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Haploid Saccharomyces cerevisiae yeast cells use a prototypic cell signaling system to transmit information about the extracellular concentration of mating pheromone secreted by potential mating partners. The ability for cells to respond distinguishably to different pheromone concentrations depends on how much information about pheromone concentration the system can transmit. Here we show that the MAPK Fus3 mediates fast-acting negative feedback that adjusts the dose-response of downstream system response to match that of receptor-ligand binding. This “dose-response alignment”, defined by a linear relationship between receptor occupancy and downstream response, can improve the fidelity of information transmission by making downstream responses corresponding to different receptor occupancies more distinguishable and reducing amplification of stochastic noise during signal transmission. We also show that one target of the feedback is a novel signal-promoting function of the RGS protein Sst2. Our work suggests that negative feedback is a general mechanism used in signaling systems to align dose-responses and thereby increase the fidelity of information transmission.
Cells use signaling systems to sense and transmit information about extracellular conditions. Haploid Saccharomyces cerevisiae yeast cells use a prototypic, G-protein coupled-receptor/MAP kinase cascade signaling system, the pheromone response system 1, to sense and transmit information about the concentration of mating pheromone secreted by cells of the opposite mating type (Fig. 1). The more information about pheromone concentration the system can transmit, the better a cell can distinguish between different pheromone concentrations, an essential ability for proper partner choice and mating. For example, a yeast cell ringed by potential mating partners strongly prefers to mate with partners producing the most pheromone 2. Partner choice involves two processes that require sensing of pheromone concentration. First, a cell grows up the pheromone concentration gradient 3, a process that likely depends on measurement of precise differences in pheromone concentration at different points on the cell surface. Second, after contacting its partner and forming a prezygote, a cell preferentially completes fusion and forms a diploid with a partner that produces high amounts of pheromone 4. These experiments indicate it is important for cells to distinguish among different pheromone concentrations at multiple steps during the mating process.
Prior work suggested that optimal transmission of information about pheromone concentration depends on both distinguishable receptor occupancies and distinguishable downstream system responses. Differences in receptor occupancy are clearly important for mating partner choice and discrimination; for example, in the presence of exogenous pheromone at a concentration that saturates the receptor, cells lose the ability to discriminate high pheromone-secreting partners from low pheromone-secreting partners 2. However, distinguishable receptor occupancies are not sufficient for partner discrimination, since hypersensitive cells, in the presence of exogenous pheromone at a concentration that does not saturate the receptor but does saturate downstream responses, also lose the ability to discriminate between partners secreting different levels of pheromone 2. In complementary studies of orientation of mating projections in spatial gradients of pheromone, Segall 3 showed that hypersensitive cells did not orient their mating projections as precisely as wild-type cells and suggested that this might result from saturation of downstream responses at most points in the gradient. However, after reducing the gradient pheromone concentrations 100 fold to concentrations at which downstream responses are not predicted to be saturated, hypersensitive cells oriented their mating projections less precisely than wild-type cells orient in gradients of higher pheromone concentrations 3. These observations suggest that hypersensitive cells are inherently less able to respond distinguishably to different pheromone concentrations (i.e., transmit less information about pheromone concentration), even when they are responding to pheromone concentrations that saturate neither receptor nor downstream responses.
One characteristic of wild-type cells that we 5 and others 6 have previously found is that, despite the large number of intermediate signaling events in the system, the dose-response curve of receptor occupancy closely aligns with dose-responses curves of downstream system responses. For example, we observe “dose-response alignment” (here called DoRA) between receptor occupancy and the amount of pheromone-activated Ste12 (pathway subsystem output P, which is reporter gene expression corrected for inherent cell-to-cell differences in the ability to express proteins 5) (Fig. 2a). Interestingly, dose-response alignment is commonly observed in many mammalian cell signaling systems, including the insulin 7, acetylcholine 8, thyroid stimulating hormone 9, angiotensin II 10, and epidermal growth factor 11,12 response systems. Researchers in the past have often regarded alignment of curves for ligand binding by a candidate receptor and downstream response as evidence that the putative receptor was in fact the molecule that bound ligand and caused the cellular responses13–15. However, to our knowledge, researchers have investigated neither the implications of dose-response alignment for yeast pheromone response nor its general consequences for the function of cell signaling systems.
We realized that dose-response alignment improves information transmission in two ways. First, DoRA describes a linear relationship between receptor occupancy and downstream response; consequently, the entire range of receptor occupancies evenly corresponds to the entire range of possible responses (Fig. 2b). By contrast, even a modest dose-response misalignment, such as a 20-fold shift in the EC50 of downstream response (Fig. 2c), compresses the downstream responses corresponding to a wide range of receptor occupancies into a narrow range (Fig. 2d). Second, dose-response alignment minimizes the amplification of upstream noise (Fig. 2e). Previous analysis of noise propagation in a synthetic gene circuit revealed analogous amplification of upstream noise in a system with misaligned dose-responses16. This reasoning suggested to us that cell signaling systems with misaligned dose-responses inherently transmit information with lower fidelity, even if downstream responses are not saturated, an idea consistent Segall’s observations that hypersensitive cells oriented mating projections less precisely in gradients than wild-type cells even at concentrations that did not saturate downstream responses 3. We hypothesized that dose-response alignment might indicate a system that can transmit large amounts of information, and therefore we sought to better understand the underlying molecular mechanisms required for DoRA and the linear relationship between upstream and downstream response that it defines.
In the 1930s, Harold Black established that “proportional” negative feedback in electrical circuits, where a constant fraction of the output is subtracted from the input, can bring about a linear input-output voltage relationship17. Biologists have also shown that negative feedback can make input-output relationships more linear in biological systems; for example, Bhalla et al. showed that, in a MAPK/PKC-mediated signaling system, increasing the amount of a MAPK-activated phosphatase that inactivates the MAPK made the average output response more linearly related to (i.e., proportional to) the input18. Research in both biology and engineering 17,19–22 has also suggested or shown that negative feedback can increase the signal-to-noise ratio in system output and decrease the sensitivity of output to variation in properties of system components (see Supplementary Information 8 for further discussion). These observations suggested to us that negative feedback might mediate dose-response alignment and improve information transmission in the yeast pheromone response system.
Prior work showed that the pheromone response system quickly establishes dose-response alignment; the accumulation of reporter gene expression in cells increased linearly from 15 minutes to three hours after stimulation (see Fig. 2 in 5), and at all times the normalized dose-response of downstream output aligned with the receptor-ligand binding curve. These facts suggested that the molecular mechanisms that bring about and stabilize DoRA occurs in the first 15 minutes of pheromone stimulation. However, no quantitative measurements of system activities in this time frame existed to indicate the action of negative feedback to align dose-responses. We therefore developed tools to measure the early dynamics of molecular events that the system uses to operate before, during, and after establishment of DoRA.
We developed reporters and methods to measure real-time signal transmission in single cells, at the membrane, and in the nucleus, and supplemented these data with biochemical measurements. We then measured system outputs (i.e., system activities at different stages in the signaling pathway, see Fig. 1) after stimulating cells with 100 nM pheromone, a concentration that produces maximal downstream transcription reporter response (Fig. 2a).
Two membrane-proximal system outputs, G-protein activation and Ste5 recruitment to the membrane, peaked and declined rapidly. To follow G-protein activation in single cells over time, we measured loss of Fluorescence Resonance Energy Transfer (FRET) between cyan fluorescent protein (CFP)-tagged Gpa1 and yellow fluorescent protein (YFP)-tagged Ste18 by image cytometry 23 (Supplementary Information 2) in a derivative of a strain developed by Yi and coworkers6. Loss of G-protein FRET rapidly peaked in the first minute and declined (Fig. 3a and Fig. S2), consistent with lower time resolution, single time point population measurements in an earlier study 6.
We then measured, also in single cells and at sub-minute intervals, a subsequent membrane-proximal signaling event, the recruitment of Ste5 to the membrane. To do this, we measured the redistribution of YFP-Ste5 from the nucleus and cytosol to the membrane (Supplementary Information 2). Membrane recruitment of Ste5 was rapid (Fig 3b). Within 5 seconds of stimulation with high pheromone, individual cells showed an increase in yellow fluorescence at the cell membrane, and a corresponding depletion of fluorescence from the cell interior; no change in fluorescence was observed in unstimulated cells or cells with unlabeled Ste5 (Fig. S3). Compared with unstimulated cells, average membrane recruitment reached near-maximal values within seconds and peaked by 20 seconds, before declining toward a plateau in later minutes, similar to the dynamics of G-protein loss of FRET.
We then assessed intermediate system output further downstream by measuring the dynamics of MAPK activation. Using quantitative immunoblotting, we measured phosphorylation of Fus3 residues Thr180 and Tyr182, which is required for Fus3 activity and pheromone response 24. The amount of phosphorylated Fus3 relative to total Fus3 increased rapidly, reaching a maximum in 2.5 min before dropping to a plateau level in approximately 5–7 min (Fig. 3c and Fig. S4a–b).
We then measured nuclear MAPK activity dynamics in single cells over time. To do this, we developed a FRET reporter to measure pheromone-induced changes in the association between the transcription factor Ste12 and one of its inhibitors Dig1 25. We deleted native STE12 and DIG1 genes and chromosomally integrated versions of these proteins fused to CFP and YFP, respectively (see Supplementary Information 5). We then measured changes in FRET between CFP and YFP 26 in the nucleus by image cytometry 23 (see Fig. S6 and Supplementary Information 2). Pheromone-induced loss of FRET did not require new protein translation (Fig. S7b), but did require both Ste5 (Fig S7c) and MAP kinase activity (Fig S7d), consistent with the interpretation that loss of FRET directly reported pheromone-induced, MAP kinase-mediated derepression of Ste12.
Loss of Dig1-Ste12 FRET quickly peaked around 3 min after stimulation (Fig. 3d), and the overall signal dynamics were very similar to those of Fus3 phosphorylation (compare Fig. 3c to Fig. 3d, see also Fig. 3f). This fast signal transfer from Fus3 activation to Ste12 derepression is consistent with the idea that Fus3 moves quickly in and out of the nucleus, as shown in studies of changes in Fus3 localization by fluorescence recovery after photobleaching (FRAP)27. We confirmed that the timing of Ste12 de-repression measured by loss of Ste12-Dig1 FRET was consistent with the dynamics of pheromone-induced mRNA transcription. Using ribonuclease protection assays, we quantified FUS1 mRNA levels. FUS1 mRNA levels peaked at 5 minutes following pheromone stimulation before declining (Fig. 3e and Fig. S8). The maximum rate of increase in mRNA occurred between 3 and 5 minutes, consistent with the time of maximum loss of Ste12-Dig1 FRET.
All measurements of signal-relaying events showed a consistent pattern of rapid peak-and-decline toward a plateau after pheromone stimulation (Fig. 3f), which suggested the action of one or more fast-acting negative feedbacks that might modulate the dose-dependence of the signal to achieve DoRA.
A number of previous works suggested that the MAPKs Fus3 and Kss1 might mediate rapid negative feedback. Our previous study of regulated cell-to-cell variation in system output revealed a Fus3-dependent reduction in variation, suggesting an autoregulatory negative feedback mediated by Fus3 5. Gartner et al. showed that levels of phosphorylated Fus3 were higher in cells bearing a kinase-dead mutant version of Fus324. Bhattacharya et al. showed that Ste5 T287A mutant cells, in which the Ste5 carries a lesion in a site of threonine phoshorylated by Fus3 on peptides in vitro exhibited increased reporter expression 28, albeit with no change in the EC50 of the dose-response. Finally, phosphoproteomic studies of pheromone response system proteins29 have uncovered numerous sites of phosphorylation on pheromone response system proteins, whose levels change upon pheromone stimulation, many of which lie in consensus MAP kinase target sequences (R. Maxwell and O. Resnekov, personal communication). We therefore hypothesized that the signal decline at different measurement points depends on non-translational, fast-acting negative feedbacks mediated by Fus3 or Kss1.
To test if Fus3 or Kss1 were sources of negative feedback on system activity, we compared the baseline system response, at system points up to and including Fus3 phosphorylation, with system response after selective inhibition of either Fus3 or Kss1 kinase activity. To do this, we first modified reporter strains by replacing either FUS3 or KSS1 with the corresponding purine analog-sensitive allele 30. We did this by changing the “gatekeeper” residue in each kinase’s ATP binding pocket (Q93 in Fus3, N94 in Kss1) to an alanine. The mutant fus3-as2 and kss1-as2 kinases were active, as measured by fluorescent protein reporter gene output (Fig. S9a), and10 μM 1-NM-PP1, a cell-permeable adenosine analogue, inhibited the activity of mutant kinases without inhibiting wild-type kinases (Fig. S9b). We then quantified Fus3 phosphorylation by quantitative immunoblotting after stimulation with pheromone, either with or without simultaneous inhibition with 1-NM-PP1 (Fig. 4a). Fus3 phosphorylation levels did not peak and decline to a plateau when we inhibited Fus3-as2, but, rather, remained high, near peak levels. By contrast, when we inhibited Kss1-as2, Fus3 phosphorylation levels were unaffected (Fig. 4b). These results indicated that Fus3 kinase activity mediated one or more negative feedbacks in this system.
We then studied where in the system the Fus3-dependent feedback acted to diminish signal amplitude. Yi et al. showed that the decrease in G-protein FRET within 30 seconds of stimulation depended on Sst2 6. This finding suggested that the Fus3-dependent negative feedback might upregulate the GTPase-activating protein (GAP) function of Sst2, which would increase G protein reassociation and decrease downstream signal. We tested if Fus3-as2 inhibition affected the observed decline in both G-protein dissociation and Ste5 recruitment. Inhibition of Fus3 activity eliminated the decline in Ste5 translocation (Fig. 4c), but surprisingly had no effect on the decline in G-protein dissociation in a G-protein FRET reporter strain carrying fus3-as2 (Fig. 4d and Fig. S10). These results indicated that Fus3-mediated negative feedback acted downstream of mechanisms regulating G-protein association.
To confirm that Fus3 acted downstream of G-protein activation, we measured Ste5 recruitment after deleting SST2. We expected deletion of SST2 to have no effect on Fus3-mediated signal decline, since Sst2 is required for efficient G-protein inactivation and, as we showed above, Fus3-mediated negative feedback does not reduce G-protein dissociation levels. Unexpectedly, when we deleted of SST2, we completely disrupted Fus3-mediated signal decline; unlike SST2+ cells, inhibition of Fus3 did not cause an increase in Ste5 recruitment (Fig. 4c). Furthermore, the Ste5 recruitment (with or without Fus3-mediated feedback) peaked and declined, similar to the baseline response of SST2+ cells (compare squares and circles in Fig. 4e with circles in Fig. 4c). This finding showed that signal peak-and-decline is the default behavior in the absence of Sst2. Since a sustained non-declining signal is only evident in SST2+ cells in the presence of Fus3-as2 inhibitor, these results also indicate that Sst2 promotes Ste5 membrane recruitment, a hitherto unknown function of the RGS protein family, and that Fus3 negatively regulates this novel signal-promoting function (Fig. 5a).
We then investigated which portions of the Sst2 protein might be involved in promoting Ste5 membrane recruitment. During analysis of Sst2 point mutants, we found that Ste5 recruitment in a fus3-as2 strain that carried sst2-T134A instead of wild-type Sst2 peaked-and-declined in the presence and absence of Fus3 inhibitor (Fig. 4f), just as observed in Δsst2 cells (Fig. 4e). The pheromone-induced growth-inhibition of sst2-T134A cells reported by halo assays was close to wild-type (Fig. S11a), and the average number of Sst2-T134A protein molecules per cell was similar to Sst2 abundance in the parent strain, (Fig. S11b), suggesting that the T134A mutation disrupted a significant fraction of the Fus3-dependent, signal-promoting function of Sst2 without disrupting the bulk of its signal-reducing GAP activity. T134 lies within the N-terminal DEP domains of Sst2, which are required for localization of Sst2 to the membrane by binding the cytosolic tail of Ste2 31. These results indicate that the DEP domains in Sst2 might aid Ste5 membrane recruitment, perhaps by providing additional membrane-proximal interaction surfaces, and suggest that mechanisms that regulate localization of Sst2 to the membrane, such as disruption of Sst2-Ste2 interactions by Yck1/2-mediated phosphorylation after longer periods of pheromone stimulation 31, might consequently regulate Ste5 membrane recruitment.
Finally, we tested if dose-response alignment between receptor-pheromone binding and downstream activities required Fus3 activity. In principle, Fus3-mediated negative feedback might scale system activity by a dose-independent factor, and therefore cause no shift in the normalized dose-response curve. For example, the Ste5 T287A mutation increases the magnitude of system output relative to wild-type cells without changing the pheromone concentration yielding half-maximal response (see Fig. 5 in 28). We measured dose-responses of Fus3 phosphorylation in a fus3-as2 strain with and without inhibitor 15 minutes after pheromone stimulation, the time when the amount of Fus3 phosphorylation had declined to a steady-state level (Fig. 3c). Inhibiting Fus3 kinase activity shifted the dose-response of Fus3 activation, lowering the pheromone concentration needed for half maximal response by 20-fold (Fig. 5b). Moreover, inhibiting Fus3 kinase activity doubled the dynamic range of the output (Fig. 5c). These results showed that Fus3-mediated negative feedback was required for dose-response alignment in the yeast pheromone response system.
We found that MAPK Fus3 mediates rapid negative feedback that aligns the dose-responses of upstream and downstream system activities in the pheromone response system. We propose that dose-response alignment improves information transmission through this and other signaling systems. Furthermore, we found that Fus3 negatively regulates a novel signal-promoting function of the RGS protein Sst2. Our results demonstrate that RGS proteins, present in many eukaryotic signaling systems (the human RGS family, for example, contains more than 35 members 32), can function in signal transduction systems by increasing signal in addition to accelerating G-protein inactivation, possibly (as in the case of pheromone response) by facilitating recruitment of MAPK scaffolds to sites of activity.
The idea that dose-response alignment increases the amount of transmitted information has practical implications for drug discovery and design. For example, consider a drug that increased sensitivity of cells to a naturally occurring antagonist of cell proliferation, analogous to the downstream dose-response shift we observed upon Fus3 inhibition in the pheromone response system (Fig. 5b). Despite increasing the average sensitivity of cells to signals to stop growth, the dose-response misalignment could reduce the amount of transmitted information about the signal. The decrease in transmitted information could increase cell-to-cell variation in response, causing a larger number of cells fall below a threshold in antagonist response and continue proliferation. It is possible some existing drugs that allosterically modify GPCR signaling systems downstream of ligand binding (see 33, Fig. 3) and those that target mid-system signaling molecules such as PKC 34 and AKT 35 may decrease dose-response alignment and increase response variation, whereas drugs that specifically affect the affinity of receptor-ligand binding (see 33, Fig. 5) should not.
We propose here that the fidelity with which a cell responds to different input concentrations of a ligand depends on a “systems-level” quantitative behavior, dose-response alignment, found in many other cell signaling systems. For biological systems, a deeper understanding of key quantitative behaviors will likely depend on articulating appropriate analytical frameworks and metrics. Information theory36 defines a framework for quantifying the relationship between system input and output (see Supplementary Information 9 for further discussion), and has enabled researchers to quantify, for example, the amount of information that an axon of a single sensory neuron can transmit 37 and the amount of information about morphogen gradient that a transcription factor can transmit to a downstream effector 38,39. Much as concepts from classical electromagnetism provide rigorous means to describe and understand the determinants of behaviors of electrical circuits, we expect that concepts from information theory will enable more rigorous and quantitative understanding of how genes (and the proteins they encode) of more complicated signaling systems interact to sense and transmit information into the cell.
We constructed yeast strains and plasmids by standard methods 40,41 essentially as described (5 and Supplementary Information 1). By doctrine, we expressed all reporter constructs from native promoters integrated into the chromosome, and verified that the level of expressed protein was similar to the native level. With the exception of strains used for G-protein FRET experiments, we constructed all strains were from otherwise- isogenic bar1- W303a reference parent strain, ACL 379 5, by the steps described. We stimulated exponentially-growing cells with the indicated concentration of pheromone and/or other reagents (such as the inhibitor 1-NM-PP1) in one of two ways. For image cytometry, we affixed the cells to the bottom of wells in a glass-bottom 96-well plate, as described in 5 and in Supplementary Information 2.1. Using custom fluidic hardware, we evacuated medium from the well, injected fresh medium containing the indicated concentration of pheromone and/or inhibitor, and proceeded to record images over time. For MAPK phosphorylation, FUS1 mRNA, and flow cytometry experiments, we stimulated cells by using a micropipette to mix a small volume of pheromone and/or inhibitor into the cell suspension to the final concentration (as indicated, typically 100 nM pheromone and 10 μM 1-NM-PP1). We performed image acquisition essentially as described in 5,23, with modifications as detailed in Supplementary Information. For image cytometry, we extracted values for parameters of interest from images using Cell-ID 1.0 23. We analyzed image and flow cytometric data using Physics Analysis Workstation (PAW; see 42) and custom scripts, depending on the type of image, described in the text and in Supplementary Information. Supplementary Information contains further details on plasmids, strains, construction methods, materials, and experimental methods.
We thank Pia Abola, Steven Andrews, Adam Arkin, Mark Bowen, Linda Buck, Charles Denby, Alexander Gann, Deidre Meldrum, Tim Mitchison, Carl Pabo, Mark Ptashne, Michael Reese, Orna Resnekov, Chris Ryan, Michael Snyder, Ty Thomson, Annie E. Tsong, and Mark Wilson for discussions and/or comments on the manuscript, and Orna Resnekov for help in articulating the requirements for fluidic induction devices. Work, including that of Mark Holl at U. Washington, was supported by the Alpha Project at the Center for Quantitative Genome Function, an NIH Center of Excellence in Genomic Science under grant P50 HG02370 from the National Human Genome Research Institute to R.B.
R.Y. performed the Ste5 translocation, loss of G-protein and Dig1/Ste12 FRET, MAP kinase phosphorylation, mRNA measurements, and the image-based measurements of system output. G.P. and A.G. noted and helped articulate the relationship of negative feedback to dose response overlap. G.P. and R.Y. performed flow cytometric measurements. L.L. and A.G. contributed to valuable discussions about mutual information calculations and analysis. R.Y. and A.G. performed data analysis on loss of FRET experiments. A.C.-L. and A.G. carried out extensive initial measurements of Ste5 recruitment. K.B. provided unpublished information about protein quantification useful in initial discussions about dose-response alignment. R.Y. and D.P. performed numerous measurements verifying protein abundance in other strains. E.S. constructed the inhibitor-sensitive fus3-as2 and kss1-as2 alleles. M.H. designed and built fluidic devices used to induce the system in response to articulated requirements. R.B. provided input into project direction, experimental design, and interpretation of results. R.Y., G.P. and R.B. wrote the paper and guarantee the integrity of the results.
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