The environmental stress response controls the biophysical properties of the cytoplasm and is critical for survival in quiescence

  1. Lorena Kronig
  2. Carmen A. Weber
  3. Pablo Aurelio Gómez-García
  4. Jonas S. Fischer
  5. Christian Doerig
  6. Agnès Michel
  7. Paolo Ronchi
  8. Sarah Khawaja
  9. Paola Picotti
  10. Benoît Kornmann
  11. Karsten Weis

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Abstract

All organisms have evolved survival strategies to cope with changes in environmental conditions. Nutrient deprivation, one of the most frequently encountered stresses in nature, causes haploid budding yeast to enter a reversible state of non-proliferation known as quiescence, which entails extensive remodeling of gene expression, metabolism and the cellular biophysical properties. Yeast cells can adapt to and survive long periods of time in glucose starvation-induced quiescence, provided they are able to respire in the early stages of glucose withdrawal. When respiration is blocked during glucose withdrawal, cells prematurely age and exhibit markedly reduced survival and cytoplasmic diffusion. We find here that respiration is required to induce a quiescence-related gene expression program. Induction of this program prior to withdrawing glucose in respiration-inhibited cells bypasses the need for respiration and rescues survival and biophysical properties to levels seen in glucose-starved but respiration-competent cells. This rescue effect relies on proteomic adaptation, which partially occurs through inactivation of Ras/PKA signaling and activation of the environmental stress response via the transcription factors Msn2/4. This signaling cascade triggers the expression of stress response genes and modulates the cytoplasmic diffusion state of cells, ensuring long-term survival in quiescence even in the absence of respiration. Our results highlight the importance of stress adaptation in quiescence and aging, integrating gene expression control and modulation of cytoplasmic properties to maintain cell fitness.

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    Reply to the reviewers

    We were very pleased to see the very positive evaluation of our work by all 3 reviewers and appreciate their constructive comments and suggestions. We have now addressed all reviewers’ comments by making changes and clarifications to the manuscript.

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    In the present manuscript, the authors present an in-depth study on the effect of a heat-shock response on the ability of yeast to regain viability after quiescence when their ability to respire is inhibited. They nicely demonstrate that these effects correlate with the measured diffusion coefficients, providing deeper insight into the (at least partially) responsible environmental stress response and the molecular players involved. This work is an important contribution to the growing (or resurging) field of the physical properties of the cell.

    We thank this reviewer for this very positive evaluation.

    My two main comments are the following:

    • The authors determine the diffusion coefficients from the MSD, as well as further analyze them all the way up to the confinement size. As far as I can judge from the manuscript, these analyses are for 2D systems and were initially developed for processes on membranes. How does this change for 3D systems? I understand that for a straightforward qualitative comparison of apparent MSD, this assumption is acceptable, but it may deviate more strongly with the additional analyses the authors present.

    This is indeed an important point, and the reviewer is correct that the trajectories are analyzed in 2D (x,y) while the cytoplasm is a 3D environment. We fully agree that this requires careful interpretation, particularly for metrics beyond the short-lag diffusion coefficient.

    First, for the diffusion coefficient, it is well established that for isotropic 3D motion the movements in all three dimensions are independent of each other and the projected 2D MSD satisfies:

    = 4*D*τ

    Thus, estimating from the short-lag slope of the 2D MSD yields the correct diffusivity of the underlying 3D process (up to standard experimental corrections such as localization error and motion blur). This approach is therefore widely used in cytoplasmic SPT and GEM studies, including in yeast, and is not restricted to membrane diffusion [1, 2].

    Regarding confinement-related metrics derived from longer time lags, we agree that these were originally developed and most rigorously interpreted for 2D systems. In our study, these quantities are intentionally used as effective in-plane (x,y) descriptors of particle motion rather than as a full reconstruction of a 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size depends on assumptions about geometry and isotropy and cannot be done uniquely without full 3D tracking. Nevertheless, MSD-based analyses have been successfully extended to explicitly model and quantify 3D confined diffusion in previous studies, provided that full 3D trajectories or well-defined confinement geometries are available. [2, 3]

    [1] Gómez-García, P.A., Portillo-Ledesma, S., Neguembor, M.V., Pesaresi, M., Oweis, W., Rohrlich, T., Wieser, S., Meshorer, E., Schlick, T., Cosma, M.P., Lakadamyali, M., 2021. Mesoscale Modeling and Single-Nucleosome Tracking Reveal Remodeling of Clutch Folding and Dynamics in Stem Cell Differentiation. Cell Rep. 34. https://doi.org/10.1016/j.celrep.2020.108614

    [2] Delarue, M., Brittingham, G.P., Pfeffer, S., Surovtsev, I. V., Pinglay, S., Kennedy, K.J., Schaffer, M., Gutierrez, J.I., Sang, D., Poterewicz, G., Chung, J.K., Plitzko, J.M., Groves, J.T., Jacobs-Wagner, C., Engel, B.D., Holt, L.J., 2018. mTORC1 Controls Phase Separation and the Biophysical Properties of the Cytoplasm by Tuning Crowding. Cell 174, 338-349.e20.

    [3] Lerner, J., Gómez-García, P.A., McCarthy, R.L., Liu, Z., Lakadamyali, M., Zaret, K.S., 2020. Two-parameter single-molecule analysis for measurement of chromatin mobility. STAR Protoc 1.

    Importantly, we do not assume perfect isotropy of the yeast cytoplasm. Local anisotropies are expected due to organelles, crowding heterogeneity, and cell geometry. However, the system is sufficiently close to isotropic at the length and time scales probed that the extracted confinement radius is highly reproducible across independent biological replicates. In our experiments, we observe consistent radius of confinements across three replicates, indicating that any bias introduced by partial anisotropy or projection into 2D is systematic and small.

    Based on the observed reproducibility and the finite depth of field of our measurements (~100 nm), we estimate that potential errors in the absolute values of confinement-related parameters arising from 2D projection and incomplete isotropy are on the order of We have now clarified this point explicitly in the Methods section, emphasizing that confinement parameters are effective 2D measures, that the cytoplasm is not assumed to be perfectly isotropic, and that the conclusions rely on consistent, comparative measurements obtained under identical imaging and analysis conditions. The updated Methods paragraph is as follows:

    […] Trajectory analysis: Radius of Confinement

    The radius of confinement was obtained only for the subgroup of confined trajectories. It quantifies the degree of confinement by estimating the radius of the 2D area explored by the particle in the imaging plane, which serves as a proxy measurement for the 3D volume that it explores. It was measured by fitting a circle-confined diffusion model to the TE-MSD (ensemble of all trajectories) (Wieser and Schütz, 2008).

    TE-MSD = R^2 * (1 - exp(-4*D*t_lag/R^2)) + O

    where R is the radius of confinement and D is the diffusion coefficient at short timescales. O is an offset value that comes from the localization precision limit inherent to localization-based microscopy methods.

    Trajectories were analyzed in the imaging plane (x,y), and confinement metrics were therefore derived from 2D MSDs. Although particles diffuse in a three-dimensional cytoplasmic environment, projection onto 2D does not bias estimation of the short-lag diffusion coefficient for isotropic motion, since the projected MSD follows ⟨Δr_xy²(τ)⟩ = 4Dτ. However, confinement-related parameters derived from longer lag times should be interpreted as effective in-plane descriptors of mobility rather than as a direct reconstruction of a full 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size would require explicit assumptions about geometry or full 3D tracking. Our conclusions rely on comparative analyses performed under identical imaging and analysis conditions, and the extracted confinement radii were highly reproducible across biological replicates, indicating that any bias introduced by 2D projection or moderate anisotropy is systematic and does not affect the validity of the relative differences reported.

    • The authors show data in the supporting information where the GEMs provide larger foci after stress with longer imaging times. Could the authors provide the images of the shorter imaging times that they use? That seems a more equal comparison than Figure C. It is also unclear to me why fixed cells are used in Figure C, as well as the meaning of the x-axis. In line with this, can the authors exclude that GEMs dimerize/oligomerize after stress, and therefore display a lower diffusion coefficient?

    We are happy to include the images acquired at a shorter time interval and have done so (Fig S2A). We apologize for insufficiently explaining the GEM intensity experiment shown in Figure S2C. The fixation was done to immobilize the GEMs, since they are rapidly diffusing in live cell imaging and the diffusion speed relative to camera exposure time will impact the brightness (any movement of a particle during exposure causes the signal on the detector to become “blurred” and reduces the intensity per pixel). Hence, GEM brightness does not solely reflect the monomer or potential aggregate/multimer state, but is also affected by diffusion speed and exposure time: faster moving GEMs will generally appear dimmer than slower moving ones, since the signal detection during the acquisition time is reduced by the particle movement. Another effect is that, since GEMs are moving in live cell imaging, they have a probability of spatially overlapping, enhancing the signal levels of the single detected spots.

    We have quantified the brightness distribution in the different conditions to detect aggregation or multimerization of GEMs, which we expect to be visible as a shoulder on the Gaussian curve. The x-axis shows the intensity which we have determined for each trajectory. We chose to assess GEM intensity in the frame with the highest intensity, and to take the “Total” intensity, meaning we sum up the intensity of the pixels within the Point Spread Function (PSF) of each localization in that frame.

    To clarify these points, we have extended the description of this experiment in the Results and Methods sections:

    Results:

    [...] Additional evidence for this comes from the observation that imaging GEMs at a lower frame rate (i.e., longer exposure time of 100 ms) showed a uniformly diffuse signal in *SCD, *whereas distinct foci appeared under starvation conditions (Figures S2A and S2B). This might suggest that GEMs aggregate in starvation. However, imaging GEMs at a faster frame rate (used for SPT, 30 ms exposure time) shows GEMs freely diffusing in all conditions (Figure S2A). Furthermore, analyzing GEM particle intensities in fixed cells, to eliminate motion blur-induced intensity attenuation, showed uniform GEM brightness distributions in all conditions (Figure S2C). Rather than aggregates, the bright foci thus represent immobile, single GEM particles that are confined and appear brighter during long exposure times due to their confinement in low-diffusive compartments. [...]

    Methods:

    [...] Trajectory analysis: Track Total Intensity

    To assess GEM brightness, we determined the intensity of each trajectory in fixed cells. Cell fixation eliminates the motion blur-induced intensity attenuation, which would otherwise confound the GEM brightness depending on the movement speed and confinement. For each individual particle trajectory, the frame with the highest signal intensity of the localized particle was determined and the sum of the pixel intensities of the particle in that frame was calculated as the “Track Total Intensity”. In fixed cells, the GEM intensities were comparable in all conditions (Figure S2C). All GEM intensity histograms show a single, bell-shaped distribution of intensities with no indication of several GEM particles aggregating into brighter foci. [...]

    Other comments:

    • For the precision of the language, the authors equate ribosome content with macromolecular crowding, with the diffusion of the GEMs throughout, and this becomes more conflated in the discussion, where it is compared to viscosity and macromolecular crowding effects, e.g., translation. Is it macromolecular crowding, mesoscale crowding, nano-rheology, or ribosome crowding? What is measured precisely?

    We agree that careful and consistent nomenclature is important and thank the reviewer for bringing this point to our attention. We believe our manuscript maintains the proper distinctions of the terms diffusion, crowding and viscosity. We refer to what we study with the GEM single-particle tracking consistently as “(cytoplasmic) diffusion”. In Figure 2, we add “crowding” as an additional term since we observe a change in ribosome concentration and we affect the cytoplasmic crowdedness with a hyperosmotic shock. Our in-depth analysis of the confined and unconfined trajectory diffusion suggested that the cytoplasm is not simply globally affected by crowding or viscosity, but contains regions or compartments that trap GEM. Apart from Figure 2, we do not use the term viscosity or crowding, and we only return to “crowding” in the Discussion, either in reference to the aforementioned experiments from Figure 2 (ribosome concentration, hyperosmotic shock) or when discussing studies from cited works.

    However, we did not use the term “macromolecular crowding” consistently and simplified it to “crowding” in a few instances. To be more precise, we now specify “macromolecular crowding” instead of “crowding” wherever applicable; namely in the text referring to Figure 2, where we specifically assess macromolecular crowding.

    • In the EM images, the ribosomes seem smaller after starvation. Is that correct, and how should we interpret this? Is this due to an increased number of monosomes?

    This is an important point, and it indeed appears that in SCD some ribosomes are close together, potentially as polysomes. In SC, the ribosomes appear more distinctly separated from each other, which would be expected due to the polysome collapse that occurs in starvation. However, the apparent size of individual ribosomes is identical in both conditions. Unfortunately, the resolution is not good enough to accurately measure the sizes of the ribosomes and clearly determine their monomer/polysome state.

    • The authors refer to recent work on how biochemical reactions, such as translation, are determined by the cytoplasm. There is some older work on this, see for example in bacteria https://doi.org/10.1073/pnas.1310377110, and also in vitro here DOI: 10.1021/acssynbio.0c00330

    We thank this reviewer for pointing out these publications and have included them in this group of citations.

    • On the section of correlating diffusion and survival outcomes (bottom page 12), it is mentioned that the lowered diffusion could enhance aggregation. However, literature indicates that the opposite is true in buffer; lower diffusion reduces aggregation (also nucleation is inversely proportional to the viscosity).

    This is a valuable point and we have happily expanded on it in the Discussion section. It is true that chemical assays have demonstrated that higher viscosity and slower diffusion decrease nucleation and aggregate formation. However, in vitro studies that alter diffusion through crowding changes have revealed a complex relation between crowding and aggregation propensity. The basic idea is that the excluded volume effect decreases aggregation by stabilization of the more compact, folded state. But the opposite effect, precluded protein folding, has also been ascribed to the excluded volume effect. As of now, studies with different crowders (dextran, ficoll, PEG, etc.) demonstrated increased or reduced protein aggregation upon crowding [1, 2, 3, 4]. The variable effect on aggregation seems to be not only based on the protein that is studied, but also the properties of the crowder (charges, shape, size), the interaction of the crowder with the protein, and the mixture of crowders [5].

    Even though the relationship between crowding and protein aggregation is complex, we speculate that lower diffusion in our more crowded cells could cause protein aggregation, because these starvation conditions are known to induce the formation of protein fibrils and the condensation of mRNA and proteins.

    [1] Uversky, V.N., M. Cooper, E., Bower, K.S., Li, J., Fink, A.L., 2002. Accelerated α-synuclein fibrillation in crowded milieu. FEBS Lett. 515, 99–103. https://doi.org/10.1016/S0014-5793(02)02446-8

    [2] Munishkina, L.A., Cooper, E.M., Uversky, V.N., Fink, A.L., 2004. The effect of macromolecular crowding on protein aggregation and amyloid fibril formation. J. Mol. Recognit. 17, 456–464. https://doi.org/10.1002/jmr.699

    [3] Biswas, S., Bhadra, A., Lakhera, S., Soni, M., Panuganti, V., Jain, S., Roy, I., 2021. Molecular crowding accelerates aggregation of α-synuclein by altering its folding pathway. Eur. Biophys. J. https://doi.org/10.1007/s00249-020-01486-1

    [4] Mittal, S., Singh, L.R., 2014. Macromolecular crowding decelerates aggregation of a β-rich protein, bovine carbonic anhydrase: a case study. J. Biochem. 156, 273–282. https://doi.org/10.1093/jb/mvu039

    [5] Kuznetsova, I.M., Zaslavsky, B.Y., Breydo, L., Turoverov, K.K., Uversky, V.N., 2015. Beyond the excluded volume effects: Mechanistic complexity of the crowded milieu. Molecules 20, 1377–1409. https://doi.org/10.3390/molecules20011377

    To be more precise, we have therefore extended our Discussion section. We believe part of this additional discussion fits better in an earlier section, where we specifically discuss how the cytoplasmic properties, and specifically crowding, have been linked to filament/condensate formation. The updated paragraphs are as follows:

    [...] Additional cytoplasmic rearrangements occur upon energy depletion, including filament formation or the formation of biomolecular condensates (Narayanaswamy et al., 2009; Noree et al., 2010; Petrovska et al., 2014; Prouteau et al., 2017; Riback et al., 2017; Saad et al., 2017; Marini et al., 2020; Stoddard et al., 2020; Cereghetti et al., 2021) highlighting a broader reorganization of the cytoplasm that could further affect the diffusion of macromolecules. In turn, the amount of crowding might also influence the propensity to form condensates and filaments (Heidenreich et al., 2020). Interestingly, in vitro studies have demonstrated a complex, dual effect of crowding on protein fibrillation and aggregation, in suppressing or accelerating it (Uversky et al., 2002; Munishkina et al., 2004; Mittal and Singh, 2014; Biswas et al., 2021). This appears to be dependent not only on the protein of study, but the properties of the crowder (size, charge, shape) and the specific mixture of crowders (Kuznetsova et al., 2015). [...]

    [...] By contrast, extremely low diffusion, as seen in the absence of respiration in glucose starvation, might irreversibly impair cellular functions due to limited movement of proteins and RNA in and out of certain compartments, cellular territories and condensates. Such a model is supported by our analysis of how lower diffusion is the result of confined spaces becoming more prevalent, creating compartments that can trap macromolecules. As previously mentioned, increased crowding and reorganization of the cytoplasm have been linked to condensation and fibril formation of proteins, and, in certain in vitro contexts, accelerated aggregation. This state of crowding-induced low diffusion might therefore enhance protein aggregation or preclude the refolding of damaged proteins, which could disrupt proteostasis and lead to toxic aggregates that are a hallmark of the aging process (López-Otín et al., 2013). Together, these effects on proteins, RNA and other macromolecules likely lead to loss of cell fitness and irreversible arrest of the cells, preventing their reentry into the cell division cycle. [...]

    Reviewer #1 (Significance (Required)):

    General assessment: Strengths: It is a comprehensive study that provides a wealth of information and insight into the intricacies of a field that has received considerable attention, and its views are evolving rapidly. Weaknesses: It may suffer from some overinterpretation of diffusion data. Advance: The significant advance is that the molecular response pathway and precise molecular players are connected to the biophysical response of cells to starvation/quiescence. The dependence of diffusion on starvation has received considerable attention (Jacobs-Wagner, Cell, 2014; the current authors in eLife, 2016; and more recent investigations by Holt, Delarue, and others). Still, the authors take the next step and demonstrate how quiescence, and particularly how the history of a cell affects it, correlates strongly with the diffusion. As far as I can tell, this is new. As mentioned, the molecular insights into the pathways are exceptionally strong from my perspective. From personal experience, this work is also very important for researchers outside of the field from a practical standpoint: Do your measurements change when you stress cells by walking to a microscope? And even if you incubate them there, your measurement outcome will change. In my experience, this is a crucial point, and the cell's history is often overlooked. Audience: Broad -- biophysicists, molecular biologists, cell biologists, biotechnologists. My field of expertise: Biophysics.

    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    This manuscript addresses an important and longstanding question in the field: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under nutrient limitation and energy stress. The authors combine tools from biophysics, proteomics, stress signaling, and functional genomics to propose that stress-induced cytoplasmic reorganization, rather than ATP availability per se, is critical for long-term survival when respiration is impaired. The topic is timely, the experiments are generally well executed, and the initial phenomenology is compelling. The paper begins with a set of clear and convincing figures that establish an interesting and biologically important phenotype: when cells are shifted into glucose starvation, they can survive long term only if respiration is functional. Blocking respiration with Antimycin A (AntA) severely compromises viability. One straightforward hypothesis is that this defect simply reflects a failure to generate sufficient ATP. The authors, however, show that a 30-minute heat shock (HS) before glucose withdrawal in the presence of AntA largely rescues survival, even though cellular ATP levels remain critically low. In parallel, they use very well-executed GEM single-particle tracking experiments to demonstrate that cytoplasmic particle mobility decreases markedly in glucose-starved, respiration-deficient cells, and that this diffusion defect is also rescued by the pre-HS, again without restoring ATP. Together, these initial figures strongly support the idea that stress-induced remodeling of the cytoplasm, rather than ATP levels per se, is a key determinant of whether cells can enter and maintain a viable quiescent state. The authors then propose that this protective effect of HS is mediated by induction of the environmental stress response (ESR) and by resulting changes in protein expression. To test whether new protein synthesis is required, they pre-treat cells with cycloheximide during the HS and recovery period. This treatment largely, although not completely, abrogates the beneficial effect of HS on survival and diffusion in AntA-treated, glucose-starved cells. This is a strong experiment and supports the idea that HS-induced synthesis of specific proteins is important for protection, while also hinting that some cycloheximide-insensitive or pre-existing components may contribute. To identify the relevant proteins, the authors turn to global proteomic analysis, comparing multiple conditions: glucose starvation (SC), heat shock followed by glucose starvation (HS SC), glucose starvation plus AntA (SC + AntA), and heat shock followed by glucose starvation plus AntA (HS SC + AntA), each at 1 and 20 hours. This is where, in my view, the story becomes significantly harder to follow. The text for Figure 3 relies almost entirely on GO term enrichment, with very little description of individual proteins or even basic quantitative summaries of the dataset. For example, the authors never clearly state how many proteins were robustly quantified, nor what fraction of the proteome that represents. Without this foundational information, it is difficult to evaluate the strength and generality of their conclusions. Related to this, the GO analysis in Figure 3F reports "significant" enrichment for categories such as ribosomes or translation, yet the underlying number of proteins making up these enrichments is not shown. From the volcano plots, it appears that only a very small number of proteins change in some conditions (e.g., SC 20 h), and yet GO terms appear with extremely strong q-values. This is confusing: how can such strong enrichment occur if only a handful of proteins are changing? At minimum, the authors should provide: • the number of significantly up- or down-regulated proteins in each comparison • the number of proteins contributing to each enriched GO category • the magnitude of the changes for these proteins Because the absolute number of significantly changing proteins appears small in several conditions, the current heavy reliance on GO analysis feels unwarranted and potentially misleading. In such cases, it would likely be more informative to list all differentially abundant proteins-either in supplementary materials or in a main-text table-and briefly describe the most relevant ones, rather than relying on broad category labels. Figure 3F, in particular, needs substantially more explanation. A related issue appears in Figure 3G (and the associated text), where the authors emphasize that the proteomic response to HS + AntA and the response to long-term glucose starvation are distinct. While this conclusion is plausible, the analysis also shows a subset of proteins that are upregulated in both conditions. These overlapping proteins may, in fact, represent the core protective module that enables survival in quiescence. The authors do not discuss these proteins at all; instead, they are effectively dismissed in favor of the "distinct responses" narrative. I encourage the authors to identify and discuss these overlapping proteins explicitly. Are they chaperones, proteasome components, antioxidant enzymes, or other classical stress-response factors? Even if the global proteomes differ, the overlapping subset could be highly informative about the minimal set of proteins required to stabilize the cytoplasm and support entry into quiescence. The SATAY screen is a major strength of the paper, as it moves from correlative proteomics to functional genetic analysis. The approach appears well-controlled, but key information is missing: How many unique insertions were obtained? Was the library saturating? What was the read distribution and coverage? The authors also discuss only a small subset of the screen hits. The volcano plots show many additional genes that are not addressed. What categories do these fall into? Are they informative about pathways beyond Ras/PKA and Msn2/4? Presenting a fuller analysis would strengthen the mechanistic interpretation. The parts of the SATAY analysis that are discussed are solid. The screen implicates the Ras/PKA signaling axis and Msn2/4 in survival under HS-preconditioned, respiration-deficient starvation, and the authors validate these hits with targeted survival assays. The correspondence between genetic perturbations and changes in cytoplasmic diffusion is an intriguing connection. However, the analysis stops short of identifying the downstream effector proteins that actually produce the biophysical benefits observed. The manuscript then returns to the idea that improved cytoplasmic diffusion and reduced confinement may be essential for survival. This is an appealing hypothesis, but the evidence remains correlative. It is still unclear whether biophysical rescue is the cause of improved survival or simply a downstream marker of a properly induced stress response. What remains missing is deeper integration of the proteomics and SATAY data to identify which proteins are likely responsible for the adaptive changes in cytoplasmic organization. Overexpression of promising candidates-such as chaperones or proteostasis factors found in the overlap between HS and long-term starvation responses-could help determine whether any single protein or small group of proteins can phenocopy the HS-induced rescue. Importantly, many of the comments above are intentionally broad: the manuscript does not simply require small clarifications but would benefit from substantial expansion and deepening of the analysis. The observations are compelling, but the mechanistic chain connecting ESR activation → proteomic remodeling → cytoplasmic biophysics → survival remains insufficiently developed in the current draft. Clearer quantitative reporting, fuller presentation of the data, and more thoughtful interpretation would significantly strengthen the manuscript.

    We thank reviewer 2 for this very thoughtful evaluation of our manuscript. We agree that expanding the descriptions and analysis of the presented data will improve the manuscript. Importantly, we now provide the proteomics data and the SATAY screen in an accessible format as supplementary materials. We address the individual points below.

    Summary of Major Issues That Need to Be Addressed • Quantitative clarity in the proteomics o State how many proteins were quantified. o Report the numbers of significantly changing proteins in each condition. o Identify the proteins underlying each GO term and provide effect sizes.

    We have now included a supplemental table containing label-free protein abundances for all 3308 reproducibly quantified proteins across all nine conditions (Supplemental Table S4). In addition, we added a sentence to the main text specifying both the number of reproducibly identified proteins and the approximate coverage of the yeast proteome.

    For the comparison of protein abundances between the different stress conditions and logarithmically growing SCD cells, we now indicate the number of significantly changed proteins in the legend of Figure 3E. Furthermore, we include a heatmap of standardized protein abundances for all proteins that were significantly changed in at least one stress condition (Supplemental File S1) and provide all pairwise comparison results in the supplemental table (Supplemental Table S5). This new Supplemental File S1 replaces the previous Supplemental File S1, which had a stricter cutoff, showing all proteins with an abundance change greater than 2 standard deviations.

    The information requested by the reviewer regarding GO term analysis is indeed important and was missing in the original version. We now report, for each GO term, the number of proteins in the top or bottom 10% of differentially abundant proteins and provide the corresponding effect size, calculated as the ratio of the observed to expected hits (Figure 3F).

    • Over-reliance on GO analysis o Provide explicit lists of differentially expressed proteins. o Indicate whether enrichment results are meaningful given the small number of hits.

    We appreciate this reviewer’s comment and agree that the presentation of the proteomic data in Figure 3 relies strongly on GO term enrichment, with limited description of individual proteins. Our primary goal for the proteomic analysis was to characterize the cellular response to stress at a global level rather than to focus on individual proteins or stress-specific details. We therefore intentionally opted for a broader, more coarse-grained analysis to not overcomplicate the manuscript and maintain accessibility for a broad readership.

    That said, we agree that the underlying data should be made fully accessible. We have therefore expanded the supplemental materials to include a heatmap of all proteins that were significantly changed in at least one condition (Supplemental File S1), as well as comprehensive tables reporting protein abundances and pairwise differences across all stress conditions (Supplemental Tables S4 and S5). These additions provide direct access to the protein-level data while preserving the clarity of the main text.

    With respect to GO term analysis, to avoid overinterpretation driven by small protein sets and better comparability across different conditions, we always performed the GO enrichment based on the top and bottom 10% changed proteins. This is already stated in the legend of Figure 3F and in the Methods section. We have now added the key missing parameters of the analysis to Figure 3F (see response above). Given that the analysis identifies multiple GO terms generally associated with the environmental stress response and that these terms exhibit coordinated behavior across conditions (Figure S3A), we are confident that the conclusions drawn from this analysis are robust.

    • Overlooked overlapping proteins o Analyze and discuss the subset of proteins upregulated both by HS and by long-term starvation. o These may represent the core factors enabling survival.

    Indeed, we agree that the overlapping proteins that are observed in our Figure 3G analysis should be presented. Perhaps surprisingly, these proteins (Hxt5, Sps19, Atg8, Aim17, Put1, Fmp45, YNL194C) have diverse functions and have so far not been implemented in the environmental stress response.

    In the Results section, we now mention and briefly discuss the four that are present in both time points of the HS SC +AntA condition. We now mention all of them in the figure legend.

    The modified text from the Results section is as follows:

    [...] Furthermore, the proteins that are enriched in long-term starvation (SC 20 h vs. SCD) and those enriched in pre-HS respiration-deficient starvation (HS SC +AntA 1 h vs. SCD; HS SC +AntA 20 h vs. SCD) are poorly correlated and there is only a small overlap of factors that are significantly upregulated in all conditions (Figure 3G). These proteins are Aim17, Put1, Fmp45 and YNL194C. Aim17 is a mitochondrial protein of unknown function and Put1 is a mitochondrial proline dehydrogenase. Fmp45 and YNL194C are paralogous membrane proteins involved in cell wall organization. Focusing on the broad proteomic adaptation, we looked at the Gene Ontology (GO) terms of the proteomic changes across all conditions, and observed that long-term starvation (SC 20) leads to the upregulation of a few groups of proteins, mostly involved in respiratory activity and rewiring of the metabolism (Figure S3A). [...]

    We greatly appreciate the suggestion to do an overexpression experiment. However, the overlapping proteins are not significant hits in the SATAY, suggesting that they are individually not required for the survival rescue although their overexpression might benefit survival.

    We have therefore chosen to keep a broad perspective on the proteomics results and investigate instead the SATAY results in more detail, since they inherently contain functional relevance to survival. Overall, we feel that the overexpression of those (individually or as a group) would extend beyond the scope of our current manuscript.

    • SATAY analysis needs fuller presentation o Provide insertion numbers, coverage, and basic library statistics. o Discuss additional hits beyond the Ras/PKA/Msn2/4 pathways. o Integrate SATAY results more deeply with proteomics.

    We have added the insertion numbers and genome coverage percentages to the Methods section as follows:

    [...] SATAY Screen: Analysis and Plotting

    Sequencing detected the following total unique transposon numbers: 690’935 (A1), 558’932 (HA1), and 359’935 (HA4d) unique transposons. The transposon insertions in the different genes yielded the following genome coverages: 96.3% (A1), 94.5% (HA1) and 89.3% (HA4). For each gene [...]

    We now also provide the SATAY screen data as Supplemental Table S6.

    In the Results section, we mention some additional hits from the SATAY screen (ribosome biogenesis, mitochondrial respiration) but then shift our focus to the ESR genes. We now add a comment to the ribosome biogenesis genes before going to the ESR:

    [...] The screen revealed several highly significant gene disruptions that promote or impair the HS-mediated rescue of respiration-deficient, glucose-starved cells (Figure 4A, Supplemental Table S6). The most significant gene hits that impair survival in *4 d HS SC +AntA *when disrupted are involved in a variety of cellular processes, including ribosome biogenesis (e.g., ARX1, BUD22, RRP6), mitochondrial respiration (e.g., CBR1, COX23, ETR1), and ESR (e.g., MSN2, PSR2, YAP1). Intriguingly, the ribosome biogenesis genes being crucial for survival suggests that new ribosomes might have to be produced to ensure proper translational response during the HS. Notable among the ESR genes are MSN2 and, less significantly scored, MSN4, the master regulators of the ESR. [...]

    To deepen the discussion on the lack of overlap between the SATAY screen and the proteomics, we have added a sentence highlighting that the SATAY screen detected the main regulators of the ESR, and the proteomics revealed its downstream targets involved in proteostasis and other stress proteins, and therefore these two data sets do both point to the ESR as the crucial response behind the HS-induced rescue. The modified Discussion text is as follows:

    [...] Furthermore, the signaling genes that scored highly in the SATAY screen are often regulated through their activity rather than their abundance. Plausibly, their downstream target proteins are differentially expressed, whereas disrupting the regulators themselves leads to strong survival phenotypes. Similar observations have been made in other stress conditions, where fitness-relevant genes showed little overlap with genes with upregulated expression (Birrell et al., 2002; Giaever et al., 2002). Nonetheless, the SATAY screen revealed the principal regulators of the ESR while the proteomic analysis detected many of the ESR downstream targets involved in proteostasis and oxidative stress, demonstrating a functional convergence on the ESR in both data sets. [...]

    • Mechanistic depth remains limited o Clarify whether cytoplasmic biophysical rescue is causal or downstream. o Test whether overexpression of candidate proteins can mimic HS-induced protection. o Expand the discussion of potential mechanisms using insights from both datasets.

    Indeed, the specific mechanism(s) that govern the cytoplasmic properties in our conditions are currently not known, preventing us from manipulating the cytoplasmic properties and confirming a causal relationship. To uncover the mechanisms, extensive follow-up studies on ESR genes and/or proteins would be required, going beyond the scope of this manuscript. Furthermore, our ongoing follow-up studies are pointing towards redundancy of some potential regulation of the cytoplasmic diffusion, further complicating the analysis.

    The suggested overexpression experiment is addressed in a previous comment where the overlapping proteins are mentioned.

    Reviewer #2 (Significance (Required)):

    This manuscript addresses a fundamental and timely question in cell biology: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under conditions of nutrient depletion and compromised energy production. Although quiescence has been studied for decades, the mechanisms that link metabolic state, stress signaling, and the physical properties of the cytoplasm remain incompletely understood. This work brings together biophysical measurements, global proteomics, and unbiased genetic screening in an ambitious effort to illuminate how cells maintain viability when respiration-and thus efficient ATP generation-is disrupted. A key conceptual contribution of this study is the demonstration that ATP levels alone do not dictate survival during starvation. Rather, the ability of cells to mount an appropriate stress response and reorganize the cytoplasm appears to be crucial. The early figures provide compelling evidence that heat shock preconditioning can rescue both viability and cytoplasmic mobility in respiration-deficient cells, even when ATP remains low. This finding is notable because it challenges the widely held assumption that energy charge is the primary determinant of successful entry into quiescence. If strengthened by deeper mechanistic analysis, this insight could reshape how the field views energy stress and cellular dormancy. The identification of the Ras/PKA-Msn2/4 axis as a key regulatory node is also significant, as it connects quiescence survival to well-established nutrient and stress signaling pathways. The integration of a genome-wide SATAY screen adds functional depth and offers the potential to uncover specific downstream effectors that remodel the cytoplasm or stabilize cellular structures during prolonged stress. Finally, the manuscript touches on a concept that is gaining traction across many subfields of biology: that the biophysical state of the cytoplasm is a regulated and physiologically meaningful parameter, not merely a passive consequence of metabolic decline. Understanding how cells tune macromolecular crowding, diffusion, and spatial organization during quiescence could have broad implications beyond yeast, including in stem cell biology, microbial dormancy, cancer cell persistence, and aging. Overall, the questions addressed are important, and the study has the potential to make a meaningful conceptual contribution. However, realizing that impact will require clearer and deeper mechanistic analysis-particularly in the proteomics and SATAY sections-to convincingly identify the specific factors and pathways that mediate the cytoplasmic remodeling underlying survival.

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    Summary. Yeast haploid cells enter quiescence during nutrient deprivation, undergoing major metabolic, transcriptional and biophysical changes. In particular, quiescent cells remodel their cytoplasm, increasing macromolecular crowding and reducing diffusion. Respiration is known to be essential for entry into quiescence and long-term survival.

    In this study, the authors discovered that respiration is not intrinsically required for yeast to survive glucose-starvation-induced quiescence. In particular, they found that a short heat shock before starvation restores survival even in the absence of respiration (Antimycin A treatment), demonstrating that a stress-induced adaptation can bypass the respiratory requirement. This rescue occurs without ATP recovery and relies on de novo protein synthesis. This stress-induced adaptation also rescues quiescent-like biophysical properties of the cytoplasm (increased crowding) that are normally prevented in non-respiring cells, which are thought to be relevant for cell survival . Proteomics reveals that heat shock induces a distinct stress-response proteome enriched in proteostasis factors. A genetic screen reveals that Ras/PKA inhibition and Msn2/4 activation enable this protective reprogramming. Altogether this highlights the importance and complexity of stress adaptation to quiescence establishment.

    This is an excellent paper in all aspects. I have no major points besides the data accessibility, below.

    We thank this reviewer for this very positive evaluation.

    Main comments.

    • It would be nice to have the MS data available as Excel files for the community, and uploaded to repositories such as PRIDE. Description of the MS data is a bit expedited to serve the purpose of the paper (clustering to evaluate the similarity of proteomic profiles between conditions, GO term enrichment) so having the full data available might help.

    We agree that the MS data should be accessible. The label-free protein abundances for the reproducibly quantified proteins across all nine conditions (Supplemental Table S4) and the pairwise comparisons shown in Figure 3E (Supplemental Table S5) are now included as supplementary Excel files. The MS data is currently not on PRIDE but we will deposit it there upon publication of our manuscript.

    • Same thing for the SATAY screen. The data is summarized in Fig 4B but I believe that the data should be provided.

    We agree that the SATAY screen results should be accessible as well, and we have now included the data as Supplemental Table S6.

    Minor comments and questions. -I believe that in graphs, the X axis should start at 0 to avoid confusion about the strength of the effect (eg. Fig 2B)

    We thank reviewer 3 for pointing this out, and we have re-evaluated the axis limits of all plots. As suggested, we have adjusted the x-axis in Fig 2B to start at 0 to better highlight the strength of the effect. For our Radius of Confinement and %Confined Trajectories graphs, we believe adjusting the y-axis to start and end at the same values will allow better comparison across figures. However, we chose not to set those y-axes to start at 0, since our measurements lie in a range which is covered by these axes, and these plots would simply include blank space if set to start at 0.

    -I found that using imaging of GEMs at low frequency to reveal cytoplasmic crowding heterogeneity very interesting. Quiescent cells are known to accumulate many "bodies" as discussed in the text, would any of those co-localize with GEM foci?

    Indeed, the imaging at low frequency has revealed that fluorescently-tagged proteins might become trapped in certain regions of the cytoplasm, allowing their detection at conventional imaging frequencies. It is very likely that a similar effect occurs for other cytoplasmic “bodies”, which become visible not only through protein accumulation in a single body but also through low mobility. We have not performed any colocalization experiment with known “bodies” (such as P-bodies or stress granules). Therefore, we do not know if any stress-induced “bodies” are confined to the same spaces as GEMs. However, we would expect at best an incomplete colocalization based on the observation that glucose starvation-induced “bodies” are generally present in a higher percentage of cells than the GEM foci we observe, i.e. it is unlikely that all “bodies” overlap with a GEM focus. It might be interesting to perform such colocalization experiments in follow-up studies, but we feel that such an analysis would go beyond the current scope of this manuscript.

    Reviewer #3 (Significance (Required)):

    General assessment, advances in the field This is an excellent study. The key finding of this paper, ie. that heat shock can compensate for lack of respiration for entry into quiescence, challenges the current views on quiescence establishment. It describes an alternative program that contributes to cell viability upon C source depletion, with details on the proteomic changes occurring in this condition and some of the genetic basis of this pathway. The study is well designed and controlled, the conclusions are in line with the obtained results and very well discussed and placed in perspective. Experimentally, the authors combine several experimental approaches including live-cell single-particle tracking of GEM nanoparticles to quantify cytoplasmic diffusion, FIB-SEM ultrastructural imaging of the cytoplasm to measure macromolecular crowding, proteomics to map stress-induced protein changes and genome-wide SATAY transposon mutagenesis to identify genes required for survival in respiration-deficient cells. The limitations are: -we don't know how this stress program facilitates survival in the absence of restoration of ATP levels. The data suggest that protein homeostasis is involved (chaperones and proteasome up-regulated upon stress, reduced ribosomal and translation-associated proteins down-regulated in the absence of respiration) but the mechanism remains elusive. -the relationships between cytoplasmic crowding and quiescence establishment remain correlative. Yet, the authors provide another pathway to favour viability upon quiescence establishment (with HS) whose activation also displays an increased crowding and reduction of cytoplasmic movement, further consolidating this link. Both of these points are adequately discussed in the manuscript. None of these points should preclude publication of this study, in my opinion.

    Audience. This study would be of interest to researchers in the field of quiescence, biophysics, proteostasis, stress response, nutrient signaling and yeast biology.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary.

    Yeast haploid cells enter quiescence during nutrient deprivation, undergoing major metabolic, transcriptional and biophysical changes. In particular, quiescent cells remodel their cytoplasm, increasing macromolecular crowding and reducing diffusion. Respiration is known to be essential for entry into quiescence and long-term survival.

    In this study, the authors discovered that respiration is not intrinsically required for yeast to survive glucose-starvation-induced quiescence. In particular, they found that a short heat shock before starvation restores survival even in the absence of respiration (Antimycin A treatment), demonstrating that a stress-induced adaptation can bypass the respiratory requirement. This rescue occurs without ATP recovery and relies on de novo protein synthesis. This stress-induced adaptation also rescues quiescent-like biophysical properties of the cytoplasm (increased crowding) that are normally prevented in non-respiring cells, which are thought to be relevant for cell survival . Proteomics reveals that heat shock induces a distinct stress-response proteome enriched in proteostasis factors. A genetic screen reveals that Ras/PKA inhibition and Msn2/4 activation enable this protective reprogramming. Altogether this highlights the importance and complexity of stress adaptation to quiescence establishment.

    This is an excellent paper in all aspects. I have no major points besides the data accessibility, below.

    Main comments.

    • It would be nice to have the MS data available as Excel files for the community, and uploaded to repositories such as PRIDE. Description of the MS data is a bit expedited to serve the purpose of the paper (clustering to evaluate the similarity of proteomic profiles between conditions, GO term enrichment) so having the full data available might help.
    • Same thing for the SATAY screen. The data is summarised in Fig 4B but I believe that the data should be provided.

    Minor comments and questions.

    • I believe that in graphs, the X axis should start at 0 to avoid confusion about the strength of the effect (eg. Fig 2B)
    • I found that using imaging of GEMs at low frequency to reveal cytoplasmic crowding heterogeneity very interesting. Quiescent cells are known to accumulate many "bodies" as discussed in the text, would any of those co-localize with GEM foci?

    Significance

    General assessment, advances in the field

    This is an excellent study. The key finding of this paper, ie. that heat shock can compensate for lack of respiration for entry into quiescence, challenges the current views on quiescence establishment. It describes an alternative program that contributes to cell viability upon C source depletion, with details on the proteomic changes occurring in this condition and some of the genetic basis of this pathway. The study is well designed and controlled, the conclusions are in line with the obtained results and very well discussed and placed in perspective. Experimentally, the authors combine several experimental approaches including live-cell single-particle tracking of GEM nanoparticles to quantify cytoplasmic diffusion, FIB-SEM ultrastructural imaging of the cytoplasm to measure macromolecular crowding, proteomics to map stress-induced protein changes and genome-wide SATAY transposon mutagenesis to identify genes required for survival in respiration-deficient cells.

    The limitations are:

    • we don't know how this stress program facilitates survival in the absence of restoration of ATP levels. The data suggest that protein homeostasis is involved (chaperones and proteasome up-regulated upon stress, reduced ribosomal and translation-associated proteins down-regulated in the absence of respiration) but the mechanism remains elusive.
    • the relationships between cytoplasmic crowding and quiescence establishment remain correlative. Yet, the authors provide another pathway to favour viability upon quiescence establishment (with HS) whose activation also displays an increased crowding and reduction of cytoplasmic movement, further consolidating this link. Both of these points are adequately discussed in the manuscript. None of these points should preclude publication of this study, in my opinion.

    Audience.

    This study would be of interest to researchers in the field of quiescence, biophysics, proteostasis, stress response, nutrient signaling and yeast biology.

  3. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    This manuscript addresses an important and longstanding question in the field: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under nutrient limitation and energy stress. The authors combine tools from biophysics, proteomics, stress signaling, and functional genomics to propose that stress-induced cytoplasmic reorganization, rather than ATP availability per se, is critical for long-term survival when respiration is impaired. The topic is timely, the experiments are generally well executed, and the initial phenomenology is compelling. The paper begins with a set of clear and convincing figures that establish an interesting and biologically important phenotype: when cells are shifted into glucose starvation, they can survive long term only if respiration is functional. Blocking respiration with Antimycin A (AntA) severely compromises viability. One straightforward hypothesis is that this defect simply reflects a failure to generate sufficient ATP. The authors, however, show that a 30-minute heat shock (HS) before glucose withdrawal in the presence of AntA largely rescues survival, even though cellular ATP levels remain critically low. In parallel, they use very well-executed GEM single-particle tracking experiments to demonstrate that cytoplasmic particle mobility decreases markedly in glucose-starved, respiration-deficient cells, and that this diffusion defect is also rescued by the pre-HS, again without restoring ATP. Together, these initial figures strongly support the idea that stress-induced remodeling of the cytoplasm, rather than ATP levels per se, is a key determinant of whether cells can enter and maintain a viable quiescent state. The authors then propose that this protective effect of HS is mediated by induction of the environmental stress response (ESR) and by resulting changes in protein expression. To test whether new protein synthesis is required, they pre-treat cells with cycloheximide during the HS and recovery period. This treatment largely, although not completely, abrogates the beneficial effect of HS on survival and diffusion in AntA-treated, glucose-starved cells. This is a strong experiment and supports the idea that HS-induced synthesis of specific proteins is important for protection, while also hinting that some cycloheximide-insensitive or pre-existing components may contribute. To identify the relevant proteins, the authors turn to global proteomic analysis, comparing multiple conditions: glucose starvation (SC), heat shock followed by glucose starvation (HS SC), glucose starvation plus AntA (SC + AntA), and heat shock followed by glucose starvation plus AntA (HS SC + AntA), each at 1 and 20 hours. This is where, in my view, the story becomes significantly harder to follow. The text for Figure 3 relies almost entirely on GO term enrichment, with very little description of individual proteins or even basic quantitative summaries of the dataset. For example, the authors never clearly state how many proteins were robustly quantified, nor what fraction of the proteome that represents. Without this foundational information, it is difficult to evaluate the strength and generality of their conclusions.

    Related to this, the GO analysis in Figure 3F reports "significant" enrichment for categories such as ribosomes or translation, yet the underlying number of proteins making up these enrichments is not shown. From the volcano plots, it appears that only a very small number of proteins change in some conditions (e.g., SC 20 h), and yet GO terms appear with extremely strong q-values. This is confusing: how can such strong enrichment occur if only a handful of proteins are changing? At minimum, the authors should provide:

    • the number of significantly up- or down-regulated proteins in each comparison
    • the number of proteins contributing to each enriched GO category
    • the magnitude of the changes for these proteins

    Because the absolute number of significantly changing proteins appears small in several conditions, the current heavy reliance on GO analysis feels unwarranted and potentially misleading. In such cases, it would likely be more informative to list all differentially abundant proteins-either in supplementary materials or in a main-text table-and briefly describe the most relevant ones, rather than relying on broad category labels. Figure 3F, in particular, needs substantially more explanation. A related issue appears in Figure 3G (and the associated text), where the authors emphasize that the proteomic response to HS + AntA and the response to long-term glucose starvation are distinct. While this conclusion is plausible, the analysis also shows a subset of proteins that are upregulated in both conditions. These overlapping proteins may, in fact, represent the core protective module that enables survival in quiescence. The authors do not discuss these proteins at all; instead, they are effectively dismissed in favor of the "distinct responses" narrative. I encourage the authors to identify and discuss these overlapping proteins explicitly. Are they chaperones, proteasome components, antioxidant enzymes, or other classical stress-response factors? Even if the global proteomes differ, the overlapping subset could be highly informative about the minimal set of proteins required to stabilize the cytoplasm and support entry into quiescence. The SATAY screen is a major strength of the paper, as it moves from correlative proteomics to functional genetic analysis. The approach appears well-controlled, but key information is missing: How many unique insertions were obtained? Was the library saturating? What was the read distribution and coverage? The authors also discuss only a small subset of the screen hits. The volcano plots show many additional genes that are not addressed. What categories do these fall into? Are they informative about pathways beyond Ras/PKA and Msn2/4? Presenting a fuller analysis would strengthen the mechanistic interpretation. The parts of the SATAY analysis that are discussed are solid. The screen implicates the Ras/PKA signaling axis and Msn2/4 in survival under HS-preconditioned, respiration-deficient starvation, and the authors validate these hits with targeted survival assays. The correspondence between genetic perturbations and changes in cytoplasmic diffusion is an intriguing connection. However, the analysis stops short of identifying the downstream effector proteins that actually produce the biophysical benefits observed. The manuscript then returns to the idea that improved cytoplasmic diffusion and reduced confinement may be essential for survival. This is an appealing hypothesis, but the evidence remains correlative. It is still unclear whether biophysical rescue is the cause of improved survival or simply a downstream marker of a properly induced stress response. What remains missing is deeper integration of the proteomics and SATAY data to identify which proteins are likely responsible for the adaptive changes in cytoplasmic organization. Overexpression of promising candidates-such as chaperones or proteostasis factors found in the overlap between HS and long-term starvation responses-could help determine whether any single protein or small group of proteins can phenocopy the HS-induced rescue. Importantly, many of the comments above are intentionally broad: the manuscript does not simply require small clarifications but would benefit from substantial expansion and deepening of the analysis. The observations are compelling, but the mechanistic chain connecting ESR activation → proteomic remodeling → cytoplasmic biophysics → survival remains insufficiently developed in the current draft. Clearer quantitative reporting, fuller presentation of the data, and more thoughtful interpretation would significantly strengthen the manuscript.

    Summary of Major Issues That Need to Be Addressed

    Quantitative clarity in the proteomics

    • State how many proteins were quantified.
    • Report the numbers of significantly changing proteins in each condition.
    • Identify the proteins underlying each GO term and provide effect sizes.

    Over-reliance on GO analysis

    • Provide explicit lists of differentially expressed proteins.
    • Indicate whether enrichment results are meaningful given the small number of hits.

    Overlooked overlapping proteins

    • Analyze and discuss the subset of proteins upregulated both by HS and by long-term starvation.
    • These may represent the core factors enabling survival.

    SATAY analysis needs fuller presentation

    • Provide insertion numbers, coverage, and basic library statistics.
    • Discuss additional hits beyond the Ras/PKA/Msn2/4 pathways.
    • Integrate SATAY results more deeply with proteomics.

    Mechanistic depth remains limited

    • Clarify whether cytoplasmic biophysical rescue is causal or downstream.
    • Test whether overexpression of candidate proteins can mimic HS-induced protection.
    • Expand the discussion of potential mechanisms using insights from both datasets.

    Significance

    This manuscript addresses a fundamental and timely question in cell biology: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under conditions of nutrient depletion and compromised energy production. Although quiescence has been studied for decades, the mechanisms that link metabolic state, stress signaling, and the physical properties of the cytoplasm remain incompletely understood. This work brings together biophysical measurements, global proteomics, and unbiased genetic screening in an ambitious effort to illuminate how cells maintain viability when respiration-and thus efficient ATP generation-is disrupted. A key conceptual contribution of this study is the demonstration that ATP levels alone do not dictate survival during starvation. Rather, the ability of cells to mount an appropriate stress response and reorganize the cytoplasm appears to be crucial. The early figures provide compelling evidence that heat shock preconditioning can rescue both viability and cytoplasmic mobility in respiration-deficient cells, even when ATP remains low. This finding is notable because it challenges the widely held assumption that energy charge is the primary determinant of successful entry into quiescence. If strengthened by deeper mechanistic analysis, this insight could reshape how the field views energy stress and cellular dormancy.

    The identification of the Ras/PKA-Msn2/4 axis as a key regulatory node is also significant, as it connects quiescence survival to well-established nutrient and stress signaling pathways. The integration of a genome-wide SATAY screen adds functional depth and offers the potential to uncover specific downstream effectors that remodel the cytoplasm or stabilize cellular structures during prolonged stress. Finally, the manuscript touches on a concept that is gaining traction across many subfields of biology: that the biophysical state of the cytoplasm is a regulated and physiologically meaningful parameter, not merely a passive consequence of metabolic decline. Understanding how cells tune macromolecular crowding, diffusion, and spatial organization during quiescence could have broad implications beyond yeast, including in stem cell biology, microbial dormancy, cancer cell persistence, and aging.

    Overall, the questions addressed are important, and the study has the potential to make a meaningful conceptual contribution. However, realizing that impact will require clearer and deeper mechanistic analysis-particularly in the proteomics and SATAY sections-to convincingly identify the specific factors and pathways that mediate the cytoplasmic remodeling underlying survival.

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    Referee #1

    Evidence, reproducibility and clarity

    In the present manuscript, the authors present an in-depth study on the effect of a heat-shock response on the ability of yeast to regain viability after quiescence when their ability to respire is inhibited. They nicely demonstrate that these effects correlate with the measured diffusion coefficients, providing deeper insight into the (at least partially) responsible environmental stress response and the molecular players involved. This work is an important contribution to the growing (or resurging) field of the physical properties of the cell.

    My two main comments are the following:

    • The authors determine the diffusion coefficients from the MSD, as well as further analyze them all the way up to the confinement size. As far as I can judge from the manuscript, these analyses are for 2D systems and were initially developed for processes on membranes. How does this change for 3D systems? I understand that for a straightforward qualitative comparison of apparent MSD, this assumption is acceptable, but it may deviate more strongly with the additional analyses the authors present.
    • The authors show data in the supporting information where the GEMs provide larger foci after stress with longer imaging times. Could the authors provide the images of the shorter imaging times that they use? That seems a more equal comparison than Figure C. It is also unclear to me why fixed cells are used in Figure C, as well as the meaning of the x-axis. In line with this, can the authors exclude that GEMs dimerize/oligomerize after stress, and therefore display a lower diffusion coefficient?

    Other comments:

    • For the precision of the language, the authors equate ribosome content with macromolecular crowding, with the diffusion of the GEMs throughout, and this becomes more conflated in the discussion, where it is compared to viscosity and macromolecular crowding effects, e.g., translation. Is it macromolecular crowding, mesoscale crowding, nano-rheology, or ribosome crowding? What is measured precisely?
    • In the EM images, the ribosomes seem smaller after starvation. Is that correct, and how should we interpret this? Is this due to an increased number of monosomes?
    • The authors refer to recent work on how biochemical reactions, such as translation, are determined by the cytoplasm. There is some older work on this, see for example in bacteria https://doi.org/10.1073/pnas.1310377110, and also in vitro here DOI: 10.1021/acssynbio.0c00330
    • On the section of correlating diffusion and survival outcomes (bottom page 12), it is mentioned that the lowered diffusion could enhance aggregation. However, literature indicates that the opposite is true in buffer; lower diffusion reduces aggregation (also nucleation is inversely proportional to the viscosity).

    Significance

    General assessment:

    Strengths: It is a comprehensive study that provides a wealth of information and insight into the intricacies of a field that has received considerable attention, and its views are evolving rapidly.

    Weaknesses: It may suffer from some overinterpretation of diffusion data.

    Advance:

    The significant advance is that the molecular response pathway and precise molecular players are connected to the biophysical response of cells to starvation/quiescence. The dependence of diffusion on starvation has received considerable attention (Jacobs-Wagner, Cell, 2014; the current authors in eLife, 2016; and more recent investigations by Holt, Delarue, and others). Still, the authors take the next step and demonstrate how quiescence, and particularly how the history of a cell affects it, correlates strongly with the diffusion. As far as I can tell, this is new. As mentioned, the molecular insights into the pathways are exceptionally strong from my perspective. From personal experience, this work is also very important for researchers outside of the field from a practical standpoint: Do your measurements change when you stress cells by walking to a microscope? And even if you incubate them there, your measurement outcome will change. In my experience, this is a crucial point, and the cell's history is often overlooked.

    Audience:

    Broad -- biophysicists, molecular biologists, cell biologists, biotechnologists.

    My field of expertise: Biophysics.

  5. Version published to 10.1101/2025.09.30.679535 on bioRxiv