mutations are found across many replicates, suggesting that repeatability is robust to GxE interactions. Of course, the authors correctly identify that these results are dependent on many particulars, as is always the case in biology, but provide a comprehensive discussion to accompany their results. I do not have any major comments to give, but simply some suggestions and points of clarification.

   Major comments: N/A

   Minor comments:

   L19: I found the definition for compensatory evolution/mutations to be somewhat vague in the introduction (and subsequently throughout the text). It's clear that this was written for a more medical/physiological audience, but without a more explicit explanation of compensatory evolution/mutations, it became difficult to properly weigh some claims/discussions made by the authors later on. Do you define compensatory mutations as those which completely recover WT function/fitness, or are simply of opposite effect to the altered genotype? Others define "compensatory evolution" as simply any epistastically interacting amino acid substitutions (Ivankov et al, 2014). It would be nice to see more explicitly defined.

   We thank the reviewer for highlighting the need for a precise definition of compensatory evolution and compensatory mutations. We recognize that the literature encompasses multiple definitions, including the one cited by the reviewer, which emphasizes compensatory mutations within the context of structural biology. This particular definition, prevalent in molecular evolution, was introduced by Kimura (Kimura, 1985) and is frequently used to explain the co-occurrence of amino acid mutations within a protein. These mutations offset each other’s defects, restoring or maintaining protein function. Here, however, we are using an older and broader definition of compensatory mutation, first introduced by Wright (Wright, 1964, 1977, 1982) and frequently used in evolutionary genomics (e.g., Moore et al., 2000; Szamecz et al., 2014; Rajon and Mazel, 2013; Eckartt et al., 2024). This definition includes any mutation in the rest of the genome that compensates (fully or partially) for another mutation's detrimental effects on fitness.

   We have now included this definition in the introduction:

   Line 19: “Compensatory evolution is a process by which cells mitigate the negative fitness effects of persistent perturbations in cellular processes across generations. This adaptation occurs through spontaneously arising compensatory mutations anywhere in the genome (Wright, 1964, 1977, 1982) that partially or fully alleviate the negative fitness effects of perturbations (Moore et al., 2000). The successive accumulation of compensatory mutations over evolutionary timescales progressively repair the cellular defects, ultimately restoring fitness.”

   Line 361: “Our findings demonstrate that while glucose availability significantly affects the physiology and adaptation speed of cells under replication stress, it does not alter the fundamental genome-wide compensatory mutations that drive fitness recovery and evolutionary repair.”

   Along these lines, I would have liked to see a more direct comparison/discussion of the degree to which deletion lines recovered. I can see from Fig 2E and Fig S2B that fitness increased quite a bit; would it not be possible to include a figure on the degree of compensation (basically relative fitness of evolved deletion lines - relative fitness of ancestral deletion lines)?

   If the reviewer is suggesting calculating the difference between the evolved and ancestor fitness, the data is already in Figure S2B and S2D, defined as ‘Absolute fitness gains Δ’ and calculated as Δ = evo% - anc%.

   If instead is suggesting to plot the fitness of evolved deletion lines (Y axis) against the relative fitness of ancestral deletion lines (X axis), we have now produced the plot is Figure S2F.

   To better understand the extent of the fitness recovery in Ctf4 strains, we have also calculated and plotted the ‘relative fitness gain’ calculated as |evo%| / |anc%| *100 (Figure S2C)

   We are now commenting on these comparisons in the following paragraph:

   Line 171: “By generation 1000, both WT and ctf4Δ evolved lines achieved, on average, slightly higher fitness in low glucose compared to high glucose conditions (Fig S2B). However, due to the varying initial fitness of ctf4Δ cells across different glucose environments, they recovered the same extenct of the original defect (Fig S2C), displaying an opposite trend to WT, with increasing absolute fitness throughout the experiment as glucose concentration rose (Fig S2B vs S2D). The differint absolute fitness gains over the same number of generations highlight distinct mean adaptation rates (Fig 2B). These differences are evident when examining the evolutionary dynamics of the evolved lines over time (Fig 2C). Additionally, we approximated the fitness trajectories using the power law function (Fig 2C, dashed purple lines), previously proposed to describe long-term evolutionary dynamics in constant environments (Wiser et al., 2013). The parameter b in this formula determines the curve's steepness, and can be used to quantify the global fitness change over generations (Fig S2E). Collectively, these analyses demonstrate that, unlike WT cells, ctf4Δ lines adapt faster in the presence of high glucose. This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

   L57: Another minor nitpick that just comes down to semantics. When discussing "96 parallel populations", it invokes a higher sense of replication than is actually present in the study. I would rephrase this to something along the lines of "12 replicate populations across 8 treatments under conditions of [...]".

   We changed the sentence as follows:

   Line 66: “We evolved 96 parallel populations of budding yeast, organized into 12 replicate lines, across four conditions of glucose availability (from starvation to abundance) with or without replication stress.”

   L185-187: The wording here needs to be clarified. Be explicit in that are examine the ratio (or count) of synonymous to non-synonymous mutations here, otherwise the interpretations appears to be direct contradiction to the (as written) results. Only after viewing the supplemental figure was I able to figure out what exactly was meant here.

   We changed the sentence as follows:

   Line 212: “We found no significant differences in the numbers of synonymous mutations detected in evolved populations in WT and ctf4∆ populations (Fig. S3A). These results support the hypothesis that replication stress in ctf4∆ lines favors the retention of beneficial mutations, rather than simply increasing the overall mutation rate.”

   L349-350: The authors observe higher rates of adaptation in deletion lines than WT lines, and discuss this in adequate detail. Although not explicitly mentioned, this is consistent with a diminishing returns epistasis model (that could be beneficial to discuss, but is not necessary), which has been implicated in modulating the degree of repeatability observed along evolutionary trajectories (Wünsche et al. 2017). Although definitely not required for this already very nice manuscript, I think it would be very rewarding if the authors were to eventually analyze fine-scale dynamics of phenotypic and genomic adaptation to mine for these putative interactions and their influence on repeatability.

   We agree with the reviewer on how our results align with a model of diminishing returns epistasis. This pattern is apparent not only between ctf4Δ and WT lines but also among ctf4Δ lines evolved in different glucose conditions. This phenomenon likely arises from the interaction of various adaptive mutations, which we aim to explore further in a dedicated manuscript. However, until we do so, we prefer to refer generally to a pattern of declining adaptability. To explicit this trend we have now included Fig S2F and commented on it in the manuscript:

   Line 181: “This evidence aligns with the declining adaptability observed in other studies (Moore et al., 2000; Kryazhimskiy et al., 2014; Couce & Tenaillon, 2015), where low-fitness strains consistently adapt faster than their more fit counterparts (Fig S2F).”

   Line 388: "Our results are consistent with declining adaptability, as evidenced by the reduced rates of adaptation observed both between ctf4Δ and WT lines and among ctf4Δ lines evolved in different glucose conditions (Fig S2F)"

   Reviewer #3 (Significance):

   It is clear to me that a great deal of time and care has been put into this study and the preparation of this manuscript. The science and analyses are appropriate to answer the questions at hand, and it bodes well that whenever I had a question pop up while reading, they were typically answered immediately after. I think that this manuscript will be broadly relevant to both biologists both evolutionary and clinical, and was written in a way to be accessible to both.

   As someone with an expertise in repeatable evolution, I felt most excited by the observation of so many parallel substitutions at a single amino acid across deletion lines. As the authors rightfully point out in the results and discussion, it's likely that this degree of robustness is highly dependent on the particular mechanism of disruption that cells experience. The authors then go above and beyond to functionally validate the putative molecular mechanisms of (repeatable) adaptation in this system. While it may not always be possible to accomplish in non-model organisms, such multi-modal approaches will be crucial to advance the field of repeatable evolution.

   Reviewer #4 (Evidence, reproducibility and clarity):

   The authors investigated the effects of DNA replication stress on adaptation in different nutrient availabilities by passaging wild-type and ctf4Δ Saccharomyces cerevisiae in media with varying levels of glucose over ~1000 generations. The ctf4Δ strain experiences increased DNA replication stress due to the deletion of a non-essential replication fork protein. The authors found differences in evolution between wild-type and ctf4Δ yeast, which held across different growth media. This study identified a compensatory single amino acid variant in Med14, a protein in the mediator complex of RNA polymerase II, that was specifically selected in ctf4Δ strains. The authors conclude that while environmental nutrient availability has implications for cell fitness and physiology, adaptation is largely independent and instead dependent on genetic background. The data provide excellent support for the key aspects of the models, although some details are (to me) overstated.

   Major comments:

   • A ctf4Δ mutant strain was used to investigate the effects of replication stress. Why was this mutant chosen instead of other deletions that cause different types of replication stress?

   We appreciate the opportunity to clarify our rationale for choosing the ctf4Δ mutant. The following are the main reasons why we believe ctf4Δ strains represent an ideal tool to study a global perturbation of the DNA replication program over evolutionary timescales:

   1. General replication stress: The absence of Ctf4 perturbs replication fork progression, leading to a spectrum of replication stress-related phenotypes, including DNA damage sensitivity, single-stranded DNA gaps, reversed forks (Abe et al., 2018; Fumasoni et al., 2015), checkpoint activation (Poli et al., 2012), cell cycle delays (Miles and Formosa, 1992), increased recombination (Alvaro et al., 2007), and chromosome instability (Kouprina et al., 1992). This broad disruption makes it an excellent model for observing global perturbations in replication processes. In contrast, other mutants typically affect specific enzymatic (e.g., POL32 and RRM3) or signaling (e.g., MRC1) functions, making them better suited to address specific questions.
   2. Constitutive stress: Unlike drug-induced stress (e.g., Hydroxyurea; Krakoff et al., 1968) or conditional depletion systems (e.g., GAL1-POLε; Zhang et al., 2022), which cells can easily circumvent through single mutations, ctf4Δ enforces persistent replication stress. Its deletion cannot be complemented by a single mutation, ensuring a robust and consistent stress environment for evolutionary studies.

   We have now modified the main text to convey these advantages in a concise form:

   Line 91: “In the absence of Ctf4, cells exhibit multiple defects commonly associated with DNA replication stress, such as single-stranded DNA gaps and altered replication forks (Fumasoni et al., 2015), leading to basal cell cycle checkpoint activation (Poli et al., 2012). These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012), making ctf4Δ mutants an ideal model for studying the cellular consequences of general and constitutive replication stress over evolutionary time.”

   It's not clear from the study that the effects are generalizable to other forms of replication stress.

   As with any method to induce DNA replication stress (including commonly used drugs like HU) each approach inevitably affects replication in a specific manner. Testing the broader applicability of our conclusions would require evolving additional strains with different replisome perturbations. For instance, mutations in ELG1 and CTF18 (affecting the alternative Replication Factor C), POL30 (affecting the sliding clamp PCNA), POL32 (affecting Polε), RRM3 (protective helicase) and (MRC1 (coordinating leading strand activities and signalling to the checkpoint) would have to be taken into account. Furthermore, specific mutant alleles of Ctf4 that disrupt interactions with particular binding partners (Such as ctf4–4E and ctf4–3E, perturbing the interaction with the CMG helicase and accessory factors respectively) will be highly informative on which specific aspects of the replication stress generated by the lack of Ctf4 each adaptive mutation alleviate.

   However, accommodating such extensive variability would inflate the sample size to an extent that will become unfeasible within the experimental design focused on capturing parallel evolution over a nutrient gradient (the primary focus of this study). We agree that this is an important question and intend to address it comprehensively in a dedicated future study.

   • The authors could be clearer that a (the?) cause of the ctf4∆ fitness defect is spurious upregulation of RNR1. I don't think it is mentioned until the Discussion, but it is highly relevant to Fig 4, and to the adaptations one would expect from ctf4∆.

   We thank the reviewer for the opportunity to clarify this aspect. We do not think that the fitness defects of ctf4∆ cells stem solely from the spurious upregulation of RNR1. However, we believe that a major aspect of the evolutionary adaptation is aimed at decreasing dNTP levels, potentially through different mechanisms. We are now mentionig increased dNTPs as major phenotype of ctf4∆ and commenting on the hypothesis more clearly in the discussion.

   Line 93: “These defects result in severe and persistent growth impairments, cell cycle delays, elevated nucleotides pools and chromosome instability (Miles and Formosa, 1992; Kouprina et al., 1992; Poli at al., 2012)”

   Line 409: “This condition will, in turn, be detrimental when proliferation rates are high (as in WT in high glucose) but beneficial under constitutive DNA replication stress (ctf4Δ), where cells experience spurious upregulation of dNTP production (Poli et al., 2012; Davidson et al., 2012).

   • In Figure 1E, there is a very large spread in the relative fitness at 2% and 8% glucose, but this was not commented on. Is this heteroscedasticity expected?

   The observed heteroscedasticity is expected. Our competition assays tend to exhibit increased variability when a strain approaches very low fitness levels. Specifically, as one strain nears extinction by the third day of competition, its abundance is estimated based on a much smaller number of events in the flow cytometer. Furthermore, we noticed a small number of reference cells carrying pACT1-yCerulean not showing strong fluorescence in 8% glucose. The nature of this effect is uncertain, and possibly linked to metabolism-linked changes in the cytoplasm. The combination of these two phenomena amplifies the impact of noise inherent to the methodology, leading to increased variability across replicates.

   Nontheless, the overall decreasing fitness trend across glucose conditions, combined with the statistical significance observed between high and low glucose levels, collectively convey a roboust phenotype

   • The med14-H919P mutant was highly selected in ctf4Δ strains, independent of glucose availability. Is this variant found in any natural yeast strains (i.e., are there environments that select for this variant)? Also, if this variant is found in natural strains, does it co-occur with other mutations that could affect DNA replication?

   We agree that this is an intriguing question. To address it, we plan to explore existing databases of variants identified in S. cerevisiae natural isolates. Specifically, we will investigate whether the med14-H919P mutation is present in these strains, identify any potential environmental factors that may select for it, and assess whether it co-occurs with other mutations that could influence DNA replication processes.

   • The statement on lines 271-273 is not particularly well-supported. The analysis of the Warfield data suggest that reduced expression of RNR1 could be causal, but the data don't go as far as showing how the med14 mutation is advantageous in ctf4∆. Further experimentation would be necessary to support the possibilities that the authors discuss.

   The sentence the reviewer refers to is: “Overall, these results show how an amino acid substitution in the Med14 subunit of the mediator complex, putatively affecting transcription, is strongly selected, and advantageous, in the presence of constitutive DNA replication stress.” We are unsure which aspect of the statement is seen as unsupported. The mutation's strong selection in ctf4∆ is demonstrated in Figures 5A, 6A, and S4C, while its advantageous nature is supported by Figures 5B and S4B. Regarding the mechanism, we have been cautious with our phrasing, describing its effect on transcription as "putative" (Line 272) and suggesting that our observations “are compatible with” reduced dNTP availability in med14-H919P cells due to RNR1 downregulation (Line 361).

   The main focus of this study is to explore how nutrient availability influences evolutionary dynamics and compensatory adaptation in cells lacking Ctf4. We believe the identification of a novel selected allele (Fig. 5A) and confirmation of its benefit across glucose conditions (Fig. 5B) serves as an excellent complement to the primary conclusions (present in the title). We invite the reviewer to consider that the molecular basis of such a phenotype is not mentioned in our abstract, as we believe that its precise characterization would require a dedicated study on Med14.

   Nonetheless, we are encouraged by the reviewer’s interest in this newly identified compensatory mutant (also noted by Reviewer #2), and we are eager to perform further experiments to better understand the biological processes affected by this mutation. We plan to extend our work as follows:

   Based on known phenotypes associated with perturbations of Med14, we propose the following novel hypotheses regarding the mechanism by which med14-H919P alleviates ctf4Δ defects:

   1. Decreased replication-transcription conflicts: Conflicts between the transcription machinery and replication forks are known to cause fragile sites, leading to increased chromosome breaks and genomic instability (Garcia-Muse and Aguilera, 2016). A general reduction in PolII transcription during replication, resulting from perturbations of the mediator complex, could reduce these conflicts and mitigate the fitness defects observed in ctf4Δ cells.
   2. Increased cohesin loading: We have demonstrated that amplification of the cohesin loader SCC2 is beneficial in the absence of Ctf4. Recent findings (Mattingly et al., 2022) indicate that the mediator complex recruits SCC2 to PolII-transcribed genes. The med14-H919P mutation may enhance the fitness of ctf4Δ cells by facilitating cohesin loading during DNA replication.
   3. Decreased dNTP levels: As discussed in the manuscript, perturbations of Med14 subunits in the mediator complex reduce the expression of genes, including those associated with nucleotide metabolism. Notably, these include RNR1, the major subunit of ribonucleotide reductase. The med14-H919P mutation could benefit the ctf4Δ background by counteracting the reported spurious increase in dNTPs, which affects replication fork speed (Poli et al., 2012).

   We plan to distinguish between these hypotheses using the following approaches. First, the proposed mechanisms underlying Hypotheses 1 and 3 suggest that med14-H919P is a loss-of-function mutation, while Hypothesis 2 implies a gain-of-function effect. Testing the impact of a heterozygous med14-H919P allele in a homozygous ctf4Δ strain will allow us to differentiate between these two categories of mechanisms. Additionally, we aim to investigate the molecular process affected by the med14-H919P allele by analyzing its genetic interactions with genes involved in replication-transcription conflicts, cohesin loading, and dNTP production (See also response to reviewer #2).

   We believe that the results of these experiments will provide further insights on the mechanism of suppression exerted by med14-H919P in the presence of constitutive DNA replication stress, without diverting the reader from the main message of the paper.

   • The authors comment that the med14-H919P mutant could have implications for the stability of Med14, based on computational modelling. Verifying the stability of the med14-H919P in vivo would strengthen this discussion.

   We believe that in vivo and in vitro structural studies investigating the effect of this mutation on the stability and function of the Mediator complex are beyond the scope of this manuscript. These investigations would be more appropriately addressed in future, dedicated studies focused on these specific aspects.

   • In the discussion, the authors propose that the context of the perturbation may influence the robustness of adaptation. A more detailed explanation of this point (including a discussion of the findings of other similar studies investigating different conditions) would be helpful to further bolster this section.

   We are now supporting this concept more explicitly by commenting on other studies as follows:

   Line 429: “Third, the environment’s influence on compensatory evolution may depend on the specific cellular module perturbed and its genetic interactions with other modules that are significantly influenced by environmental conditions. For example, the actin cytoskeleton, which must rapidly respond to extracellular stimuli, is likely to be more directly influenced by environmental factors (Filateau et al., 2015) compared to the DNA replication machinery, which operates within the nucleus and is relatively insulated from such changes. Supporting this idea, a study examining mutants’ fitness across diverse environments found that conditions such as different carbon sources or TOR inhibition, similar to those used in this study, primarily affected genes involved in vesicle trafficking, transcription, protein metabolism, and cell polarity. In contrast, genes associated with genome maintenance, as well as their epistatic interactions, were largely unaffected (Costanzo et al., 2021)”.

   In addition, to further substantiate this hypothesis, we plan to re-analyze published datasets on fitness and epistatic interactions among genes in various environments, testing whether specific cellular modules are more prone to changes following shifts in nutrient conditions.

   Minor comments:

   • Competitions were performed between ctf4Δ strains and a constructed strain with yCerulean integrated at ACT1. Is the fitness of the fluorescent strain comparable to the ancestral wild-type strain (i.e., in a competition between the ancestral WT and the fluorescent strain, does either have an advantage)?

   We noticed a slight disadvantage of the reference strain compare to WT, likely due to the costs of the extra fluorescence reporter. However, the disadvantage is minimal, ranging from -0.5 to -2.5 depending on the glucose environment (raw measurments are reported supplementary file 1, sheet 5). To take this into account, all fitness reported in figures are normalized for the WT value measured in the same environment line 613: “Relative fitness of the ancestral WT strain was used to normalize fitness across conditions.​​”

   • In Figure 3, the legends for panels B and C appear to be swapped. Discussion of Figure 3 on pages 6 and 7 appear to reference the wrong panels.

   We are unsure about this typo. Main text and figure legend seem to refer to the appropriate panels, 3B for mutation fractions and 3C for mutation counts. Perhaps the organization of the panels with B being under A instead of on its right confounds the reader?

   • In Figure 4A and B, having the same colour scale between both heatmaps is misleading, as the scales are different. Consider having the same scale across both heatmaps so that enrichments are visually comparable.

   Following the reviewer’s suggestion we have have chosen a uniform heatmap to visually represent GO terms enrichment in WT and ctf4∆ genetic backgrounds.

   • In Figure 4C, having a legend in the figure for node size would be helpful to understand the actual number of populations with mutations in each gene.

   A legend for node size has now being added next to Figure 4C.

   Reviewer #4 (Significance):

   In this study, a high-throughput evolution experiment uncovered the effects of genetic background on the development of adaptive mutations. The authors were able to identify a single amino acid variant of Med14 (med14-H919P) that was positively selected in ctf4Δ. Furthermore, they demonstrated the causality of med14-H919P in conferring a fitness advantage in ctf4Δ. The novelty of this mechanistic finding opens future avenues of investigation regarding the interaction network of the mediator complex in conditions of DNA replication stress. A limitation of the study is that only one mechanism of replication stress was assessed (ctf4Δ). Other gene mutations that cause replication stress would be interesting to assess and would provide a more thorough investigation of the effects of DNA replication factors on evolvability. This work will be of interest to researchers in the population genetics and genotype-by-environment fields, as it suggests the robustness of evolvability to environmental factors in the specific condition of DNA replication stress. As discussed by the authors, this finding differs from other works that have linked environmental conditions to adaptive evolution to different conditions, and is concordant with work that indicates the robustness of genetic interactions to environmental stresses. Furthermore, the identification of the highly-selected med14-H919P variant will be of interest to the DNA replication field. There is the potential for future work investigating the role of Med14 in mediating the response to DNA replication stress in both yeast and mammalian cell contexts, since the authors note that there are links between altered mediator complex regulation and cancers. Although I suspect that the very different regulation of RNR in mammalian cells makes it unlikely that the kind of upregulation of dNTP pools seen in ctf4∆ would be induced by replication stress in mammalian cells.

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   Referee #4

   Evidence, reproducibility and clarity

   The authors investigated the effects of DNA replication stress on adaptation in different nutrient availabilities by passaging wild-type and ctf4Δ Saccharomyces cerevisiae in media with varying levels of glucose over ~1000 generations. The ctf4Δ strain experiences increased DNA replication stress due to the deletion of a non-essential replication fork protein. The authors found differences in evolution between wild-type and ctf4Δ yeast, which held across different growth media. This study identified a compensatory single amino acid variant in Med14, a protein in the mediator complex of RNA polymerase II, that was specifically …

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   Referee #4

   Evidence, reproducibility and clarity

   The authors investigated the effects of DNA replication stress on adaptation in different nutrient availabilities by passaging wild-type and ctf4Δ Saccharomyces cerevisiae in media with varying levels of glucose over ~1000 generations. The ctf4Δ strain experiences increased DNA replication stress due to the deletion of a non-essential replication fork protein. The authors found differences in evolution between wild-type and ctf4Δ yeast, which held across different growth media. This study identified a compensatory single amino acid variant in Med14, a protein in the mediator complex of RNA polymerase II, that was specifically selected in ctf4Δ strains. The authors conclude that while environmental nutrient availability has implications for cell fitness and physiology, adaptation is largely independent and instead dependent on genetic background. The data provide excellent support for the key aspects of the models, although some details are (to me) overstated.

   Major comments:

   • A ctf4Δ mutant strain was used to investigate the effects of replication stress. Why was this mutant chosen instead of other deletions that cause different types of replication stress? It's not clear from the study that the effects are generalizable to other forms of replication stress.
   • The authors could be clearer that a (the?) cause of the ctf4∆ fitness defect is spurious upregulation of RNR1. I don't think it is mentioned until the Discussion, but it is highly relevant to Fig 4, and to the adaptations one would expect from ctf4∆.
   • In Figure 1E, there is a very large spread in the relative fitness at 2% and 8% glucose, but this was not commented on. Is this heteroscedasticity expected?
   • The med14-H919P mutant was highly selected in ctf4Δ strains, independent of glucose availability. Is this variant found in any natural yeast strains (i.e., are there environments that select for this variant)? Also, if this variant is found in natural strains, does it co-occur with other mutations that could affect DNA replication?
   • The statement on lines 271-273 is not particularly well-supported. The analysis of the Warfield data suggest that reduced expression of RNR1 could be causal, but the data don't go as far as showing how the med14 mutation is advantageous in ctf4∆. Further experimentation would be necessary to support the possibilities that the authors discuss.
   • The authors comment that the med14-H919P mutant could have implications for the stability of Med14, based on computational modelling. Verifying the stability of the med14-H919P in vivo would strengthen this discussion.
   • In the discussion, the authors propose that the context of the perturbation may influence the robustness of adaptation. A more detailed explanation of this point (including a discussion of the findings of other similar studies investigating different conditions) would be helpful to further bolster this section.

   Minor comments:

   • Competitions were performed between ctf4Δ strains and a constructed strain with yCerulean integrated at ACT1. Is the fitness of the fluorescent strain comparable to the ancestral wild-type strain (i.e., in a competition between the ancestral WT and the fluorescent strain, does either have an advantage)?
   • In Figure 3, the legends for panels B and C appear to be swapped. Discussion of Figure 3 on pages 6 and 7 appear to reference the wrong panels.
   • In Figure 4A and B, having the same colour scale between both heatmaps is misleading, as the scales are different. Consider having the same scale across both heatmaps so that enrichments are visually comparable.
   • In Figure 4C, having a legend in the figure for node size would be helpful to understand the actual number of populations with mutations in each gene.

   Significance

   In this study, a high-throughput evolution experiment uncovered the effects of genetic background on the development of adaptive mutations. The authors were able to identify a single amino acid variant of Med14 (med14-H919P) that was positively selected in ctf4Δ. Furthermore, they demonstrated the causality of med14-H919P in conferring a fitness advantage in ctf4Δ. The novelty of this mechanistic finding opens future avenues of investigation regarding the interaction network of the mediator complex in conditions of DNA replication stress. A limitation of the study is that only one mechanism of replication stress was assessed (ctf4Δ). Other gene mutations that cause replication stress would be interesting to assess and would provide a more thorough investigation of the effects of DNA replication factors on evolvability.
   This work will be of interest to researchers in the population genetics and genotype-by-environment fields, as it suggests the robustness of evolvability to environmental factors in the specific condition of DNA replication stress. As discussed by the authors, this finding differs from other works that have linked environmental conditions to adaptive evolution to different conditions, and is concordant with work that indicates the robustness of genetic interactions to environmental stresses. Furthermore, the identification of the highly-selected med14-H919P variant will be of interest to the DNA replication field. There is the potential for future work investigating the role of Med14 in mediating the response to DNA replication stress in both yeast and mammalian cell contexts, since the authors note that there are links between altered mediator complex regulation and cancers. Although I suspect that the very different regulation of RNR in mammalian cells makes it unlikely that the kind of upregulation of dNTP pools seen in ctf4∆ would be induced by replication stress in mammalian cells.

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   Referee #3

   Evidence, reproducibility and clarity

   This paper combines phenotypic and genomic data from an experimental evolution study in yeast to assess how repeatable evolution is in response to DNA replication stress. Importantly, the authors ask whether genotype by environment interactions influence repeatability of their evolved lines. To this end, the authors have constructed an elegant highly-replicated experiment in which two yeast genotypes (WT and CTF4 KO) were evolved under a variety of glucose levels for 1,000 generations. Recurrent mutations are found across many replicates, suggesting that repeatability is robust to GxE interactions. Of course, the authors correctly …

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   Referee #3

   Evidence, reproducibility and clarity

   This paper combines phenotypic and genomic data from an experimental evolution study in yeast to assess how repeatable evolution is in response to DNA replication stress. Importantly, the authors ask whether genotype by environment interactions influence repeatability of their evolved lines. To this end, the authors have constructed an elegant highly-replicated experiment in which two yeast genotypes (WT and CTF4 KO) were evolved under a variety of glucose levels for 1,000 generations. Recurrent mutations are found across many replicates, suggesting that repeatability is robust to GxE interactions. Of course, the authors correctly identify that these results are dependent on many particulars, as is always the case in biology, but provide a comprehensive discussion to accompany their results. I do not have any major comments to give, but simply some suggestions and points of clarification.

   Major comments: N/A

   Minor comments:

   L19: I found the definition for compensatory evolution/mutations to be somewhat vague in the introduction (and subsequently throughout the text). It's clear that this was written for a more medical/physiological audience, but without a more explicit explanation of compensatory evolution/mutations, it became difficult to properly weigh some claims/discussions made by the authors later on. Do you define compensatory mutations as those which completely recover WT function/fitness, or are simply of opposite effect to the altered genotype? Others define "compensatory evolution" as simply any epistastically interacting amino acid substitutions (Ivankov et al, 2014). It would be nice to see more explicitly defined.

   Along these lines, I would have liked to see a more direct comparison/discussion of the degree to which deletion lines recovered. I can see from Fig 2E and Fig S2B that fitness increased quite a bit; would it not be possible to include a figure on the degree of compensation (basically relative fitness of evolved deletion lines - relative fitness of ancestral deletion lines)?

   L57: Another minor nitpick that just comes down to semantics. When discussing "96 parallel populations", it invokes a higher sense of replication than is actually present in the study. I would rephrase this to something along the lines of "12 replicate populations across 8 treatments under conditions of [...]".

   L185-187: The wording here needs to be clarified. Be explicit in that are examine the ratio (or count) of synonymous to non-synonymous mutations here, otherwise the interpretations appears to be direct contradiction to the (as written) results. Only after viewing the supplemental figure was I able to figure out what exactly was meant here.

   L349-350: The authors observe higher rates of adaptation in deletion lines than WT lines, and discuss this in adequate detail. Although not explicitly mentioned, this is consistent with a diminishing returns epistasis model (that could be beneficial to discuss, but is not necessary), which has been implicated in modulating the degree of repeatability observed along evolutionary trajectories (Wünsche et al. 2017). Although definitely not required for this already very nice manuscript, I think it would be very rewarding if the authors were to eventually analyze fine-scale dynamics of phenotypic and genomic adaptation to mine for these putative interactions and their influence on repeatability.

   Significance

   It is clear to me that a great deal of time and care has been put into this study and the preparation of this manuscript. The science and analyses are appropriate to answer the questions at hand, and it bodes well that whenever I had a question pop up while reading, they were typically answered immediately after. I think that this manuscript will be broadly relevant to both biologists both evolutionary and clinical, and was written in a way to be accessible to both.

   As someone with an expertise in repeatable evolution, I felt most excited by the observation of so many parallel substitutions at a single amino acid across deletion lines. As the authors rightfully point out in the results and discussion, it's likely that this degree of robustness is highly dependent on the particular mechanism of disruption that cells experience. The authors then go above and beyond to functionally validate the putative molecular mechanisms of (repeatable) adaptation in this system. While it may not always be possible to accomplish in non-model organisms, such multi-modal approaches will be crucial to advance the field of repeatable evolution.

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   Referee #2

   Evidence, reproducibility and clarity

   Review of "Compensatory evolution to DNA replication stress is robust to nutrient availability" from Natalino and Fumasoni.

   The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork …

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   Referee #2

   Evidence, reproducibility and clarity

   Review of "Compensatory evolution to DNA replication stress is robust to nutrient availability" from Natalino and Fumasoni.

   The paper addresses the effect of sugar availability in shaping compensatory evolution. The first observation of the paper is that cell physiology changes by modulating glucose availability also in strains that come with defective DNA replication (ctf4-null previously studied by the authors). An intriguing result is that ctf4-null grows comparatively better in low concentrations of glucose. This is hypothesized to be a consequence of both the decrease in dNTPs in low glucose, which causes slow down of fork progression, and/or reduced fork collapse at rDNA locus. Hence, wild types and ctf4-null show an opposite trend: in the mutant, the lowest concentration of glucose is the least affected by the mutation; in wild type, the highest concentration is the least affected. Adaptation rate is inversely related with the initial fitness.

   The effect on physiology and adaptation rate is a starting point for asking the key question: are evolutionary trajectories influnced by the growth conditions? The answer is negative: evolution experiments show the very same core of genetic changes at all sugar concentrations. The result is apparently at odds with previous publications, and the authors conclude that, in this particular setting, availability of carbon sources plays a minor role compared to impaired DNA replication. The different rates of adaptation in WT and mutant is rather explained by the initial fitness at the different glucose concentrations, which, as mentioned, is opposite in WT and ctf4-null mutants.

   The paper also reports a new mutation in MED14, component of the transcription mediator complex, which rescues the lack of Ctf4 activity. The study is interesting and asks a relevant question. The experiments are well executed and convincing, but the paper can be strengthened by testing some of the hypotheses which are put forward.

   Main points

   1. The raw data for evolutionary dynamics (Figure S2C) are fitted with the power law suggested by Wiser and Lenski, and return different values of the parameter 'b'. The authors say that the result depends greatly on the initial conditions ("due to the varying initial fitness of ctf4Δ cells across different glucose environments, they display an opposite trend to WT"). Around the initial values, however, the curves are non-monotonic, especially for low glucose availability. Both for WT and ctf4-null there is an initial drop in fitness, after which fitness increases. If one would neglect this initial dynamics, the value of the parameter 'b' would likely be different. In general, one can question whether curves with this shape are best fitted by
      the power law proposed by Wiser and Lenski. For example, for the WT 0.25% glucose the linear fit gives a better R2 (why do theauthors show the linear fit anyway?). This impression is further reinforced by the observation that Wiser and Lenski fit dynamics that last 50.000 generation, here the curves last 1/50th of it. In conclusion, I would question whether the parameter 'b' is a solidmeasurement of 'rate of adaptation'. Also, normalizations makes it difficult to appreciate the result shown in Figure 2B.

   I think the authors should look for a different way to show the different trend in adaptation dynamics for different glucose concentrations between wild types and mutants. For example, they could move Figure S2C in the main text to stress the result shown in Figure 2C, which already shows the difference between WT and mutant. This is especially true if what Figure 2C shows is (evo-anc)/evo. This is not fully clear to me: in the legend it refers to the delta, in the label of the y-axis I read that this is a percentage.

   To conclude: the data show a different trend between wild types and mutants, which is interesting. Fitting it with the power law seems to be neither required nor appropriate. I suggest the authors to show the WT vs mutant pattern differently.

   1. In Figure S2C, the individual trajectories for WT at 2% glucose are strangely variable. In this case, plotting the average does not make too much sense. This result is strange, since this is the default condition, where cells are grown without any change of sugar concentration. Can the authors give any rationale? Are there other available results to replace those published in Figure S2C?
   2. The molecular explanation given for the rescue of ctf4-null proposes a very relevant role for dNTPs downregulation. Particularly, both for Irx1 and med14-H919P, the authors propose that this happens via Rnr1 downregulation.

   At this stage, this is only a hypothesis. The molecular verification of the central role of Rnr1 downregulation would make the conclusion much stronger. For example, a preliminary test would imply that duplicating RNR1 in ctf4-null irx1-null and/or ctf4-null med14-H919P would revert the rescue. Any other experiment addressing this point would be useful to improve the paper.

   1. The authors propose from Figure S4B that the rescue of ixr1-null is less evident at low sugar concentration since both conditions trigger a reduction of dNTPs. I think this is interesting, since it would provide a link between glucose concentration and evolutionary trajectories to adaptation, which is what the authors wanted to study.

   In particular, one would predict that 0.25% glucose would see less ixr1-null than the other glucose conditions. I could not (was not able to) confute this hypothesis from the data shown in the paper. Likewise, for med14-H919P. If the authors have not tested it, it would be worth trying.

   1. The combination of the four genetic adaptation (Fig 6B) would benefit from an experimental verification to show that the different solutions are not mutually exclusive. This is not obvious: if more than one solution acts by reducing dNTPs, maybe their combined effect is less strong than what measured theoretically. The authors could derive some clones at the end of the experiment and Sanger sequencing some of the four genes, to confirm the co-presence of some of them in the same cell.

   Minor points

   Figures

   • S4B: in the legend it should be explained that it is compared to ctf4D .
   • 2A: the color code is not fully clear to me: what does green and blue indicate? higher and lower than 2%?
   • S3A: the authors should show the statistical difference between WT and ctf4-null, which is mentioned as non-existent in p.6

   Text

   • RNR1 is not really the gene with the highest score in Figure 5D, not even close: can you give a rationale for pin-pointing it (see also main point 3)?
   • The med14-H919P mutation is observed in 22/48 wells. I guess the authors checked already: are somee of these wells
     close to each other in the plate?
   • Compensatory evolution of ctf4-null in 2% glucose is the experiment published by Fumasoni and Murray in eLife. In that paper,
     there is no trace of mutations in MED14. I think the authors should comment on this (different method for detecting
     putative compensatory mutations?).
   • I may be mistaken, but Szamecz et al do not actually investigate whether different conditions result in different
     evolutionary trajectories (i.e., different genetics), and so their results may not be at odds with those presented here.

   typos

   p.18, line 564 preformed -> performed

   p. 6 line 189 with a strongly skew -> with a strong skew ?

   Significance

   This is a well-done paper that could be of interest for the community of evolutionary biologists, scientists working on metabolism and cell division. It addresses an interesting problem, how metabolism affects compensatory evolution. Among the strengths: experiments are well done, the results are novel, the cross-talk between metabolism and evolutionary repair is intriguing. Among the weaknesses, the fact that the molecular explanations for the observations are only hypothesized and not tested experimentally. This is where the authors could improve the manuscript.

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   Referee #1

   Evidence, reproducibility and clarity

   This study investigates the compensatory evolutionary response of Saccharomyces cerevisiae to DNA replication stress, focusing on the influence of genotype-environment interactions (GXE). The authors used a range of experimental conditions with varying nutrient levels to assess evolutionary outcomes under replication stress. Their genomic analysis reveals that while glucose levels affect initial adaptation rates, the genetics of adaptation remain robust across all nutritional environments.

   The research offers new insights into the adaptability of S. cerevisiae, emphasizing the role of the nutritional environment in evolutionary …

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   Referee #1

   Evidence, reproducibility and clarity

   This study investigates the compensatory evolutionary response of Saccharomyces cerevisiae to DNA replication stress, focusing on the influence of genotype-environment interactions (GXE). The authors used a range of experimental conditions with varying nutrient levels to assess evolutionary outcomes under replication stress. Their genomic analysis reveals that while glucose levels affect initial adaptation rates, the genetics of adaptation remain robust across all nutritional environments.

   The research offers new insights into the adaptability of S. cerevisiae, emphasizing the role of the nutritional environment in evolutionary processes related to DNA replication stress. It identifies recurrent advantageous mutations under different macronutrient availabilities and uncovers a novel role for the RNA polymerase II mediator complex in adaptation to replication stress.

   Overall, this well-designed study adds to the growing recognition of the complexity and robustness of evolutionary responses to environmental stressors. It provides strong evidence that compensatory evolution to replication stress is robust across varying nutritional conditions. It both challenges and reinforces previous findings regarding the resilience of the yeast genetic interaction network to environmental perturbations. The detailed analysis of specific compensatory mutations and their fitness impacts across different conditions offers valuable insights into adaptive dynamics over 1000 generations, contributing a clear empirical framework for understanding how replication-associated stress shapes evolutionary outcomes in diverse environments. Based on the analysis:

   1. The conclusions are generally well-supported by the presented data. The evolution experiments and genomic analyses are robust and provide convincing evidence for the study's main claims. The authors took steps to eliminate bias, such as maintaining an adequate Ne, which, if not done, could have compromised their conclusions by affecting genetic drift and limiting the population's access to beneficial mutations.
   2. The figures are well-designed and easy to understand.
   3. The methodology is well-described and appears reproducible. The authors provide sufficient details on experimental procedures. Experimental replication is adequate, with multiple evolutionary lines.
   4. They also made efforts to validate their observations, such as the validation of mutations, the prediction of interactions in the Med14 structure, and its potential implication in gene regulation, as well as the analysis of the cumulative fitness benefit and the reconstruction of the quadruple mutant.

   There are, however, a few results that would benefit from further clarification:

   1. The experimental design is strong, offering a diverse range of conditions. However, the high glucose condition (8%) stands out as significantly different from the neutral 2% condition, both in range and margin, compared to the low glucose conditions (0.25-0.5%). While this mainly affects growth profiles and evolvability in the early generations, a brief explanation in the discussion would strengthen the conclusions. Specifically, addressing:

   a) The rationale behind selecting these particular glucose concentrations.

   b) How other glucose concentrations might influence the outcomes.
   Providing this additional context would enhance the reader's understanding of the experimental setup and its potential implications, while also offering insights into the broader applicability of the findings and possible directions for future research.

   1. In the discussion section, a more explicit comparison with similar studies in other model organisms would help contextualize the findings within the broader field of evolutionary biology. While the results appear robust, it would be beneficial to explore how they align with or contrast to previous studies on DNA damage, particularly in bacteria or highly complex eukaryotes.

   Minor comments:

   1. The presentation of data in the figures is clear and informative. However, some figure legends could benefit from more detailed explanations. For example, although the statistical tests used are mentioned in the methods section, it would be helpful to also include them in the figure legends, such as in legend 1acde, as well as in all other figures.
   2. In terms of broader conclusions, here are a few suggestions, though they are, of course, optional:

   a) The study could benefit from exploring the potential trade-offs of adaptive mutations in the hypothetical return to environments without replication stress, at least theoretically. This would provide a more comprehensive understanding of the evolutionary constraints.

   b) A brief discussion of the potential limitations of using lab strains versus wild isolates of S. cerevisiae would offer valuable context for the generalizability of the findings.

   c) It would be valuable to present the differences in ploidy in the context of other studies, such as the nutrient-limitation hypothesis (e.g., 'The Evolutionary Advantage of Haploid Versus Diploid Microbes in Nutrient-Poor Environments' by Bessho, 2015), since, as previously demonstrated by the authors of this article that is being reviewed, ploidy may influence the evolutionary trajectories of DNA repair. Interrelating these three terms: nutrient-limitation, ploidy, and DNA repair could be an interesting avenue to explore in the discussion.

   1. Specific details:

   a) Line 116: To improve clarity, it would be beneficial to refer to the figure right after the statement: 'However, their relative fitness improved compared to the WT reference as the initial glucose levels (Figure X).'

   b) Line 404: The statement about antibiotics and cancer progression is somewhat brief here; it might be helpful to provide more context on why this mechanism influences these processes (here or before).

   c) Line 418: "were re-suspended in water containing zymolyase (Zymo Research, Irvine, CA, US, 0.025 μ/μL), incubated at". Something is missing in the units.

   d) Line 459: "and G2 phases for each genotype was estimated by deriving the the relative cell distribution". The article "the" is repeated.

   e) Fig. 1a: The x-axis ticks appear misaligned, which makes it difficult to interpret the boxplots. For example, at 0.25, the tick is closer to the orange boxplot than to the black one. In contrast, at 2%, the tick seems well-centered."

   f) Figure 3 could benefit from a general legend at the top regarding the colors, as finding it in 2c was not intuitively easy.

   g) I didn't review the code on GitHub.

   Significance

   The main strength of the study is that it shows robustness of compensatory evolution across varying nutrient conditions. The study adds to the growing body of literature on DNA replication stress and evolutionary adaptation by showing that compensatory evolution can occur regardless of nutrient availability. This fundamental finding challenges prior assumptions that nutrient conditions significantly alter evolutionary outcomes, contributing to a more nuanced understanding of how cells respond to stress. Furthermore, the discovery of the RNA polymerase II mediator complex's role in this process is particularly novel and opens new lines of investigation.

   Advance in the field: The results advance our understanding of evolutionary biology, particularly in the context of DNA replication stress and compensatory evolution. The study demonstrates that evolutionary repair mechanisms are predictable, even under variable environmental conditions, which has key implications for evolutionary biology and therapeutic applications.

   Audience:

   This paper will be of interest to a specialized audience in evolutionary biology, genomics, and cell biology, particularly those interested in DNA replication stress and adaptive evolution. Researchers studying stress responses in model organisms, such as S. cerevisiae, will find the findings valuable, as will those working in applied fields where stress adaptation is a critical factor (e.g., industrial yeast fermentation, drug development, disease resistance, cancer research, or aging studies).

   Expertise:

   Evolutionary biology, genomic analysis, and cellular stress responses, with a particular focus on experimental evolution under DNA damage stress in Saccharomyces cerevisiae. Recently graduated and beginner reviewer.

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7. 

   Version published to 10.1101/2024.10.29.620637 on bioRxiv

   Nov 2, 2024