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Genomics of cellular proliferation in periodic environmental fluctuations

2018; Springer Nature; Volume: 14; Issue: 3 Linguagem: Inglês

10.15252/msb.20177823

1744-4292

Jérôme Salignon, Magali Richard, Etienne Fulcrand, Hélène Duplus‐Bottin, Gaël Yvert,

Gene Regulatory Network Analysis

Article5 March 2018Open Access Transparent process Genomics of cellular proliferation in periodic environmental fluctuations Jérôme Salignon Jérôme Salignon Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Magali Richard Magali Richard orcid.org/0000-0003-3165-3218 Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Etienne Fulcrand Etienne Fulcrand Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Hélène Duplus-Bottin Hélène Duplus-Bottin Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Gaël Yvert Corresponding Author Gaël Yvert [email protected] orcid.org/0000-0003-1955-4786 Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Jérôme Salignon Jérôme Salignon Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Magali Richard Magali Richard orcid.org/0000-0003-3165-3218 Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Etienne Fulcrand Etienne Fulcrand Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Hélène Duplus-Bottin Hélène Duplus-Bottin Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Gaël Yvert Corresponding Author Gaël Yvert [email protected] orcid.org/0000-0003-1955-4786 Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France Search for more papers by this author Author Information Jérôme Salignon1,‡, Magali Richard1,‡, Etienne Fulcrand1, Hélène Duplus-Bottin1 and Gaël Yvert *,1 1Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard de Lyon, Université de Lyon, Lyon, France ‡These authors contributed equally to this work *Corresponding author. Tel: +33 4 72 72 80 00; E-mail: [email protected] Molecular Systems Biology (2018)14:e7823https://doi.org/10.15252/msb.20177823 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes. Synopsis Genome-scale analysis of yeast fitness under periodic stress reveals how the selection of mutations can operate in dynamic environments. In oscillating environments, deviation from time-average fitness is common, and numerous gene deletions confer hyperproliferation at short stress periods. Two genomic screens are performed to address the question of fitness inhomogeneity (deviation from time-average expectation) in periodic salt stress and periodic methionine availability. Fitness is inhomogeneous for hundreds of genes, and these genes differ between the two surveys. Fast salinity oscillations provide a profound fitness advantage to a subset of gene deletions. Gene deletion mutants of the same pathway or with similar fitness alterations in steady conditions can largely differ in their response to dynamic conditions. Introduction Cells are dynamic systems that modify themselves in response to variation of their environment. Interactions between internal dynamics of intracellular regulations and external dynamics of the environment can determine whether a cell divides, differentiates, cooperates with other cells or dies. For some systems, usually from model organisms, the molecules involved in signal transduction and cellular adaptation are largely known. How they act in motion, however, is unclear, and it is difficult to predict which ones may be essential upon certain frequencies of environmental fluctuations. In addition, since most molecular screens were conducted in steady stress conditions or after a single stress occurrence, molecules that are key to the response dynamics may have been missed. The control of cellular proliferation is essential to life and is therefore the focus of intense research, but the interplay between proliferative control and environmental dynamics remains poorly characterized. In addition, proliferation drives evolutionary selection, and the properties of natural selection in fluctuating environments are largely unknown. Although experimental data exist (Stomp et al, 2008; Bleuven & Landry, 2016), they are scarce and the selection of mutations in dynamic conditions has mostly been studied under theoretical frameworks (Kussell & Leibler, 2005; Cvijović et al, 2015; Sæther & Engen, 2015; Svardal et al, 2015). Repeated stimulations of a cellular response may have consequences on growth that largely differ from the consequences of a single stimulus. First, a small growth delay following a stimulus may become highly significant when cumulated over multiple stimuli. Second, growth rate at a given time may depend on past environmental conditions that cells "remember", and this memory can sometimes be transmitted to daughter cells (Hilker et al, 2016). These two features are well illustrated by the study of Razinkov et al, who manipulated the stability of GAL1 mRNA transcripts that participated to short-term "memory" of galactose exposures: this resulted in a growth delay that was negligible after one galactose-to-glucose change but significant over multiple changes (Razinkov et al, 2013). Other memorization effects were observed in bacteria during repeated lactose to glucose transitions, this time due to both short-term memory conferred by persistent gene expression and long-term memory conferred by protein stability (Lambert & Kussell, 2014). The yeast response to high concentrations of salt is one of the best-studied mechanisms of cellular adaptation. When extracellular salinity increases abruptly, cell size immediately reduces and yeast triggers a large process of adaptation. The translation programme (Uesono & Toh-e, 2002; Warringer et al, 2010) and turnover of mRNAs (Miller et al, 2011) are re-defined, calcium accumulates in the cytosol and activates the calcineurin pathway (Ariño et al, 2010), osmolarity sensors activate the high-osmolarity glycerol MAPK pathway (Hohmann, 2009; Ariño et al, 2010), glycerol accumulates intracellularly as a harmless compensatory solute (Hohmann, 2009), and membrane transporters extrude excess ions (Ariño et al, 2010). Via this widespread adaptation, hundreds of genes are known to participate to growth control after a transition to high salt. What happens in the case of multiple osmolarity changes is less clear, but can be investigated by periodic stimulations of the adaptive response. For example, periodic transitions between 0 and 0.4 M NaCl showed that MAPK activation was efficient and transient after each stress except in the range of ~8-min periods, where sustained activation of the response severely hampered cell growth (Mitchell et al, 2015). How genes involved in salt tolerance contribute to cell growth in specific dynamic regimes is unknown. Yeast cells also respond to extracellular methionine concentrations by modulating its import and biosynthesis. This response relies on molecular pathways that are largely unrelated to salt stress. It has also been well described (Thomas & Surdin-Kerjan, 1997), but not in the context of dynamic environmental fluctuations. We sought to systematically search for genes involved in the dynamics of a cellular response. Identifying such genes can be done by applying specific stimulations to mutant cells periodically and testing whether the effect of the mutation on proliferation is averaged over time. In other words, does fitness (proliferation rate relative to wild type) of a mutant under periodic stress match the time average of its fitness in each alternating condition? This problem of temporal heterogeneity is equivalent to the homogenization problem commonly encountered in physics for spatial heterogeneity, where microscopic heterogeneities in materials modify macroscopic properties such as stiffness or conductivity (Hassani & Hinton, 1998). A homogeneous fitness (averaged over time) implies that (i) the effect of a mutation on the response occurs rapidly as compared to the frequency of environmental changes, (ii) it does not affect the response lag phase and (iii) the mutated gene is not involved in relevant memory mechanisms. In contrast, fitness inhomogeneity (deviation from time-average expectation) is indicative of a role of the gene in the response dynamics. In this study, we present two genomic screens that address this homogenization problem in the context of yeast cells responding to periodic salt stress or periodic methionine availability. The results reveal how selection of mutations can depend on environmental oscillations and identify molecular processes that unexpectedly become major controllers of proliferation at short periods of repeated stress. Results Genomic profiling of proliferation rates in steady and periodic salt stress We measured experimentally the contribution of thousands of yeast genes on proliferation in two steady conditions of different salinity and in an environment that periodically oscillated between the two conditions. We used a collection of yeast mutants where ~5,000 non-essential genes have been individually deleted (Giaever et al, 2002). Since every mutant is barcoded by a synthetic DNA tag inserted in the genome, the relative abundance of each mutant in pooled cultures can be estimated by parallel sequencing of the barcodes (BAR-Seq) (Smith et al, 2009; Robinson et al, 2014). We set up an automated robotic platform to culture the pooled library by serial dilutions. Every 3 h (average cell division time), populations of cells were transferred to a standard synthetic medium containing (S) or not (N) 0.2 M NaCl. The culturing programme was such that populations were either maintained in N, maintained in S or exposed to alternating N and S conditions at periods of 6, 12, 18, 24 or 42 h (Fig 1A). Every regime was run in quadruplicates to account for biological and technical variability. The duration of the experiment was 3 days, and populations were sampled at times 0, 24, 48 and 72 h for sequencing. After data normalization and filtering, we examined how relative proliferation rates compared between the periodic and the two steady environments. Figure 1. Genomic profiling of fitness in periodic salt stress Experimental design. Populations of yeast deletion strains are cultured in media N (no salt), S (salt) and in conditions alternating between N and S at various periods. Allele frequencies are determined by BAR-Seq and used to compute fitness (proliferation rate relative to wild type) of each mutant. Time course of mutant abundance in the population, shown for six mutants. Relative abundance corresponds to the median of log2(y/y0) values ± SD (n = 4 replicate cultures, except for condition N at day 3: n = 3), where y is the normalized number of reads, and y0 is y at day 0. Conditions: N (blue), S (yellow), 6-h periodic oscillations (NS6, hatching). Generalized linear models (predicted value ± SE) fitted to the data shown in (B), coloured by condition: N (blue); S (yellow); NS6 predicted by the null model (grey) or predicted by the complete model including inhomogeneity (red). ***P < 10−8. n.s., non-significant, based on the GLM (see 4). Fitness values (w) computed from the data of two mutants shown in (B). Bars, mean ± SEM, n = 3 (N) or 4 (S, NS6) replicate cultures, coloured according to culture condition. Grey dashed line: expected fitness in case of additivity (geometric mean of fitness in N and S weighted by the time spent in each medium). Scatterplot of all mutants showing their observed fitness under 6-h periodic oscillations (y-axis, NS6 regime) and their expected fitness in case of additivity (x-axis, weighted geometric mean of fitness in N and S). Deviation from the diagonal reflects inhomogeneity. Red dots: 456 mutants with significant inhomogeneity (FDR = 0.0001, see 4). Correlation between fitness estimates (w). Each dot corresponds to the median fitness of one mutant in one condition (N, S or NS6), measured from pooled cultures (x-axis) or from individual assays (one mutant co-cultured with WT cells, y-axis). Whole data: 52 mutants. R, Pearson coefficient; grey line, y = x; red line, linear regression. Validation of inhomogeneity by cell counting. One graph shows the time course of mutant abundance when it was individually co-cultured with GFP-tagged wild-type cells, measured by flow cytometry. Median values ± SD (n = 4 replicate cultures). Conditions: N (blue), S (yellow), 6-h periodic oscillations (NS6, hatching). Download figure Download PowerPoint Protective genes have diverse contributions to proliferation under periodic stress We observed that genes involved in salt tolerance during steady conditions differed in the way they controlled growth under periodic regimes. Differences were visible both among genes inhibiting growth and among genes promoting growth in high salt. For example, NBP2 is a negative regulator of the HOG pathway (Mapes & Ota, 2004) and MOT3 is a transcriptional regulator having diverse functions during osmotic stress (Montañés et al, 2011, 3; Martínez-Montañés et al, 2013). As shown in Fig 1B, deletion of either of these genes improved tolerance to steady 0.2 M NaCl (condition S). In the 6-h periodic regime, the relative growth of mot3Δ/Δ cells was similar to the steady condition N, as if transient exposures to the beneficial S condition had no positive effect. In contrast, the benefit of transient exposures was clearly visible for nbp2Δ/Δ cells. Differences were also apparent among protective genes. The Rim101 pathway has mostly been studied for its role during alkaline stress (Ariño et al, 2010), but it is also required for proper accumulation of the Ena1p transporter and efficient Na+ extrusion upon salt stress (Marqués et al, 2015). Eight genes of the pathway were covered by our experiment. Not surprisingly, for all positive regulators of the pathway, gene deletion decreased proliferation in S and increased proliferation in N (Fig 1B and Appendix Fig S1). This is consistent with the need of a functional pathway in S and the cost of maintaining it in N where it is not required. However, the response to 6-h periodic stimulations was different between mutants (Appendix Fig S1). Although RIM21, DFG16 and RIM9 all code for units of the transmembrane sensing complex (Obara et al, 2012), proliferation was high for rim21Δ/Δ and dfg16Δ/Δ cells but not for rim9Δ/Δ cells. Similarly, Rim8 and Rim20 both mediate the activation of the Rim101p transcriptional repressor (Xu & Mitchell, 2001; Herrador et al, 2010); but rim8Δ/Δ and rim101Δ/Δ deletions increased proliferation under periodic stress, whereas rim20Δ/Δ did not. This pathway was not the only example displaying such differences. Cells lacking either the HST1- or the HST3 NAD(+)-dependent histone deacetylase (Brachmann et al, 1995) grew poorly in S, but hst1Δ/Δ cells tolerated 6-h periodic stress better than hst3Δ/Δ cells (Fig 1B). Thus, gene deletion mutants of the same pathway or with similar fitness alterations in steady conditions can largely differ in their response to dynamic conditions. Widespread deviation from time-average fitness in periodic salt stress We then systematically asked for each of the 3,568 gene deletion mutants, whether its fitness in periodic salt stress matched the time average of its fitness in conditions N and S. We tested both the statistical significance and quantified the deviation from the time-average expectation. For statistical inference, we exploited the full BAR-Seq count data, including all replicated populations, by fitting to the data a generalized linear model that included a non-additive term associated with the oscillations (see 4). The models obtained for the six genes discussed above are shown in Fig 1C. Overall, at the 6-h period, we estimated that deviation from time-average fitness was significant for as many as ~2,000 genes, because it was significant for 2,497 genes at a false discovery rate (FDR) of 0.2 (Appendix Table S1). At a stringent FDR of 0.0001, we listed 456 gene deletions for which fitness inhomogeneity was highly significant. For quantification, we computed fitness values as in Qian et al (2012) (Fig 1D) and plotted the observed fitness of all genes in the 6-h periodic environment as a function of their expected time-average fitness (Fig 1E). As for nbp2Δ/Δ, observed and expected values were often in good agreement. Highlighting the 456 significant genes revealed a surprising trend: for the majority of gene deletions expected to increase proliferation in the periodic regime (expected fitness > 1), observed fitness was unexpectedly high. Gene annotations corresponding to higher-than-expected fitness were enriched for transcriptional regulators and for members of the cAMP/PKA pathway (Appendix Table S2), which is consistent with cellular responses to environmental dynamics. Although BAR-Seq can estimate thousands of fitness values in parallel, it has two important limitations: estimation by sequencing is indirect and the individual fitness of a mutant is not distinguished from possible interactions with other mutants of the pool. We therefore sought to validate a subset of our observations by applying individual competition assays. Each mutant was co-cultured with a GFP-tagged wild-type strain, in N or S conditions or under the 6-h periodic regime, and the relative number of cells was counted by flow cytometry (Qian et al, 2012; Duveau et al, 2014). Correlation between fitness estimates from BAR-Seq and individual assays was similar to previous reports (Qian et al, 2012; Venkataram et al, 2016; Fig 1F, Appendix Fig S2), and the assays unambiguously validated the fitness inhomogeneity of several mutants including rim21Δ/Δ and mot3Δ/Δ (Fig 1G). Impact of salinity dynamics on mutant proliferation If fitness inhomogeneity (deviation from time average) is attributable to environmental dynamics, then it should be less pronounced at large periods of oscillations. Our experiment included four conditions with periods larger than 6 h. For each period, we computed for each mutant the ratio between its observed fitness in periodic stress and the time-average expectation from its fitness in the two steady conditions N and S. Fitness is inhomogeneous when this ratio deviates from 1. Plotting the distribution of this ratio at different periods of oscillation showed that, as expected, inhomogeneity was less and less pronounced as the period increased (Fig 2A). We examined more closely three mutants displaying the highest inhomogeneity at the 6-h period. Plotting their relative abundance in the different populations over the time of the experiment clearly showed that fitness of these mutants was unexpectedly extreme at short periods but less so at larger periods (Fig 2B). Figure 2. Proliferative advantage depends on environmental dynamics Violin plots showing the distribution of fitness inhomogeneity of 3,568 gene deletions at the indicated periods of salinity oscillations. Traces and labels, mutants with extreme inhomogeneity at 6-h period. Top, number of gene deletions with significant inhomogeneity at FDR = 0.0001. Time course of the abundance of mutants cin5Δ/Δ, srf1Δ/Δ and yor029wΔ/Δ in the pool of all mutants, under different alternating regimes, quantified by BAR-Seq. Median values ± SD (n = 4 replicate cultures, except for the N condition at day 3: n = 3). The genetic variance in fitness of the pooled population of mutants was computed for each condition. Bars: 95% CI bootstrap intervals. Download figure Download PowerPoint The result showing that some mutants but not all were extremely fit to short-period oscillations suggested that the extent of differences in fitness between mutants may change with the environmental period. To see whether this was the case, we estimated the genetic variance in fitness of each pooled population of mutants. Distinguishing the genetic variance from the non-genetic variance was possible because of the presence of replicates in our experimental design (see 4). Fitness variation between strains was more pronounced when populations were grown in S than in N, which agrees with the known effect of stress on fitness differences (Martin & Lenormand, 2006). Remarkably, differences were even larger in fast-oscillating regimes, but not slow-oscillating ones (Fig 2C). This shows that environmental dynamics can exert additional selective pressures at the level of the whole population (see 3). Fitness in periodic salt stress vs. steady conditions We asked whether fitness inhomogeneity in the 6-h periodic stress was related to fitness values in steady conditions, and for many gene deletions, it was. Inhomogeneity was associated with high fitness in both N and S conditions (Fig 3A–D, red dots). Interestingly, a particular set of gene deletions displayed very high fitness inhomogeneity together with distinct fitness in steady conditions: advantageous in N but not in S (Fig 3A–D, blue dots). Annotations of these genes were enriched for osmosensing and response (Appendix Table S2), although several gene deletions of this functional category did not display this behaviour. Thus, cells defective for specific components of the osmostress response can outproliferate other cells in this periodic environment, likely because they do not trigger a costly and unnecessary adaptive process. Figure 3. Fitness inhomogeneity with respect to fitness in steady conditions A. Fitness inhomogeneity in the 6-h periodic regime is poorly correlated to expected fitness. Red, genes with high expected fitness (> 1.05). Blue, genes with high inhomogeneity (> 1.045) but moderate expected fitness (< 1.05), with those annotated in relation to osmosensing and response shown in light blue and by name. Black dots, examples of genes involved in osmosensing/response, but which do not display inhomogeneity. B. Fitness inhomogeneity vs. difference in fitness between the two steady environments. Colours, same as panel (A). C, D. Observed fitness in the 6-h periodic regime as a function of fitness in S (panel C) or fitness in N (panel D). Grey line: identity. Colours, same as in panel (A). Download figure Download PowerPoint Some gene deletions improved growth in one steady condition (w > 1) and penalized it in the other (w < 1). This phenomenon is a special case of gene × environment interaction called antagonistic pleiotropy (AP) (Qian et al, 2012). It is difficult to anticipate whether such mutations will have a positive or negative impact on growth in a periodic regime that alternates between favourable and unfavourable conditions, especially since fitness is not necessarily homogenized over time. Using a specific test (see 4), we found 48 gene deletions with statistically significant AP between the N and S conditions (FDR = 0.01, Appendix Table S3 and Fig S3). Interestingly, three of these genes coded for subunits of the chromatin-modifying Set1/COMPASS complex (Appendix Table S2 and Fig S4). We investigated whether the direction of effect of these 48 deletions depended on the period of oscillations (Fig 4A–C). The effect was positive at all periods for 33 AP deletions and negative at all periods for 6 AP deletions. For two mutations (vhr1Δ/Δ and rim21Δ/Δ), the direction of selection changed with the oscillating period. To visualize the periodicity dependence of all AP deletions, we clustered them according to their fitness inhomogeneities (Fig 4B and C). This highlighted five different behaviours: oscillations could strongly favour proliferation of a mutant at all periods (e.g. cin5Δ/Δ) or mainly when they were fast (e.g. oca1Δ/Δ), they could mildly increase (e.g. rim101Δ/Δ) or decrease it (e.g. csf1Δ/Δ) or they could both increase and decrease it depending on their period (vhr1Δ/Δ). Thus, fitness during alternating selection was generally asymmetric in favour of positive selection, and its dependency to the alternating period differed between genes. Figure 4. Oscillations between antagonistic conditions The 48 gene deletions with significant antagonistic pleiotropy (AP) between N and S were classified according to their direction of effect on growth in periodic salt stress ("pos"itive = advantageous, "always" = at all periods of oscillations, "neg"ative = disadvantageous). Hierarchical clustering of AP deletions according to fitness inhomogeneity in periodic salt stress. Fitness values of five mutants representative of the clusters shown in (B). Bars: mean ± SEM, n = 3 (N) or 4 (others) replicate cultures. Download figure Download PowerPoint Salinity oscillations heighten the proliferation of some mutant cells We made the surprising observation that fitness during salt oscillations could exceed or fall below the fitness observed in both steady conditions (Fig 2B), a behaviour called "transgressivity" hereafter. By using the available replicate fitness values, we detected 55 gene deletions where fitness in the 6-h periodic stress was significantly higher than the maximum of fitness in N and in S and 23 gene deletions where it was lower than the minimum (Fig 5A, FDR = 0.03, see 4). Importantly, transgressivity was observed not only from BAR-Seq but also when studying gene deletions one by one in competition assays, as shown for pde2Δ/Δ, tom7Δ/Δ, trm1Δ/Δ and yjl135wΔ/Δ (Fig 5B–E). This transgressivity may have important implications on the spectrum of mutations found in hyperproliferative clones that experienced repetitive stress (see 3). Strikingly, the gene deletions displaying this effect were associated with various cellular and molecular processes: cAMP/PKA (pde2Δ/Δ), protein import into mitochondria (tom7Δ/Δ), autophagy (atg15Δ/Δ), tRNA modification (trm1Δ/Δ), phosphatidylcholine hydrolysis (srf1Δ/Δ) and MAPK signalling (ssk1Δ/Δ, ssk2Δ/Δ); and some of these molecular functions were not previously associated with salt stress. Figure 5. Extreme proliferation rates emerging from salinity oscillations A. Scatterplot of all mutants showing their observed fitness in the 6-h periodic salt stress (NS6) relative to their fitness in N (x-axis) and S (y-axis). Violet, 78 mutants with significant transgressivity (FDR = 0.03). B–E. Time course of mutant abundance in the pool of all mutants (BAR-Seq, left, as in Fig 1B) or when the mutant was individually co-cultured with GFP-tagged wild-type cells (flow cytometry, right, as in Fig 1G). Median values ± SD (n = 4 replicate cultures, except for BAR-Seq N condition at day 3: n = 3). Conditions: N (blue), S (yellow), NS6 (hatching). F–G. Complementation assays. Diploid homozygous deletion mutants for pde2 and tom7 (strains GY1821 and GY1804, respectively) were complemented by integration of the wild-type gene at the HO locus (strains GY1929 and GY1921, respectively). Strains were co-cultured for 24 h with GFP-tagged wild-type cells (strain GY1961), and relative fitness was measured by flow cytometry. Conditions: N (blue), S′ (0.4 M NaCl; orange) and 6-h periodic oscillations between N and S′ (hatching). Bars, mean fitness ± SEM (n = 3 replicate cultures). Download figure Download PowerPoint The high-affinity cAMP phosphodiesterase and Tom7p are necessary to limit hyperproliferation during periodic salt stress As mentioned above, several gene deletions impairing the cA