Author response:
General Statements
We thank the four reviewers for their careful reading of the manuscript and for their constructive and insightful comments. Below, we outline the revisions we have already started or plan to make shortly in response to the reviewers' specific comments.
Description of the planned revisions
Reviewer #1:
In general, the manuscript is well structured and written, with carefully designed figures. The authors are also generally aware of the limitations of the work. The manuscript is interesting and valuable, but some interpretations should be moderated.
Major Comments:
(1) The data presented provide in general good support for majority of conclusions presented in the manuscript. However, less support is provided for the theories that: there is a universal "core expression signature" of dormancy; the mRNA/ribosome protein uncoupling reflects a specific mechanism of preparation for exiting dormancy; and the observed changes provide evidence for an epigenetic "memory" of dormancy. These elements are currently more interpretive than evidentiary. I would recommend softening the language in these places and clearly labeling them as hypothetical.
We agree, of course, that the core expression signatures determined for yeast, killifish and human may not extend to all other dormant states (and we do not call it universal in the manuscript). Also, the mechanistic nature of the ‘memory’ of dormancy is not known, and we have merely speculated on the possibility of an epigenetic memory for discussion, based on the known causes of similar phenomena. Similarly, we discussed plausible processes underlying the mRNA/ribosome protein uncoupling. We will make it clearer in the revised manuscript that these are only hypotheses at this point.
(2) The authors propose that spores and post-dormancy cells retain a memory of time spent in dormancy or stress exposure. However, sporulation is also generally expected to reset or remove accumulated damage. Please clarify whether the observed long-term effects reflect classical "memory" of the cell, persistent regulatory state, or selection of cells with different fitness (carry-over effects). I would be careful in interpreting the effects as a form of memory associated mostly with epigenetic control - the statement seems stronger than what the data directly demonstrate
Our experiments focus on spores (post-sporulation) and their offspring cells, so these findings have no bearing on any reset that may occur during earlier sporulation. It is extremely unlikely that the observed ‘memory’ effects reflect the selection of cells with different fitness, because we started with genetically identical cells, findings were repeated independent biological replicates, and our conditions (stress and prolonged time) maintained the viability of most cells. However, we certainly agree that our results do not establish a mechanism for the observed post-dormancy ‘memory’ effects. Again, we merely discussed the plausible possibility of an epigenetic memory and will provide more nuanced interpretations in the revised manuscript.
(3) Validation of a small set of top hits by independent assays would substantially strengthen the conclusions. For example, targeted qPCR and/or biochemical assays for key genes (like ribosomal and autophagy) or pathways would help confirm the most important transcriptomic and proteomic trends. Without this, the suggestion of, for example, translational regulation remains possible but unproven
For transcriptome and proteome studies using current, reliable methods, transcripts or proteins from specific genes are not typically validated, especially when studies focus on global patterns and functional enrichments rather than on any specific genes. General induction of autophagy-related genes at both the RNA and protein levels is unlikely to occur by chance, and autophagy genes have previously been shown to be induced under similar conditions, including in the transcriptomes of killifish diapause embryos (see Fig. S2H). However, we agree that validation of the unexpected regulation of ribosomal genes in diapause embryos would strengthen this conclusion. In spores, the effect is weaker and depends on the control sample used. We have begun qPCR and Western validation of ribosomal protein gene expression in killifish embryos to independently confirm the antagonistic changes in relative expression at the RNA and protein levels. We will incorporate these results in the revised manuscript.
(4) Overall, the study appears reproducible, with detailed methods, biological replicates, and appropriate statistical analyses. However, reproducibility could be improved by more clearly describing the limitations of the comparisons (especially with the published datasets) and the data-processing parameters, particularly for correlation and functional enrichment analyses. Some comparative conclusions are based on data from different studies, under different conditions and often with different depths of coverage. It is not always clear to what extent interspecies comparisons are fully methodologically similar
We used two strategies to minimise the limitations of comparing our RNA-seq data with a published RNA-seq dataset (human dormant cancer cells): first, we considered only genes that were detected in all datasets being compared, so that any differences in sequencing depth would not bias the comparison; second, we used relative expression values normalized to their respective controls instead of raw values, using identical cutoffs for all studies (fold-change >1.5x; FDR <0.05), to make the cross-study comparisons more consistent. We will further clarify this and its limitations in the Methods and Results. Notably, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in the analyses. A strength of our study is that the RNA-seq datasets used to compare killifish and yeast were both generated in our laboratory under standardised conditions and processed through the same bioinformatics pipeline, thus enhancing comparability.
Minor Comments:
(1) Because the Bar-seq assay measures barcode abundance only after germination and regrowth, the phenotype may reflect not only spore survival but also germination efficiency and subsequent growth. Please discuss how the authors controlled for selection during regrowth and whether new mutations, clonal selection, or differences in ploidy could contribute to the observed effects.
We highlight in the manuscript that “Bar-seq quantification of deletion mutants was carried out after spore germination and regrowth. Therefore, the abundance of each barcode is a product of the combined influence of spore survival, germination efficiency and growth, which may affect stress- or lifespan-associated phenotypes for some mutants, a compromise necessary to prevent barcodes of dead spores from being sequenced.” To partially correct for this effect, we used conservative cutoffs and normalised all barcode abundances to the 2-week reference timepoint, so mutants that were already depleted early on due to poor germination or growth should not confound the longevity analysis. Similarly, the heat-shocked samples were compared to unstressed control samples, and any differences between the two samples should therefore reflect the effect of heat stress. All timepoints and pools were grown under consistent, uniform outgrowth conditions. We used two independent biological replicates and pooled samples, so any effects arising from extremely rare events, such as new mutations or diploidization, should be negligible. Moreover, our genome-wide screens focus on global functional enrichments rather than any specific mutants. We will describe this strategy in more detail in the Methods section.
(2) Please clarify whether spores were analyzed as pooled spores or after tetrad dissection, and how the authors ensured that colonies originated from single haploid spores rather than mixed events. Since ploidy can affect growth and lifespan, please indicate whether ploidy was checked in the recovered colonies or whether the experimental design excludes diploid formation during germination/regrowth.
As indicated in the scheme in Fig. 1A, meiosis was triggered by crossing a haploid h+ with a haploid h- colony to generate diploid cells that immediately sporulate, using a colonypicking robot. All cells that do not form haploid spores after this step are then killed by heat. This is the same principle as applied for genetic interaction screens using synthetic genetic arrays (SGA). Thus, the haploid spores in each colony resulted from a cross between cells of different mating types carrying an identical deletion mutant. For both the Bar-seq screen and expressionbased spore analyses, spores were analysed as pooled populations without tetrad dissection. Our results refer to the population rather than the single-cell level. Unlike budding yeast, fission yeast cells do not normally grow as diploids and will not mate in the medium used for germination and regrowth. Naturally occurring, vegetatively growing diploid cells in fission yeast are rare and unstable; they would be at an equal growth disadvantage during regrowth from all conditions tested, thus minimising the possibility of a systematic bias caused by diploidy. Accordingly, we did not directly check for ploidy. We will describe this experimental design in greater detail in the Methods.
(3) The cross-species GO enrichment analyses may be affected by differences in annotation quality and gene coverage, especially between yeast and killifish. Please clarify how gene duplication, paralogs and annotation redundancy were handled and whether the conclusions remain robust after accounting for GO term similarity or annotation bias.
Response: The handling of orthology, paralogs, and annotation differences between species is described in the Methods. Importantly, yeast and killifish genes, and their GO terms, were not directly compared but via their corresponding human orthologs and GO associations, as these mappings are most reliable and comprehensive. Orthology relationships between fission yeast and humans were obtained from a manually curated list in PomBase (Wood et al. 2019), compiled from various sources. In some cases, the consensus ortholog from the major ortholog predictors (Compara, Inparanoid, OrthoMCL) is used. Distant orthologs have also been identified by PSI-BLAST matches. Other ortholog predictions come from experimental data demonstrating functional correspondence or involving membership of corresponding complexes. These predictions are aligned and submitted to Pfam before inclusion. PomBase’s approach ensures that the breadth of coverage exceeds that of any individual prediction method and includes many ortholog calls that are not detected by any automated method. Killifish-to-human orthology was inferred from BLASTp-based mapping to the human UniProt reference proteome, with additional hidden orthologs identified using the spotted gar proteome (Kelmer Sacramento et al. 2020). Spotted gar is a fish whose lineage diverged from teleosts before their genome duplication, and it was therefore used as a bridge to identify hidden orthologs between teleosts and mammals. All paralogs were included to ensure comprehensive coverage, although some may have specialised roles.
Thus, all genes were mapped to their human orthologs and their GO associations, and genes commonly regulated were identified by filtering for significantly induced or repressed genes across all models, as indicated. This procedure circumvents the bias in GO term annotation between yeast and killifish. Naturally, given the relatively poor annotation of the killifish genome, such comparative analyses may miss some orthologs. Nevertheless, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in annotation quality or gene coverage. We will highlight this information more clearly in the revised manuscript.
(4) Please discuss whether the age of the mother cells could influence spore quality, lifespan, or stress resilience. Did the authors control for mother-cell age? I would like to know about a possible impact on spore quality and subsequent lifespan.
Spores were always produced from freshly grown cells derived from frozen stocks, thereby controlling for any effects of cell age on spore quality and lifespan. Moreover, unlike budding yeast, fission yeast cells grow by symmetrical division, producing two daughter cells of equal size, so the concepts of mother cells and replicative ageing do not apply.
(5) It may be useful to briefly note how dormancy in plants differs from the fungal and animal systems studied here or to clarify that the present conclusions are limited to the models analyzed in this manuscript.
We agree that this is an interesting topic, e.g. plant seeds. However, given that dormancy is an understudied cell state, there is not enough solid information to make meaningful comparisons at this point. Moreover, any comparative review of what is known in plants is beyond the scope of our study. In the revised manuscript, we will clarify that our findings are naturally limited to the fungal and animal cells analysed in our study.
(6) Please briefly justify the choice of heat shock as the acute stress condition. It would also be helpful to comment on whether oxidative stress (for example, by incubation with H2O2) might reveal similar or distinct aspects of dormant-cell resilience.
Heat shock is a well-established stressor for yeast that elicits a conserved stress response and, most critically, avoids the spore wall permeability problem that complicates the interpretation of trials with chemical stressors. We have actually tried to stress spores with H2O2 at different doses, treatment times, and cell densities; under all tested conditions, spores maintained high viability similar to untreated spores, possibly due to their strong catalase activity (spore suspensions foamed after adding H2O2). Therefore, oxidative stress is not a suitable stressor for the resilient spores. In the revised manuscript, we will show these data in a supplement to justify the use of heat shock as an acute stress condition.
Reviewer #2:
This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy. This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.
Major Comments:
(1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?
We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of this work. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.
(2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?
We conducted these analyses and did not observe any positive correlation between post-dormancy gene expression changes in yeast offspring from old spores and gene expression changes in stationary-phase ageing cells; if anything, the gene expression signatures showed a slight inverse correlation (R = -0.05; p = 9e-04). For killifish, postdormancy expression changes in embryos from late diapause showed no significant correlations with age-related expression changes in adult brain (R = −0.04, p = 0.84), liver (R = −0.03, p = 0.39), or skin (R = −0.06, p = 0.19). Changes in expression in killifish late diapause compared to early diapause showed no significant correlation with age-related changes in expression in adult killifish liver (R = −0.008, p = 0.53), but did show a weak correlation with old skin (R = 0.18 , p <2e−16), specifically repression of collagen and extracellular matrix genes. We will include the latter analyses in the revised manuscript as supplemental panels.
(3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.
To address this issue, we re-ran the correlation analyses shown in Figs 1C, 4F and 5F using independent replicates for the shared condition (split-replicate analyses). For example, in Fig 4F, replicate A of the 2-week unstressed spores was used for the old-versus-young comparison, while replicate B was used for the heat-shock-versus-control comparison. For Fig 1C, using independent batches, the correlations remained significant (Batch A vs B: R = −0.16, p = 4.e−16; Batch B vs A: R = −0.27, p <2e−16). For Fig 4F, using independent replicates of the unstressed spores, the correlations remained significant across all replicate pairs (Rep 1 vs Rep 2: R = 0.18, p <2e−16; Rep 2 vs Rep 3: R = 0.14, p = 3e−12; Rep 2 vs Rep 1: R = 0.23, p <2e−16). For Fig 5F, the correlations were also robust across independent pairs (Pair 1: R = 0.15, p = 4e−10; Pair 2: R = 0.13, p = 3e−08; Pair 3: R = 0.16, p = 2e−11). These results show that the correlations are not an artefact of shared samples.
Minor Comments:
(4) In Figure 1, it could be informative to present data from intermediate timepoints (i.e., 1, 2, and 5 months). Do mutants that affect spore longevity show a progressive enrichment or depletion over time in dormancy, as depicted in the schematic in Figure 1A? Are there differences in kinetics across the different mutants?
We thank the reviewer for this suggestion. We will add line plots of the logCPM abundance of short-lived and long-lived mutants (relative to 2-week spore mutants) for all timepoints (2 weeks, 1, 2, 5 and 6 months) to Figure 1. The plots show that most short-lived mutants exhibit progressive depletion over time, while most long-lived mutants exhibit progressive enrichment.
(5) The authors focus on shared gene expression changes in dormancy between yeast spores, killifish diapause embryos, and dormant cancer cells. Are any pathways enriched among yeast-, killifish-, or human-specific changes?
We focused on pathways shared across species, but we have now also examined pathways enriched among genes that change specifically in each organism. Among the species-specific gene sets, only genes repressed in yeast spores were significantly enriched, with cytoplasmic translation being the most enriched process (p = 4e-11). The killifish- and human-specific gene sets were not significantly enriched for any functional terms. These results will be included as a supplementary figure.
Reviewer #3:
In this study, the authors use several genome-wide approaches to find commonalities and differences between distinct dormancy models, including S. pombe spores, killifish diapause embryos, and human dormant cancer cells. As G0 states are ubiquitous in nature, yet still understudied, this study provides not only a clear demonstration of an evolutionarily-conserved component in G0 regulation, but also a rich resource of genomics datasets, that will be especially of interest to S. pombe and killifish researchers.
The main conclusions of the study are that: (1) there are commonalities in dormancy programs across eukaryotic evolution; (2) the uncoupling between transcriptome and translatome is more pronounced in non-dividing cells, particularly at ribosomal protein coding genes; and (3) dormant cells retain dynamic responses to stress, changing their cellular state and maintaining this difference.
Overall, the study is clearly written, provides large data resources for future research in the dormancy field, and the conclusions are supported by the presented evidence (although point 3 would require further experimental clarification, see below).
Major Comments:
A key consideration when comparing transcriptomes of cycling and dormant cells is the overall RNA quantity in the cells; however, it is not made clear in the manuscript whether this overall level was compared and/or if RNA-seq used spike-in quantification. However, this will directly affect some conclusions; for example there is a conceptual difference between a transcript upregulated in spores and a transcript that appears upregulated in spores but is in fact 'less repressed than average' (resulting in higher FC). While the overall transcript diversity as described will be not affected, this analysis can provide important context. For the highlighted example of ribosomal protein mRNAs, this scenario could be what resulted in an apparent mRNA increase but protein decrease (which would be interpreted as both mRNA stabilization and translation inhibition); if the total mRNA is much lower, then the mRNA levels may be similar (which would then be interpreted as no change in transcription nor mRNA stability, but highlight the importance of translation inhibition). This point was more clearly described in a previous seminal study by the authors
(Marguerat et al 2012), and so should be made clearer here as well. What is the overall RNA level in cycling cells compared to quiescent cells, and compared to spores? Likewise, what is the overall reduction in total protein levels? Was the overall reduction comparable to the previous study? These overall levels should at least be quantified, which is a straightforward experiment. The current mention of this limitation (two sentences in the last paragraph of the conclusion) is not enough.
Accordingly, the term "induced" (in Fig 2 for example) may be misleading, and could be separated into truly "induced" genes and "relatively induced" genes, when normalized to absolute levels. Likewise, Fig S2A and S2I are misleading due to this effect (normalization will tend to show a similar number of up-regulated and down-regulated genes). These panels should be clarified by plotting next to the relative quantification (current panels) a comparison taking overall RNA level into account. This will very likely strongly increase the number of downregulated genes and decrease the number of upregulated genes, and the new lists can also be subjected to GO analysis.
The same issue would be important to discuss for the killifish experiment as well.
We acknowledge that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells are important considerations. As described in the Results and Methods, our study presents relative expression changes between dormant and proliferating cells. We highlight this limitation in the Conclusions: “As is the case for most expression studies, our transcriptome and proteome data reflect relative expression levels between two conditions. It is likely that the absolute cellular numbers of most transcripts and proteins are reduced in dormant cells, as they are in quiescent S. pombe cells (Marguerat et al. 2012). Thus, genes induced in dormant cells relative to active cells might be repressed in absolute levels, but to a lesser extent than most other genes.” We also make it clear in the concluding sentences for different sections that our data refer to relative changes (e.g., “Accordingly, several transcripts and proteins were differentially expressed in offspring from 5month-old spores relative to offspring from 2-week-old spores, including 199 mRNAs, 223 proteins, and 33 long non-coding RNAs”). We will carefully review the manuscript, including the Abstract and figures, to further clarify this point, e.g., by using "relatively induced" or “higher relative abundance”.
We would like to note, however, that relative quantification, normalised to average expression, focusing on genes that become more or less enriched relative to average genes in the condition of interest compared to a control condition, is a widely accepted and biologically meaningful approach for identifying expression signatures that specify the condition of interest. This is also the principle for qPCR and western analyses, where housekeeping gene normalisation is common. If the absolute expression of all or most genes decreases in dormant cells (which is likely), it would not be meaningful to determine functional enrichments among these genes. Relative expression data help distinguish groups of genes that increase or decrease more than most other genes, reflecting cellular regulation and functional signatures.
However, we fully agree that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells offer a complementary perspective. Therefore, we are quantifying the overall RNA and protein levels in yeast spores compared to proliferating cells as well as in killifish diapause embryos compared to actively developing embryos. If successful, these results will provide useful context for the relative expression data and will be included in the revised manuscript.
For spore offspring stress experiments, the estimated number of cell divisions after spore germination should be indicated. It appears over 20, which would confirm the authors' conclusion that an epigenetic state is induced and persists over multiple rounds of replication and underlie improved heat resistance, indeed hinting at transcriptional memory through chromatin modification. As the phenotype is tested at a population-level, it is unclear whether every spore is equally able to impart this phenotype, and for how long the stress resistance response persists. Moreover, it may be clearer to test heat resistance in log-phase cells, as in the current experimental setup cells are grown to stationary-phase before plating; however, stationary-phase cultures are more heterogenous and more heatresistant.
We agree, and several of these issues are already addressed in the manuscript. First, regarding the number of cell divisions, the offspring cells were obtained by germinating spores in rich medium and allowing them to grow to mid-exponential phase for approximately 6 generations, as described in the Results (p. 24). Second, for the log-phase testing, we state in the Methods (p. 43) that cultures were adjusted to OD600 = 0.2 and grown to OD600 = 0.6 before stress treatment and plating. Thus, all assays were in fact already performed on log-phase cells.
We will further emphasise these points in the revised manuscript.
Yes, our experiments were conducted at the cell population level, and we therefore cannot distinguish whether there is uniform transmission of the transcriptional and phenotypic legacy of dormancy across all germinating spores or whether single cells show heterogeneity within the population in this respect. While this is an interesting question, it would be too technically challenging and time-consuming to pursue this project within the framework of the current study.
Note also that using 3-fold serial dilutions (instead of more commonly used 10-fold) means that the differences shown in Fig 4M are mild. Plating duplicates (at log-phase) and imaging at several times of growth would be necessary to have a clearer view of the result.
We fully agree that the serial dilution assay for stress resistance should be improved given the subtle differences. We are repeating the spot assay with 10-fold serial dilutions as suggested, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result.
Minor Comments:
One point for clarification in the introduction is that there is a scale difference between cellular dormancy (at the level of a cell) and organismal dormancy such as diapause (in which the dormancy qualifies the whole organism; typically this will involve slower metabolism, but not necessarily the arrest of the cell cycle for every cell in the organism). As diapause can take different forms depending on organism, detailing this process for the turquoise killifish model may clarify the phenotypic commonalities between these models. Indeed, as pointed out by the authors, several mutants show opposite effects for stationary phase viability and for dormant spore viability, highlighting the diversity of G0 states.
We agree that there may be a difference between cellular dormancy, in which individual cells arrest their cell cycle, and organismal dormancy, such as diapause, in which individual cells may differ in cell-cycle arrest. However, it has been shown in killifish that all cells throughout the diapause embryo exit the cell cycle and become quiescent (Dolfi et al. 2019), as may be expected given that these embryos can remain dormant for up to two years. We will highlight this aspect more clearly in the Introduction.
The title of the manuscript may gain from also mentioning some of the insights (instead of "Gene function and expression of X and Y", "Gene function and expression in X and Y shows Z").
We agree and will change the title to “Gene function and expression profiling in yeast spores, killifish diapause embryos, and their offspring cells reveal common regulatory features of dormancy
The Venn diagram in Fig 1D may not be the best way to represent the data; by definition, multiple overlaps here will be zero (between "long-lived spores" and "short-lived spores" and between "long-lived cells" and "short-lived cells"). A table or an upset plot may be useful there. Note that the gained space in Fig 1 may be used to provide examples of mutants in each GO category.
We agree and will replace the Venn diagram in Figure 1D with an UpSet plot, which better represents the overlapping and non-overlapping categories.
Reviewer #4:
Specific Comments:
There is a dissonance between the apparent upregulation of autophagy genes during dormancy in both systems and the observation that loss of autophagy genes enhance dormancy. While the authors brush this off by noting "that genes regulated in a given cellular state often do not overlap with those functionally required for that state" (an argument that undermines their whole analysis), it raises the possibility that the process signatures they report are not, in fact, functionally-linked to dormancy and could be coincidental. What would make the expression analysis more convincing would be functional data showing that at least one of the upregulated genes in one of the systems is important for dormancy.
We respectfully disagree with this assessment. Please note that this apparent paradox between autophagy gene regulation and spore phenotype is highlighted and discussed in the manuscript, and our data actually confirm the importance of autophagy in dormant spores when considered carefully. It is not correct to say that ‘loss of autophagy genes enhance dormancy’. Although autophagy mutants show increased lifespan, they also show decreased stress resiliency. Stress resilience is a key feature and purpose of dormancy, so the loss of autophagy function would clearly be a problem for dormant cells, consistent with the induction of autophagy genes in spores. The surprising trade-off between processes (including autophagy) that support spore longevity and those that support stress resilience is a key finding of our study.
Regarding the request for functional data linking at least one upregulated gene to dormancy, we note that we have already provided this at a scale far beyond a single gene. In fact, we identified as many as 600 genes that are differentially expressed in spores and, when deleted, cause spore phenotypes (altered stress resilience and/or lifespan). Moreover, the processes significantly enriched among these 600 genes, including protein translation, ribosomes, TCA cycle, and autophagy, are the same processes most enriched among the differentially expressed genes. This significant process-level convergence between functional and expression data provides compelling evidence that our expression signatures are not coincidental but largely reflect roles in dormancy.
It is not clear how the bar-coding experiment was controlled for mutants that make defective spores (eg poor spore walls) rather than being required for dormancy per se. As the authors note, several of the mutants with the strongest effects (shortening lifespan of the spores) have been previously identified in screens for sporulation or spore wall defects, consistent with those genes playing a specific role in proper spore assembly rather than a general function in dormancy.
The experimental design was carefully set up to avoid the contribution of mutants that do not sporulate or fail to produce mature spores. After sporulation, samples were heatshocked at 43°C for 3 days and stored in water for at least 2 weeks prior to starting the longevity assay. These conditions will kill any remaining vegetative cells; hence, only spores that can withstand these treatments will be included in the barcode measurements. All abundance values were also normalised to the abundance of each mutant that regrew after two weeks of spore ageing, so any differences in abundance should be caused by our conditions of interest. Thus, mutants with sporulation defects or that fail to form viable spores would be substantially depleted before this normalisation point and would therefore not be included in the subsequent analysis; if they were included, they should not show different abundances across our postsporulation conditions. Nevertheless, we cannot rule out that some of the short-lived mutants reflect problems during sporulation rather than spore traits, as suggested by overlaps with the published sporulation screen. However, this overlap is limited to a very few genes (among 416 short-lived spore mutants), as described in the Results.
In this context, we also comment on a broader issue known in the ageing field. Short-lived mutants are less likely to function in ageing-related processes than long-lived mutants, as any perturbation that compromises cell integrity or general fitness – including poor spore wall assembly – will compromise survival regardless of its specific relevance to dormancy regulation. Thus, the long-lived mutants in our screen are more revealing of ageing-related processes. We will clarify and better describe these perspectives in the revised manuscript.
It would be helpful to make the data - or at least the lists of genes identified in the screen and the up and downregulated transcripts/proteins available as supplemental information.
Please note that we provide extensive Supplemental Datasets S1 to S6 that include this very information in table form (genes identified in the Bar-seq screens, differentially expressed RNAs and proteins across the different conditions), along with additional useful data from our large-scale assays.
There are at least two different points of diapause in Killifish. It was not entirely clear which diapause was analyzed here. This should be clarified.
We described the Diapause II stage in the Introduction as the most prominent dormant state and commonly studied state in N. furzeri. In the Results (p. 12), we stated, “To uncover conserved aspects of dormant states between yeast and vertebrates, we analyzed the transcript and protein signatures of N. furzeri diapause II embryos, henceforth called diapause embryos,….”. We also mentioned Diapause II in the Methods.
The data that offspring from older or heat-shocked spores are more stress resistant (Figure 4M) is not convincing. Serial dilution assays as shown are more qualitative than quantitative and an actual quantitative measure (eg. c.f.u.'s per cell plated) would be better and would allow statistical confidence to be calculated.
Serial dilution assays are widely used and accepted to assess differences in stress resistance. The problem with CFUs is that they only determine the proportion of viable cells, while stress resistance also incorporates growth (e.g., stress-sensitive cells may survive the stress but grow more slowly in its presence). However, we agree that the current data in Fig. 4M are not convincing. We are repeating the spot assay with 10-fold serial dilutions, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result. This is in response to a similar concern raised by Reviewer 3. We will also determine CFU counts, which would provide a more quantitative measure for statistical confidence, should the stress resistance be primarily reflected in cell viability. If necessary, for a more quantitative growth assay allowing statistical analysis, we may also analyse control and stressed cells growing in liquid cultures.
Description of the revisions that have already been incorporated in the transferred manuscript
We have started conducting additional experiments and making text changes to the manuscript, as described in the specific points above. At this stage, we do not provide a partially revised manuscript, but we will provide a fully revised one when everything is ready.
Description of analyses that authors prefer not to carry out
Reviewer 2:
Comment 1: The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?
We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of a revision. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.
