Oscillatory stress stimulation uncovers an Achilles’ heel of the yeast MAPK signaling network

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Science  11 Dec 2015:
Vol. 350, Issue 6266, pp. 1379-1383
DOI: 10.1126/science.aab0892

Altering timing perturbs cell signaling

Biological regulatory systems have been optimized by evolution to accommodate environmental variation. Yet these systems may also have fragile aspects that can be exposed by variation in the timing of signaling events. Mitchell et al. studied the properties of the yeast signaling system that allows cells to adapt to changing osmotic conditions. The same properties also made the system sensitive to hyperactivation and the consequent inhibition of cell growth if exposed to oscillations in osmotic conditions with a particular frequency. The identification of similar fragility in other regulatory pathways might prove useful in the development of therapeutic strategies against diseases in which signaling is perturbed, such as cancer and diabetes.

Science, this issue p. 1379


Cells must interpret environmental information that often changes over time. In our experiment, we systematically monitored the growth of yeast cells under various frequencies of oscillating osmotic stress. Growth was severely inhibited at a particular resonance frequency, at which cells show hyperactivated transcriptional stress responses. This behavior represents a sensory misperception: The cells incorrectly interpret oscillations as a staircase of ever-increasing osmolarity. The misperception results from the capacity of the osmolarity-sensing mitogen-activated protein kinase (MAPK) network to retrigger with sequential osmotic stresses. Although this feature is critical for coping with natural challenges, such as continually increasing osmolarity, it results in a trade-off of fragility to non-natural oscillatory inputs that match the retriggering time. These findings demonstrate the value of non-natural dynamic perturbations in exposing hidden sensitivities of cellular regulatory networks.

Cells have evolved complex signaling networks to monitor and respond to stimuli in their environment. As the cellular environment can dynamically change, evolution may select for sensory systems that are optimized for temporal patterns of stimulation that are frequently encountered by the organism. Such sensory systems may perform poorly when challenged by a non-natural stimulus pattern. Thus, exposing cells to time-variant inputs in controlled experiments can shed light on the mechanisms underlying cellular response, as well as the selection forces that shaped the biological system during evolution.

We systematically probed how the fitness of yeast cells responded to different dynamic patterns of osmotic stress. In Saccharomyces cerevisiae, the Hog1 mitogen-activated protein kinase (MAPK) pathway responds to increases in osmotic stress and ultimately leads to increased synthesis and retention of glycerol (1). Activation of the Hog1 MAPK is transient, even when osmotic stress persists (2). This adaptation allows cells to reset themselves and remain responsive to further increasing osmolarity that might occur with evaporation (3). Although MAPK signaling dynamics are well characterized, relatively little is known about the fitness of yeast cells when faced with different dynamic patterns of osmolarity.

We used time-lapse microscopy with single-cell resolution to monitor cell growth under dynamically controlled osmolarity profiles (Fig. 1A and supplementary materials and methods). Cells grown in microfluidic chambers were subjected to regular oscillations in osmolarity over a time span allowing for multiple rounds of cell division (amplitude range: 0 to 0.4 M KCl). We tracked colony growth when cells were exposed to continuous high osmolality (single-step increase) or to oscillations in osmolarity with a periodicity of 1, 8, or 32 min (Fig. 1B). Although the integrated osmolarity experienced by cells during these experiments was identical, cells grew considerably slower under the intermediate frequency of 8 min (movie S1). When tested under a wide range of oscillatory frequencies (0.5 to 128 min), cellular growth was drastically hampered in a narrow range of intermediate frequencies, with this inhibitory effect peaking at an 8-min resonance frequency (Fig. 1C). At this periodicity, cells were larger and contained large vacuoles (fig. S2).

Fig. 1 Osmotic oscillations at an intermediate frequency cause slow proliferation.

(A) Schematic of the flow chamber used in our experiment. (B) Cell growth under various frequencies of mild osmostress (0.4 M KCl). The graphs show the average number of progeny cells relative to the number of cells before stress is applied (n indicates the number of parental cells monitored). Growth without osmotic stress is depicted in gray. The insets at right show representative images of cells. (C) Systematic frequency scan of mild osmotic oscillations (0.4 M KCl). The graph shows the mean doubling time over a period of 8 hours. Each point marks the mean generation time calculated from at least 50 individual sets of progeny in two biological repeats. Error bars indicate SE.

To explore what cellular mechanisms might underlie the band-pass frequency selectivity of growth inhibition, we used a computational model developed to study the adaptive dynamics of yeast osmotic signaling (3) (Fig. 2A). Changes in the turgor pressure across the cell wall and membrane are sensed and culminate in phosphorylation of the MAPK Hog1. Phosphorylated Hog1 (Hog1-PP) regulates cytoplasmic proteins and gene expression, thus increasing internal glycerol concentrations and restoring turgor pressure. In response to a single-step osmotic shock, accumulation of Hog1-PP shows two phases: an induction phase that quickly peaks at 5 min, followed by slower adaptation within 30 min (Fig. 2B). However, if osmolarity stress is suddenly removed, Hog1-PP levels decrease almost immediately through action of protein phosphatases.

Fig. 2 Mathematical modeling of adaptive signaling of the osmotic pathway predicts downstream pathway hyperactivation at resonance stress frequency.

(A) Schematic of the osmotic pathway (3). Changes in turgor pressure activate Hog1-dependent and Hog1-independent response arms that act to reduce deviation from the optimal turgor pressure. (B) Pathway activation according to the perfect-adaptation model (3). (Top) Predicted amounts of Hog1 phosphorylation in response to a 0.4 M increase in osmolality with induction and adaptation phases. (Bottom) Integral under the Hog1-PP curve, taken as an approximation of the accumulated transcriptional output. (C) Pathway activation at three representative pulse durations (ON and OFF intervals are marked in red and gray, respectively). The area under the predicted signaling curve (Top) was normalized to the entire pulse period (ON + OFF) (Bottom). (D) Model-predicted signaling and transcriptional dynamics under representative oscillation periods. (E) Experimentally observed signaling dynamics under representative oscillation periods, as measured by tracking Hog1-GFP nuclear localization. The graphs show the mean intensity ratio of nuclear Hog1-GFP over total Hog1-GFP in 40 to 100 cells (relative to the basal ratio at t = 0 min). (F) Measured signaling integral (normalized per minute) in a frequency scan. The blue bars show the average integral in two biological repeats (error bars indicate SD). The dashed black curve marks the model predictions.

Because downstream changes in Hog1-PP–induced gene expression are expected to occur on a much slower time scale (hours) (4) as compared with MAPK adaptation (minutes), we can use the integral under the Hog1-PP curve as an approximation for the expected level of downstream transcriptional output (Fig. 2B). In response to a single-step increase in osmolarity, Hog1-PP shows a transient adaptive curve, and transcriptional output is expected to monotonically increase and reach a plateau once Hog1-PP returns to its basal level (protein levels will slowly decay afterward due to dilution and degradation). Similar downstream dynamics are not restricted to transcription but can manifest in any cellular activity that involves slow decay and hence acts as an integrator of MAPK signaling activity over time.

By tracking expected changes in osmotic stress–induced gene expression, this computational model can explain the stress sensitivity at the resonance frequency. We used the model to estimate response dynamics for a cell exposed to a high-osmolarity pulse at three representative pulse durations (Fig. 2C). Under oscillations, the ON pulse is followed by an OFF pulse of the same length; therefore, the averaged transcriptional rate can be calculated from the integral under the signaling curve divided by the length of the full pulse period (ON + OFF duration). The model predicts that the normalized transcriptional output will maximize at an intermediate frequency of 16 min (as though the system contains a band-pass filter). The signaling dynamics have markedly different effects at different frequencies (Fig. 2D): Under a high-frequency stimulus, the signaling is terminated quickly, leading to very slow transcription. However, under an intermediate frequency, the signaling peaks in each oscillation. Because oscillations are still relatively frequent, signaling results in a high, ever-increasing transcriptional output. Under a low frequency, the signaling peaks and completely adapts. Yet because the encounters with stress are rare, this leads to a low overall transcriptional output. We experimentally tested this hypothesis by tracking Hog1–green fluorescent protein (GFP) localization under osmotic oscillations as a proxy for signaling dynamics (Hog1-GFP enters the nucleus when activated) (Fig. 2E). We observed a good agreement with the model predictions: The integral under the nuclear Hog1-GFP curve is maximized for an intermediate frequency of 16 min (Fig. 2F).

Thus, the model points to a plausible cellular mechanism: Adaptive signaling dynamics (the ability of the MAPK to reset and retrigger) may lead to downstream pathway hyperactivation at an intermediate resonance frequency. We used live-cell reporters (promoters linked to fluorescent proteins) to examine the transcriptional activity of the osmotic pathway and the intimately related invasive growth MAPK pathway that is triggered by starvation (Fig. 3A) (5, 6). Note that despite sharing many common components, the individual pathways normally remain highly insulated from one another (5, 711). Under a single-step osmotic stress, cells transiently induced the osmotic transcriptional response (peaking at 50-fold after 2 hours) (movie S1) with very little effect on the invasive-growth pathway (Fig. 3B). However, oscillatory osmolarity led to continuous induction of the osmotic response, culminating in pathway hyperactivation (450-fold increase after 8 hours) (movie S1). Moreover, the oscillations also led to full activation of the normally isolated invasive pathway (consistent with morphological changes observed for some cells) (fig. S2). A frequency scan showed that transcription of both pathways peaks at an intermediate frequency range (8 to 16 min) (Fig. 3C). The mating pathway, a third interwoven pathway, remains isolated (fig. S3) (10, 12). Thus, stimulation at the resonance frequency led to hyperactivation of the osmotic response and misactivation of the invasive growth response (Fig. 3D).

Fig. 3 Pathway hyperactivation and cross-talk underlie growth inhibition at the sensitive frequency.

(A) Network diagram of the high-osmolality and invasive-growth pathways. YFP, yellow fluorescent protein. (B) Transcriptional output of the pathways in response to alternative inputs. The graphs show the mean fold induction in florescence per cell and the single-cell traces of cells within the interquartile range. The graph with the dotted purple line shows full pathway activation in response to butanol. Although pathway isolation is maintained under a step input profile, osmotic oscillations lead to hyperactivation of the osmotic response and full activation of the invasive-growth pathway. The microscopy images show representative cells 8 hours after their first exposure to stress. Arrowheads indicate cells that died during the stress period. (C) Transcriptional response at various frequencies of osmotic stress (0.4 M KCl). The activity of both reporters behaves as a band-pass filter, with peaked activity at intermediate frequencies (8 to 16 min). Error bars indicate SE of maximal fold fluorescence measured for 70 to 200 individual cells. (D) Frequency-dependent model of the MAPK network that explains growth inhibition at the resonance frequency. (E) Mutational analysis points to a contribution from both pathways in growth inhibition under osmotic oscillations (0.4 M, 8-min period). The color code marks the fold improvement of the deleted strain relative to the WT strain. Statistical significance was evaluated with the t test (comparing the mean growth rate of multiple progeny of the deleted strain and multiple progeny of the cocultured WT strain).

To evaluate whether both osmotic hyperactivation and cross-talk with the invasive pathway are detrimental to growth, we tested the phenotypes of specific mutations. We reasoned that deletions that weaken pathway activity might improve growth under oscillatory osmotic stress (Fig. 3E and fig. S5). Our measurements indicated that weakening the pathway by deletion of one of the osmosensing branches improved growth (complete knockout of the osmotic pathway did not improve growth because the core protective osmotic response is still necessary, even under oscillations). We also observed that knockout of invasive pathway genes is beneficial and that deletions targeting shared components in both pathways, such as Ste11 MAPK kinase kinase (13), are more advantageous than deletions targeting only one pathway.

Given the detrimental effects of pathway hyperactivation, we reasoned that an improved cascade could be engineered by adding a slow negative-feedback loop to the MAPK cascade. Ideally, this feedback would allow an initial osmotic response while dampening rapid retriggering (adding a longer refractory period). We implemented a feedback loop using OspF, a previously characterized bacterial effector protein that irreversibility inactivates phosphorylated Hog1 (14) (Fig. 4A). Monitoring the transcription in an engineered strain showed that the engineered pathway is still responsive to a single-step input but is not hyperactivated under oscillations (Fig. 4B and movie S2).

Fig. 4 Introducing a synthetic feedback loop resolves osmotic hyperactivation and relieves growth inhibition under osmotic oscillations but also reduces proliferation under more natural input dynamics.

(A) Diagram of the genetic circuit that underlies the conditional negative feedback. The bacterial effector OspF (fused to an osmotic stress–responsive promoter) deactivates phosphorylated Hog1 by removing a hydroxyl group (14), leading to a longer delay in retriggering of the pathway. (B) Transcriptional response of the osmotic pathway in the WT strain and engineered strain. Both strains show a transient response after an osmotic shock but respond differently to an oscillating input. The graphs show the mean fold induction in florescence per cell and the single-cell traces of cells within the interquartile range. (C) Comparative growth assays of the WT and engineered strains under alternative inputs. (D) Growth inhibition under oscillatory input originates from the adaptive nature of the osmotic response. Although the signaling cascade effectively filters oscillatory inputs at a high frequency (15), oscillations at a lower frequency lead to repeated stimulation of the osmotic pathway. In this frequency range, the cascade circuitry perceives an oscillatory input as gradually increasing osmolarity and hence keeps the osmotic pathway continuously active to counteract the seemingly increasing high osmolality. Growth inhibition is maximized at an intermediate frequency because it is interpreted as the steepest stepwise (i.e., staircase) increase in osmolality, which leads to peaked levels of downstream hyperactivation.

We then tested whether this network rewiring could improve growth under alternative dynamic stress inputs (Fig. 4C). Consistent with measurements of transcriptional activity, we observed that the engineered and wild-type (WT) strains had equal growth rates when exposed to a single step of osmotic stress but that the engineered negative-feedback strain grew considerably faster under osmotic oscillations. Nonetheless, when we compared strain growth under more natural types of dynamic stress profiles, we observed an opposite trend: Under a primed or gradually increasing osmolarity pattern, as may occur during evaporation of an aqueous niche, the WT strain grew faster. Thus, there is an inherent trade-off—our rewiring prevents detrimental pathway hyperactivation in response to oscillations but also leads to impaired growth in dynamic environments that truly require pathway reactivation (such as naturally occurring upward ramps of stress).

The detrimental sensitivity to osmotic oscillations can be viewed as an inherent limitation of the underlying biological system (Fig. 4D). In analogy to a sensory misperception phenomenon, the ability of oscillations to retrigger the osmotic response is misinterpreted by the cells as an infinite staircase increase in osmolality (15) that culminates in deleterious transcriptional hyperactivation. Thus, although the adaptive response allows the biological system to remain responsive in complex environments that it experiences in nature, it also creates an inherent “Achilles’ heel” due to its failure to prevent pathway hyperactivation in non-natural oscillating environments. From an evolutionary perspective, this Achilles’ heel is unimportant because the yeast are unlikely to experience oscillatory stress at the resonant frequency.

Our observations in yeast may have implications for the dynamic sensitivities of other biological systems, as many responses display adaptation or the ability to retrigger (16), and these may also have resonance frequency sensitivities. Our results may additionally be relevant for cellular signaling in disease, as mutations affecting cellular signaling are common in cancer, autoimmune disease, and diabetes. These mutations may rewire the native network and thus could modify its activation and adaptation dynamics. Such network rewiring in disease may lead to changes that can be most clearly revealed by simulation with oscillatory inputs or other non-natural patterns. The changes in network response behaviors could be exploited for diagnosis and functional profiling of disease cells or could potentially be taken advantage of as an Achilles’ heel to selectively target cells bearing the diseased network (17).

Supplementary Materials

Materials and Methods

Figs. S1 to S5

Tables S1 and S2

References (18, 19)

Movies S1 and S2

References and Notes

  1. Acknowledgments: We thank H. Youk, R. Almeida, S. Coyle, and M. Thomson for insightful discussions. This work was supported by NIH grants R01 GM55040, R01 GM62583, PN2 EY016546, and P50 GM081879; the NSF Synthetic Biology Engineering Research Center (SynBERC); and HHMI (to W.A.L.). This work was also supported in part by MOST grant 2015CB910300, National Natural Science Foundation of China grant 31470819, and Peking-Tsinghua Center for Life Sciences (to P.W.). A.M. is a European Molecular Biology Organization Fellow (ALTF 419-2010) and the recipient of a Program for Breakthrough Biomedical Research Postdoctoral Research Award (UCSF).
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