Negative feedback that improves information transmission in yeast signalling

Tissue-specific tumorigenesis: context matters

How can we treat cancer more effectively? Traditionally, tumours from the same anatomical site are treated as one tumour entity. This concept has been challenged by recent breakthroughs in cancer genomics and translational research that have enabled molecular tumour profiling. The identification and validation of cancer drivers that are shared between different tumour types, spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use, or within so-called basket or umbrella trials which are designed to test whether molecular alterations in one tumour entity can be extrapolated to all others. However, recent clinical and preclinical studies suggest that there are tissue- and cell type-specific differences in tumorigenesis and the organization of oncogenic signalling pathways. In this Opinion article, we focus on the molecular, cellular, systemic and environmental determinants of organ-specific tumorigenesis and the mechanisms of context-specific oncogenic signalling outputs. Investigation, recognition and in-depth biological understanding of these differences will be vital for the design of next-generation clinical trials and the implementation of molecularly guided cancer therapies in the future.

Spatial and temporal signal processing and decision making by MAPK pathways

Recent studies show that MAPK pathways perform exquisite spatial and temporal signal processing. This review discusses the mechanisms that process dynamic inputs into graded output responses, the role of positive and negative feedbacks, and feedforward regulation.

Mitogen-activated protein kinase (MAPK) pathways are conserved from yeast to man and regulate a variety of cellular processes, including proliferation and differentiation. Recent developments show how MAPK pathways perform exquisite spatial and temporal signal processing and underscores the importance of studying the dynamics of signaling pathways to understand their physiological response. The importance of dynamic mechanisms that process input signals into graded downstream responses has been demonstrated in the pheromone-induced and osmotic stress–induced MAPK pathways in yeast and in the mammalian extracellular signal-regulated kinase MAPK pathway. Particularly, recent studies in the yeast pheromone response have shown how positive feedback generates switches, negative feedback enables gradient detection, and coherent feedforward regulation underlies cellular memory. More generally, a new wave of quantitative single-cell studies has begun to elucidate how signaling dynamics determine cell physiology and represents a paradigm shift from descriptive to predictive biology.

Molecular noise can minimize the collective sensitivity of a clonal heterogeneous cell population

It is now widely accepted that molecular noise, rather than be always detrimental, introduces in many circumstances the required boost to reach fundamental cellular activities or strategies otherwise unattainable. In threshold-like genetic systems, molecular noise serves to generate heterogeneous responses in a clonal population, also in a tissue, due to cell-to-cell variability. Here, we derived a mathematical framework from which we could study in detail this effect. We focused on a minimal decision-making gene circuit implemented as a transcriptional positive-feedback loop. We evidenced that when the individual responses of each cell within the population are averaged, a sort of collective behavior, the resulting dose-response curve is linearized. In other words, the population is less sensitive than the individuals, which otherwise enhances the information transfer from signal to response. We found that the distance to the ideal linear response of the cell population is minimized for a particular noise level, and also characterized the interplay between intrinsic and extrinsic noise. Overall, our results highlight how cells could, by acting as a collective, entangle their genetic systems with their environments by adjusting the intracellular noise levels.

Yeast GPCR signaling reflects the fraction of occupied receptors, not the number

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.

Signaling Tug-of-War Delivers the Whole Message

How do cells transmit biochemical signals accurately? It turns out, pushing and pulling can go a long way.

Push-Pull and Feedback Mechanisms Can Align Signaling System Outputs with Inputs

Many cell signaling systems, including the yeast pheromone response system, exhibit "dose-response alignment" (DoRA), in which output of one or more downstream steps closely matches the fraction of occupied receptors. DoRA can improve the fidelity of transmitted dose information. Here, we searched systematically for biochemical network topologies that produced DoRA. Most networks, including many containing feedback and feedforward loops, could not produce DoRA. However, networks including "push-pull" mechanisms, in which the active form of a signaling species stimulates downstream activity and the nominally inactive form reduces downstream activity, enabled perfect DoRA. Networks containing feedbacks enabled DoRA, but only if they also compared feedback to input and adjusted output to match. Our results establish push-pull as a non-feedback mechanism to align output with variable input and maximize information transfer in signaling systems. They also suggest genetic approaches to determine whether particular signaling systems use feedback or push-pull control.

Genetic Redundancies Enhance Information Transfer in Noisy Regulatory Circuits

Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.

There is increasing evidence that the presence of molecular noise greatly influences function in biological systems. This could imply, for instance, that genetic circuits adopt particular architectures in order to reduce noise. On the other hand, noise can be beneficial. Here, we show that this could be the case for the functioning of analog to digital genetic devices, which are commonly found in cellular decision making situations. We use the framework of information theory to illustrate first how noise can enhance information transfer in these devices. In those regimes in which noise is detrimental, we discuss how genetic redundancies allow information to be maximized, and how this effect depends on the specifics of the devices, and the interdependence among them. These results provide overall an additional rationale for genetic redundancies in genomic systems.

Single-cell dynamics and variability of MAPK activity in a yeast differentiation pathway

In response to pheromones, yeast cells activate a MAPK pathway to direct processes important for mating, including gene induction, cell-cycle arrest, and polarized cell growth. Although a variety of assays have been able to elucidate signaling activities at multiple steps in the pathway, measurements of MAPK activity during the pheromone response have remained elusive, and our understanding of single-cell signaling behavior is incomplete. Using a yeast-optimized FRET-based mammalian Erk-activity reporter to monitor Fus3 and Kss1 activity in live yeast cells, we demonstrate that overall mating MAPK activity exhibits distinct temporal dynamics, rapid reversibility, and a graded dose dependence around the K_D of the receptor, where phenotypic transitions occur. The complex dose response was found to be largely a consequence of two feedbacks involving cyclin-mediated scaffold phosphorylation and Fus3 autoregulation. Distinct cell cycle-dependent response patterns comprised a large portion of the cell-to-cell variability at each dose, constituting the major source of extrinsic noise in coupling activity to downstream gene-expression responses. Additionally, we found diverse spatial MAPK activity patterns to emerge over time in cells undergoing default, gradient, and true mating responses. Furthermore, ramping up and rapid loss of activity were closely associated with zygote formation in mating-cell pairs, supporting a role for elevated MAPK activity in successful cell fusion and morphogenic reorganization. Altogether, these findings present a detailed view of spatiotemporal MAPK activity during the pheromone response, elucidating its role in mediating complex long-term developmental fates in a unicellular differentiation system.

Variable Dependence of Signaling Output on Agonist Occupancy of Ste2p, a G Protein-coupled Receptor in Yeast

We report here on the relationship between ligand binding and signaling responses in the yeast pheromone response pathway, a well characterized G protein-coupled receptor system. Responses to agonist (α-factor) by cells expressing widely varying numbers of receptors depend primarily on fractional occupancy, not the absolute number of agonist-bound receptors. Furthermore, the concentration of competitive antagonist required to inhibit α-factor-dependent signaling is more than 10-fold higher than predicted based on the known ligand affinities. Thus, responses to a particular number of agonist-bound receptors can vary greatly, depending on whether there are unoccupied or antagonist-bound receptors present on the same cell surface. This behavior does not appear to be due to pre-coupling of receptors to G protein or to the Sst2p regulator of G protein signaling. The results are consistent with a signaling response that is determined by the integration of positive signals from agonist-occupied receptors and inhibitory signals from unoccupied receptors, where the inhibitory signals can be diminished by antagonist binding.

Sensory input attenuation allows predictive sexual response in yeast

Animals are known to adjust their sexual behaviour depending on mate competition. Here we report similar regulation for mating behaviour in a sexual unicellular eukaryote, the budding yeast Saccharomyces cerevisiae. We demonstrate that pheromone-based communication between the two mating types, coupled to input attenuation by recipient cells, enables yeast to robustly monitor relative mate abundance (sex ratio) within a mixed population and to adjust their commitment to sexual reproduction in proportion to their estimated chances of successful mating. The mechanism of sex-ratio sensing relies on the diffusible peptidase Bar1, which is known to degrade the pheromone signal produced by mating partners. We further show that such a response to sexual competition within a population can optimize the fitness trade-off between the costs and benefits of mating response induction. Our study thus provides an adaptive explanation for the known molecular mechanism of pheromone degradation in yeast.

Cells of the yeast Saccharomyces cerevisiae can mate with other cells of opposite mating type. Here, the authors show that the combination of a pheromone and a pheromone-degrading enzyme allows yeast cells to monitor relative mate abundance within a population and adjust their commitment to sexual reproduction.

Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease.

Co-evolution of a new receptor-ligand pair will affect the downstream signal transduction network. Here, the authors use experimental evolution of yeast mating receptor Ste2 to show the effect of enhanced binding affinity and weakened interactions with the network's negative regulators on protein evolution.

Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks

Cellular decisions are made by complex networks that are difficult to analyze. Although it is common to analyze smaller sub-networks known as network motifs, it is unclear whether this is valid, because these motifs are embedded in complex larger networks. Here, we address the general question of modularity by examining the S. cerevisiae pheromone response. We demonstrate that the feedforward motif controlling the cell-cycle inhibitor Far1 is insulated from cell-cycle dynamics by the positive feedback switch that drives reentry to the cell cycle. Before cells switch on positive feedback, the feedforward motif model predicts the behavior of the larger network. Conversely, after the switch, the feedforward motif is dismantled and has no discernable effect on the cell cycle. When insulation is broken, the feedforward motif no longer predicts network behavior. This work illustrates how, despite the interconnectivity of networks, the activity of motifs can be insulated by switches that generate well-defined cellular states.

Protein expression patterns of the yeast mating response

Microfluidics, in combination with time-lapse microscopy, is a transformative technology that significantly enhances our ability to monitor and probe biological processes in living cells. However, high-throughput microfluidic devices mostly require sophisticated preparatory and setup work and are thus hard to adopt by non-experts. In this work, we designed an easy-to-use microfluidic chip, which enables tracking of 48 GFP-tagged yeast strains, with each strain under two different stimulus conditions, in a single experiment. We used this technology to investigate the dynamic pattern of protein expression during the yeast mating differentiation response. High doses of pheromone induce cell cycle arrest and the shmoo morphology, whereas low doses of pheromone lead to elongation and chemotrophic growth. By systematically analyzing the protein dynamics of 156 pheromone-regulated genes, we identified groups of genes that are preferentially induced in response to low-dose pheromone (elongation during growth) or high-dose pheromone (shmoo formation and cell cycle arrest). The protein dynamics of these genes may provide insights into the mechanisms underlying the differentiation switch induced by different doses of pheromone.

A Predictive Model for Yeast Cell Polarization in Pheromone Gradients

Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other.

Parallel feedback loops control the basal activity of the HOG MAPK signaling cascade

Tight regulation of the MAP kinase Hog1 is crucial for survival under changing osmotic conditions. Interestingly, we found that Hog1 phosphorylates multiple upstream components, implying feedback regulation within the signaling cascade. Taking advantage of an unexpected link between glucose availability and Hog1 activity, we used quantitative single cell measurements and computational modeling to unravel feedback regulation operating in addition to the well-known adaptation feedback triggered by glycerol accumulation. Indeed, we found that Hog1 phosphorylates its activating kinase Ssk2 on several sites, and cells expressing a non-phosphorylatable Ssk2 mutant are partially defective for feedback regulation and proper control of basal Hog1 activity. Together, our data suggest that Hog1 activity is controlled by intertwined regulatory mechanisms operating with varying kinetics, which together tune the Hog1 response to balance basal Hog1 activity and its steady-state level after adaptation to high osmolarity.

Information theory in systems biology. Part II: protein-protein interaction and signaling networks

By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.

The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks

Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation.

This work demonstrates how different sources of variability within biochemical networks impact the interpretation of information transmission. These sources are the intrinsic noise generated within the pathway of a single cell, variability due to initial conditions and/or global parameters across the population. A theoretical analysis of a simple signaling pathway and experimental exploration of a synthetic circuit are used to discuss the contributions of these sources of variability to information transmission using mutual information as a metric.

Variability in State-Dependent Plasticity of Intrinsic Properties during Cell-Autonomous Self-Regulation of Calcium Homeostasis in Hippocampal Model Neurons

How do neurons reconcile the maintenance of calcium homeostasis with perpetual switches in patterns of afferent activity? Here, we assessed state-dependent evolution of calcium homeostasis in a population of hippocampal pyramidal neuron models, through an adaptation of a recent study on stomatogastric ganglion neurons. Calcium homeostasis was set to emerge through cell-autonomous updates to 12 ionic conductances, responding to different types of synaptically driven afferent activity. We first assessed the impact of theta-frequency inputs on the evolution of ionic conductances toward maintenance of calcium homeostasis. Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models. Assessing the sensitivity of this form of plasticity, we noted that intrinsic neuronal properties and the firing response were sensitive to the target calcium concentration and to the strength and frequency of afferent activity. Next, we studied the evolution of calcium homeostasis when afferent activity was switched, in different temporal sequences, between two behaviorally distinct types of activity: theta-frequency inputs and sharp-wave ripples riding on largely silent periods. We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity. These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

Modulation of receptor dynamics by the regulator of G protein signaling Sst2

G protein–coupled receptor signaling is negatively regulated by both receptor internalization and regulator of G protein signaling (RGS) protein–stimulated inactivation of the G protein. The RGS protein can also positively regulate receptor signaling by binding to the receptor, thereby reducing receptor internalization.

G protein–coupled receptor (GPCR) signaling is fundamental to physiological processes such as vision, the immune response, and wound healing. In the budding yeast Saccharomyces cerevisiae, GPCRs detect and respond to gradients of pheromone during mating. After pheromone stimulation, the GPCR Ste2 is removed from the cell membrane, and new receptors are delivered to the growing edge. The regulator of G protein signaling (RGS) protein Sst2 acts by accelerating GTP hydrolysis and facilitating pathway desensitization. Sst2 is also known to interact with the receptor Ste2. Here we show that Sst2 is required for proper receptor recovery at the growing edge of pheromone-stimulated cells. Mathematical modeling suggested pheromone-induced synthesis of Sst2 together with its interaction with the receptor function to reestablish a receptor pool at the site of polarized growth. To validate the model, we used targeted genetic perturbations to selectively disrupt key properties of Sst2 and its induction by pheromone. Together our results reveal that a regulator of G protein signaling can also regulate the G protein–coupled receptor. Whereas Sst2 negatively regulates G protein signaling, it acts in a positive manner to promote receptor retention at the growing edge.

Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance

BACKGROUND

Psoriasis is a chronic disease characterized by the development of scaly red skin lesions and possible co-morbid conditions. The psoriasis lesional skin transcriptome has been extensively investigated, but mRNA levels do not necessarily reflect protein abundance. The purpose of this study was therefore to compare differential expression patterns of mRNA and protein in psoriasis lesions.

METHODS

Lesional (PP) and uninvolved (PN) skin samples from 14 patients were analyzed using high-throughput complementary DNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

We identified 4122 differentially expressed genes (DEGs) along with 748 differentially expressed proteins (DEPs). Global shifts in mRNA were modestly correlated with changes in protein abundance (r = 0.40). We identified similar numbers of increased and decreased DEGs, but 4-fold more increased than decreased DEPs. Ribosomal subunit and translation proteins were elevated within lesions, without a corresponding shift in mRNA expression (RPL3, RPS8, RPL11). We identified 209 differentially expressed genes/proteins (DEGPs) with corresponding trends at the transcriptome and proteome levels. Most DEGPs were similarly altered in at least one other skin disease. Psoriasis-specific and non-specific DEGPs had distinct cytokine-response patterns, with only the former showing disproportionate induction by IL-17A in cultured keratinocytes.

CONCLUSIONS

Our findings reveal global imbalance between the number of increased and decreased proteins in psoriasis lesions, consistent with heightened translation. This effect could not have been discerned from mRNA profiling data alone. High-confidence DEGPs were identified through transcriptome-proteome integration. By distinguishing between psoriasis-specific and non-specific DEGPs, our analysis uncovered new functional insights that would otherwise have been overlooked.

Accurate encoding and decoding by single cells: amplitude versus frequency modulation

Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms.

Signals, and hence information, can generally be transmitted either by amplitude (AM) or frequency (FM) modulation, as used, for example, in the transmission of radio waves since the 1930s. Both types of modulation are known to play a role in biology with AM conventionally associated with signaling and gene expression, and FM used to reliably transmit electrical signals over large distances between neurons. Surprisingly, FM was recently also observed in gene regulation, making their roles less distinct than previously thought. Although the engineering advantages and disadvantages of AM and FM are well understood, the equivalent question in biological systems is still largely unsolved. Here, we propose a simple model of signaling by receptors (or ion channels) with subsequent gene regulation, thus implementing both AM and FM in different types of biological pathways. We then compare the accuracy in the production of target proteins. We find that FM can be more accurate than AM only for a single receptor with fast signaling, whereas AM is more accurate in slow gene regulation and with signaling by multiple receptors. Finally, we propose possible reasons that cells use FM despite the potential decrease in accuracy.

Compartmentalization of a bistable switch enables memory to cross a feedback-driven transition

Cells make accurate decisions in the face of molecular noise and environmental fluctuations by relying not only on present pathway activity, but also on their memory of past signaling dynamics. Once a decision is made, cellular transitions are often rapid and switch-like due to positive feedback loops in the regulatory network. While positive feedback loops are good at promoting switch-like transitions, they are not expected to retain information to inform subsequent decisions. However, this expectation is based on our current understanding of network motifs that accounts for temporal, but not spatial, dynamics. Here, we show how spatial organization of the feedback-driven yeast G1/S switch enables the transmission of memory of past pheromone exposure across this transition. We expect this to be one of many examples where the exquisite spatial organization of the eukaryotic cell enables previously well-characterized network motifs to perform new and unexpected signal processing functions.

Optimal census by quorum sensing

Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.

Bacteria regulate gene expression in response to changes in cell density in a process called quorum sensing. To synchronize their gene-expression programs, these bacteria need to glean as much information as possible about their cell density. Our study is the first to physically model the flow of information in a quorum-sensing microbial community, wherein the internal regulator of the individuals response tracks the external cell density via an endogenously generated shared signal. Combining information theory and Lagrangian formalism, we find that quorum-sensing systems can improve their information capabilities by tuning circuit feedbacks. Our analysis suggests that achieving information benefit via feedback requires dedicated systems to control gene expression noise, such as sRNA-based regulation.

Systems biology. Accurate information transmission through dynamic biochemical signaling networks

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyzed the ability of temporal signal modulation--that is, dynamics--to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca(2+)), and nuclear factor kappa-B (NF-κB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to nondynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise levels confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise-induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks.

Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast

Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.

Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters

Experimental measurements of biochemical noise have primarily focused on sources of noise at the gene expression level due to limitations of existing noise decomposition techniques. Here, we introduce a mathematical framework that extends classical extrinsic-intrinsic noise analysis and enables mapping of noise within upstream signaling networks free of such restrictions. The framework applies to systems for which the responses of interest are linearly correlated on average, although the framework can be easily generalized to the nonlinear case. Interestingly, despite the high degree of complexity and nonlinearity of most mammalian signaling networks, three distinct tumor necrosis factor (TNF) signaling network branches displayed linearly correlated responses, in both wild-type and perturbed versions of the network, across multiple orders of magnitude of ligand concentration. Using the noise mapping analysis, we find that the c-Jun N-terminal kinase (JNK) pathway generates higher noise than the NF-κB pathway, whereas the activation of c-Jun adds a greater amount of noise than the activation of ATF-2. In addition, we find that the A20 protein can suppress noise in the activation of ATF-2 by separately inhibiting the TNF receptor complex and JNK pathway through a negative feedback mechanism. These results, easily scalable to larger and more complex networks, pave the way toward assessing how noise propagates through cellular signaling pathways and create a foundation on which we can further investigate the relationship between signaling system architecture and biological noise.

A single mechanism can explain network-wide insulin resistance in adipocytes from obese patients with type 2 diabetes

The response to insulin is impaired in type 2 diabetes. Much information is available about insulin signaling, but understanding of the cellular mechanisms causing impaired signaling and insulin resistance is hampered by fragmented data, mainly obtained from different cell lines and animals. We have collected quantitative and systems-wide dynamic data on insulin signaling in primary adipocytes and compared cells isolated from healthy and diabetic individuals. Mathematical modeling and experimental verification identified mechanisms of insulin control of the MAPKs ERK1/2. We found that in human adipocytes, insulin stimulates phosphorylation of the ribosomal protein S6 and hence protein synthesis about equally via ERK1/2 and mTORC1. Using mathematical modeling, we examined the signaling network as a whole and show that a single mechanism can explain the insulin resistance of type 2 diabetes throughout the network, involving signaling both through IRS1, PKB, and mTOR and via ERK1/2 to the nuclear transcription factor Elk1. The most important part of the insulin resistance mechanism is an attenuated feedback from the protein kinase mTORC1 to IRS1, which spreads signal attenuation to all parts of the insulin signaling network. Experimental inhibition of mTORC1 using rapamycin in adipocytes from non-diabetic individuals induced and thus confirmed the predicted network-wide insulin resistance.

Measuring and sorting cell populations expressing isospectral fluorescent proteins with different fluorescence lifetimes

Study of signal transduction in live cells benefits from the ability to visualize and quantify light emitted by fluorescent proteins (XFPs) fused to different signaling proteins. However, because cell signaling proteins are often present in small numbers, and because the XFPs themselves are poor fluorophores, the amount of emitted light, and the observable signal in these studies, is often small. An XFP's fluorescence lifetime contains additional information about the immediate environment of the fluorophore that can augment the information from its weak light signal. Here, we constructed and expressed in Saccharomyces cerevisiae variants of Teal Fluorescent Protein (TFP) and Citrine that were isospectral but had shorter fluorescence lifetimes, ∼1.5 ns vs ∼3 ns. We modified microscopic and flow cytometric instruments to measure fluorescence lifetimes in live cells. We developed digital hardware and a measure of lifetime called a “pseudophasor” that we could compute quickly enough to permit sorting by lifetime in flow. We used these abilities to sort mixtures of cells expressing TFP and the short-lifetime TFP variant into subpopulations that were respectively 97% and 94% pure. This work demonstrates the feasibility of using information about fluorescence lifetime to help quantify cell signaling in living cells at the high throughput provided by flow cytometry. Moreover, it demonstrates the feasibility of isolating and recovering subpopulations of cells with different XFP lifetimes for subsequent experimentation.

Ultrasensitivity part I: Michaelian responses and zero-order ultrasensitivity

Quantitative studies of signal transduction systems have shown that ultrasensitive responses - switch-like, sigmoidal input/output relationships - are commonplace in cell signaling. Ultrasensitivity is important for various complex signaling systems, including signaling cascades, bistable switches, and oscillators. In this first installment of a series on ultrasensitivity we survey the occurrence of ultrasensitive responses in signaling systems. We review why the simplest mass action systems exhibit Michaelian responses, and then move on to zero-order ultrasensitivity, a phenomenon that occurs when signaling proteins are operating near saturation. We also discuss the physiological relevance of zero-order ultrasensitivity to cellular regulation.

Utilization of extracellular information before ligand-receptor binding reaches equilibrium expands and shifts the input dynamic range

Cell signaling systems sense and respond to ligands that bind cell surface receptors. These systems often respond to changes in the concentration of extracellular ligand more rapidly than the ligand equilibrates with its receptor. We demonstrate, by modeling and experiment, a general "systems level" mechanism cells use to take advantage of the information present in the early signal, before receptor binding reaches a new steady state. This mechanism, pre-equilibrium sensing and signaling (PRESS), operates in signaling systems in which the kinetics of ligand-receptor binding are slower than the downstream signaling steps, and it typically involves transient activation of a downstream step. In the systems where it operates, PRESS expands and shifts the input dynamic range, allowing cells to make different responses to ligand concentrations so high as to be otherwise indistinguishable. Specifically, we show that PRESS applies to the yeast directional polarization in response to pheromone gradients. Consideration of preexisting kinetic data for ligand-receptor interactions suggests that PRESS operates in many cell signaling systems throughout biology. The same mechanism may also operate at other levels in signaling systems in which a slow activation step couples to a faster downstream step.

Gene regulation: when analog beats digital

Why do some genes seem to respond in a 'digital', on/off manner to a graded signal, while others produce an 'analog', graded response? A new study suggests that the DNA-binding properties of transcription factors can strongly influence the response patterns of gene networks.

Nonlinear biochemical signal processing via noise propagation

Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced ultra-sensitivity. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.

A mathematical model for a T cell fate decision algorithm during immune response

We formulate and analyze an algorithm of cell fate decision that describes the way in which division vs. apoptosis choices are made by individual T cells during an infection. Such model involves a minimal number of known biochemical mechanisms: it basically relies on the interplay between cell division and cell death inhibitors on one hand, and membrane receptors on the other. In spite of its simplicity, the proposed decision algorithm is able to account for some significant facts in immune response. At the individual level, the existence of T cells that continue to replicate in the absence of antigen and the possible occurrence of T cell apoptosis in the presence of antigen are predicted by the model. Moreover, the latter is shown to yield an emergent collective behavior, the observed delay in clonal contraction with respect to the end of antigen stimulation, which is shown to arise just from individual T cell decisions made according to the proposed mechanism.

Secreting and sensing the same molecule allows cells to achieve versatile social behaviors

Cells that secrete and sense the same signaling molecule are ubiquitous. To uncover the functional capabilities of the core "secrete-and-sense" circuit motif shared by these cells, we engineered yeast to secrete and sense the mating pheromone. Perturbing each circuit element revealed parameters that control the degree to which the cell communicated with itself versus with its neighbors. This tunable interplay of self-communication and neighbor communication enables cells to span a diverse repertoire of cellular behaviors. These include a cell being asocial by responding only to itself and social through quorum sensing, and an isogenic population of cells splitting into social and asocial subpopulations. A mathematical model explained these behaviors. The versatility of the secrete-and-sense circuit motif may explain its recurrence across species.

Fluorescent approaches for understanding interactions of ligands with G protein coupled receptors

G protein coupled receptors are responsible for a wide variety of signaling responses in diverse cell types. Despite major advances in the determination of structures of this class of receptors, the underlying mechanisms by which binding of different types of ligands specifically elicits particular signaling responses remain unclear. The use of fluorescence spectroscopy can provide important information about the process of ligand binding and ligand dependent conformational changes in receptors, especially kinetic aspects of these processes that can be difficult to extract from X-ray structures. We present an overview of the extensive array of fluorescent ligands that have been used in studies of G protein coupled receptors and describe spectroscopic approaches for assaying binding and probing the environment of receptor-bound ligands with particular attention to examples involving yeast pheromone receptors. In addition, we discuss the use of fluorescence spectroscopy for detecting and characterizing conformational changes in receptors induced by the binding of ligands. Such studies have provided strong evidence for diversity of receptor conformations elicited by different ligands, consistent with the idea that GPCRs are not simple on and off switches. This diversity of states constitutes an underlying mechanistic basis for biased agonism, the observation that different stimuli can produce different responses from a single receptor. It is likely that continued technical advances will allow fluorescence spectroscopy to play an important role in continued probing of structural transitions in G protein coupled receptors. This article is part of a Special Issue entitled: Structural and biophysical characterisation of membrane protein-ligand binding.

Mosaic physiology from developmental noise: within-organism physiological diversity as an alternative to phenotypic plasticity and phenotypic flexibility

A key problem in organismal biology is to explain the origins of functional diversity. In the context of organismal biology, functional diversity describes the set of phenotypes, across scales of biological organization and through time, that a single genotype, or genome, or organism, can produce. Functional diversity encompasses many phenomena: differences in cell types within organisms; physiological and morphological differences among tissues and organs; differences in performance; morphological shifts in external phenotype; and changes in behavior. How can single genomes produce so many different phenotypes? Modern biology proposes two general mechanisms. The first is developmental programs, by which single cells and their single genomes diversify, via relatively deterministic processes, into the sets of cell types, tissues and organs that we see in most multicellular organisms. The second general mechanism is phenotypic modification stemming from interactions between organisms and their environments – modifications known either as phenotypic plasticity or as phenotypic flexibility, depending on the time scale of the response and the degree of reversibility. These two diversity-generating mechanisms are related because phenotypic modifications may sometimes arise as a consequence of environments influencing developmental programs. Here, I propose that functional diversity also arises via a third fundamental mechanism: stochastic developmental events giving rise to mosaics of physiological diversity within individual organisms. In biological systems, stochasticity stems from the inherently random actions of small numbers of molecules interacting with one another. Although stochastic effects occur in many biological contexts, available evidence suggests that they can be especially important in gene networks, specifically as a consequence of low transcript numbers in individual cells. I briefly review known mechanisms by which organisms control such stochasticity, and how they may use it to create adaptive functional diversity. I then fold this idea into modern thinking on phenotypic plasticity and flexibility, proposing that multicellular organisms exhibit ‘mosaic physiology’. Mosaic physiology refers to sets of diversified phenotypes, within individual organisms, that carry out related functions at the same time, but that are distributed in space. Mosaic physiology arises from stochasticity-driven differentiation of cells, early during cell diversification, which is then amplified by cell division and growth into macroscopic phenotypic modules (cells, tissues, organs) making up the physiological systems of later life stages. Mosaic physiology provides a set of standing, diversified phenotypes, within single organisms, that raise the likelihood of the organism coping well with novel environmental challenges. These diversified phenotypes can be distinct, akin to polyphenisms at the organismal level; or they can be continuously distributed, creating a kind of standing, simultaneously expressed reaction norm of physiological capacities.

Identification of the molecular mechanisms for cell-fate selection in budding yeast through mathematical modeling

The specification and maintenance of cell fates is essential to the development of multicellular organisms. However, the precise molecular mechanisms in cell fate selection are, to our knowledge, poorly understood due to the complexity of multiple interconnected pathways. In this study, model-based quantitative analysis is used to explore how to maintain distinguished cell fates between cell-cycle commitment and mating arrest in budding yeast. We develop a full mathematical model of an interlinked regulatory network based on the available experimental data. By theoretically defining the Start transition point, the model is able to reproduce many experimental observations of the dynamical behaviors in wild-type cells as well as in Ste5-8A and Far1-S87A mutants. Furthermore, we demonstrate that a moderate ratio between Cln1/2→Far1 inhibition and Cln1/2→Ste5 inhibition is required to ensure a successful switch between different cell fates. We also show that the different ratios of the mutual Cln1/2 and Far1 inhibition determine the different cell fates. In addition, based on a new, definition of network entropy, we find that the Start point in wild-type cells coincides with the system's point of maximum entropy. This result indicates that Start is a transition point in the network entropy. Therefore, we theoretically explain the Start point from a network dynamics standpoint. Moreover, we analyze the biological bistablity of our model through bifurcation analysis. We find that the Cln1/2 and Cln3 production rates and the nonlinearity of SBF regulation on Cln1/2 production are potential determinants for irreversible entry into a new cell fate. Finally, the quantitative computations further reveal that high specificity and fidelity of the cell-cycle and mating pathways can guarantee specific cell-fate selection. These findings show that quantitative analysis and simulations with a mathematical model are useful tools for understanding the molecular mechanisms in cell-fate decisions.

Determinants of cell-to-cell variability in protein kinase signaling

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds (‘pathway sensitivity’) and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Cells sense their surroundings and respond to soluble factors in the extracellular space. Extracellular factors frequently induce heterogeneous responses, thereby restricting the biological outcome to a fraction of the cell population. However, the question arises how such cell-to-cell variability can be controlled, because some cellular systems show a very homogenous response at a defined level of an extracellular stimulus. We derived an analytical framework to systematically characterize the cell-to-cell variability of intracellular signaling pathways which transduce external signals. We analyzed how heterogeneity arises from fluctuations in the total concentrations of signaling proteins because this is the main source of variability in eukaryotic systems. We find that signaling pathways can be highly variable or inherently invariant, depending on the kinetic parameters and the structural features of the cascade. Our results indicate that the cell-to-cell variability can be reduced by negative feedback in the cascade or by signaling crosstalk between parallel pathways. We precisely define the role of negative feedback loops in variability suppression, and show that different aspects of the dose-response curve can be controlled, depending on the feedback kinetics and site of action in the cascade. This work constitutes a first step towards a systematic understanding of cell-to-cell variability in signal transduction.

Msn2 coordinates a stoichiometric gene expression program

BACKGROUND

Many cellular processes operate in an "analog" regime in which the magnitude of the response is precisely tailored to the intensity of the stimulus. In order to maintain the coherence of such responses, the cell must provide for proportional expression of multiple target genes across a wide dynamic range of induction states. Our understanding of the strategies used to achieve graded gene regulation is limited.

RESULTS

In this work, we document a relationship between stress-responsive gene expression and the transcription factor Msn2 that is graded over a large range of Msn2 concentrations. We use computational modeling and in vivo and in vitro analyses to dissect the roots of this relationship. Our studies reveal a simple and general strategy based on noncooperative low-affinity interactions between Msn2 and its cognate binding sites as well as competition over a large number of Msn2 binding sites in the genome relative to the number of Msn2 molecules.

CONCLUSIONS

In addition to enabling precise tuning of gene expression to the state of the environment, this strategy ensures colinear activation of target genes, allowing for stoichiometric expression of large groups of genes without extensive promoter tuning. Furthermore, such a strategy enables precise modulation of the activity of any given promoter by addition of binding sites without altering the qualitative relationship between different genes in a regulon. This feature renders a given regulon highly "evolvable."

Assessing cytokines' talking patterns following experimental myocardial damage by applying Shannon's information theory

BACKGROUND

The simultaneous measurement of multiple cytokines in parallel by using multiplex proteome arrays (MPA) is of great interest to understanding the inflammatory response following myocardial infarction; however, since cytokines are pleiotropic and redundant, increase of information throughput (IT) attained by measuring multiple cytokines remain to be determined. We aimed this study to assess the IT of an MPA system designed to assess 8 cytokines - commercially available at the time of the study - serum levels, before (control state) and after experimental myocardial cryoinjury (activated state) in rats.

METHODS

By assuming that redundant information do not generally increase the IT, we derived Entropy (H) and Redundancy (R) of information by using formulas of Shannon modified accordingly, where a high IT (high H and low R) corresponds to a low level of correlation between cytokines and vice versa for a low IT. The maximum theoretical level of IT and the contribution of each cytokine were also estimated.

RESULTS

In control state, no significant correlations were found between cytokines showing high IT; on the contrary, in activated state, several significant correlations were found supporting a complex cross-talk pattern between cytokines with low IT. Using as reference the maximum theoretical level of IT, in activated state, H was reduced of 67.0% and R was increased of 77.4% supporting a reduction of IT. Furthermore, the contribution of individual cytokines to H value of MPA was variable: in control state, IL-2 gave the most contribution to H value, conversely during activated state IL-10 gave most contribution. Finally during activated state, IL-1β was the only cytokine strongly correlated with values of all other cytokines, suggesting a crucial role in the inflammatory cascade.

CONCLUSIONS

Paradoxically, by analyzing an MPA system designed for redundant analytes such as cytokines, translating the Shannon's information theory from the field of communication to biology, the IT system in our model deteriorates during the activation state by increasing its redundancy, showing maximum value of entropy in the control conditions. Finally, the study of the mutual interdependence between cytokines by the contribution to the IT may allow formulating alternative models to describe the inflammatory cascade after myocardial infarction.

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes

Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks.

Intracellular signaling networks are central to a cell's ability to adapt to its environment. Developing the capacity to effectively manipulate such networks would have a wide range of applications, from cancer therapy to synthetic biology. This requires a thorough understanding of the mechanisms of signal transduction, particularly the kinds of protein complexes that are formed during transmission of extracellular information to the nucleus. Traditionally, signaling complexes have been largely perceived (albeit often implicitly) as machine-like structures. However, the number of molecular complexes that could theoretically be formed by complex signaling networks is astronomically large. This has led to the pleiomorphic ensemble hypothesis, which posits that diverse and rapidly changing sets of transient protein complexes can transmit and process information. Our goal was to use computational approaches, specifically rule-based modeling, to test these hypotheses. We constructed a model of the prototypical yeast mating pathway and found significant ensemble-like behavior. Our results thus demonstrated that ensembles can in fact transmit extracellular signals with minimal noise. Additionally, a comparison of this model with one tailored to generate machine-like complexes displayed notable phenotypic differences, revealing potential advantages for ensemble-like signaling. Our demonstration that ensembles can function effectively will have a significant impact on how we conceptualize signaling and other processes inside cells.

Single-cell measurements of enzyme levels as a predictive tool for cellular fates during organic acid production

Organic acids derived from engineered microbes can replace fossil-derived chemicals in many applications. Fungal hosts are preferred for organic acid production because they tolerate lignocellulosic hydrolysates and low pH, allowing economic production and recovery of the free acid. However, cell death caused by cytosolic acidification constrains productivity. Cytosolic acidification affects cells asynchronously, suggesting that there is an underlying cell-to-cell heterogeneity in acid productivity and/or in resistance to toxicity. We used fluorescence microscopy to investigate the relationship between enzyme concentration, cytosolic pH, and viability at the single-cell level in Saccharomyces cerevisiae engineered to synthesize xylonic acid. We found that cultures producing xylonic acid accumulate cells with cytosolic pH below 5 (referred to here as "acidified"). Using live-cell time courses, we found that the probability of acidification was related to the initial levels of xylose dehydrogenase and sharply increased from 0.2 to 0.8 with just a 60% increase in enzyme abundance (Hill coefficient, >6). This "switch-like" relationship likely results from an enzyme level threshold above which the produced acid overwhelms the cell's pH buffering capacity. Consistent with this hypothesis, we showed that expression of xylose dehydrogenase from a chromosomal locus yields ∼20 times fewer acidified cells and ∼2-fold more xylonic acid relative to expression of the enzyme from a plasmid with variable copy number. These results suggest that strategies that further reduce cell-to-cell heterogeneity in enzyme levels could result in additional gains in xylonic acid productivity. Our results demonstrate a generalizable approach that takes advantage of the cell-to-cell variation of a clonal population to uncover causal relationships in the toxicity of engineered pathways.

Robustness and compensation of information transmission of signaling pathways

Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells.

Quantitative measurement of protein relocalization in live cells

Microscope cytometry provides a powerful means to study signaling in live cells. Here we present a quantitative method to measure protein relocalization over time, which reports the absolute fraction of a tagged protein in each compartment. Using this method, we studied an essential step in the early propagation of the pheromone signal in Saccharomyces cerevisiae: recruitment to the membrane of the scaffold Ste5 by activated Gβγ dimers. We found that the dose response of Ste5 recruitment is graded (EC50 = 0.44 ± 0.08 nM, Hill coefficient = 0.8 ± 0.1). Then, we determined the effective dissociation constant (K(de)) between Ste5 and membrane sites during the first few minutes when the negative feedback from the MAPK Fus3 is first activated. K(de) changed during the first minutes from a high affinity of < 0.65 nM to a steady-state value of 17 ± 9 nM. During the same period, the total number of binding sites decreased slightly, from 1940 ± 150 to 1400 ± 200. This work shows how careful quantification of a protein relocalization dynamic can give insight into the regulation mechanisms of a biological system.

Signaling to extracellular signal-regulated kinase from ErbB1 kinase and protein kinase C: feedback, heterogeneity, and gating

Many extracellular signals act via the Raf/MEK/ERK cascade in which kinetics, cell-cell variability, and sensitivity of the ERK response can all influence cell fate. Here we used automated microscopy to explore the effects of ERK-mediated negative feedback on these attributes in cells expressing endogenous ERK or ERK2-GFP reporters. We studied acute rather than chronic stimulation with either epidermal growth factor (ErbB1 activation) or phorbol 12,13-dibutyrate (PKC activation). In unstimulated cells, ERK-mediated negative feedback reduced the population-average and cell-cell variability of the level of activated ppERK and increased its robustness to changes in ERK expression. In stimulated cells, negative feedback (evident between 5 min and 4 h) also reduced average levels and variability of phosphorylated ERK (ppERK) without altering the “gradedness” or sensitivity of the response. Binning cells according to total ERK expression revealed, strikingly, that maximal ppERK responses initially occur at submaximal ERK levels and that this non-monotonic relationship changes to an increasing, monotonic one within 15 min. These phenomena occur in HeLa cells and MCF7 breast cancer cells and in the presence and absence of ERK-mediated negative feedback. They were best modeled assuming distributive (rather than processive) activation. Thus, we have uncovered a novel, time-dependent change in the relationship between total ERK and ppERK levels that persists without negative feedback. This change makes acute response kinetics dependent on ERK level and provides a “gating” or control mechanism in which the interplay between stimulus duration and the distribution of ERK expression across cells could modulate the proportion of cells that respond to stimulation.

Modelling the activation of alkaline pH response transcription factor PacC in Aspergillus nidulans: involvement of a negative feedback loop

Alkaline pH adaptation represents an important environmental stress response in Aspergillus nidulans. It is mediated by the pal signalling pathway and the PacC transcription factor. Although studied extensively experimentally, the activation mechanism of PacC has not been quantified, and it is not clear how this activation is regulated. Here, by constructing mathematical models, we first show that the pattern of PacC activation observed in previously published experiments cannot be explained based on existing knowledge about PacC activation. Extending the model with a negative feedback loop is necessary to produce simulation results that are consistent with the data, suggesting the existence of a negative feedback loop in the PacC activation process. This extended model is then validated against published measurements for cells with drug treatment and mutant cells. Furthermore, we investigate the role of an intermediate form of PacC in the PacC activation process, and propose experiments that can be used to test our predictions. Our work illustrates how mathematical models can be used to uncover regulatory mechanisms in the transcription regulation, and generate hypotheses that guide further laboratory investigations.

Feedforward regulation ensures stability and rapid reversibility of a cellular state

Cellular transitions are important for all life. Such transitions, including cell fate decisions, often employ positive feedback regulation to establish and stabilize new cellular states. However, positive feedback is unlikely to underlie stable cell-cycle arrest in yeast exposed to mating pheromone because the signaling pathway is linear, rather than bistable, over a broad range of extracellular pheromone concentration. We show that the stability of the pheromone-arrested state results from coherent feedforward regulation of the cell-cycle inhibitor Far1. This network motif is effectively isolated from the more complex regulatory network in which it is embedded. Fast regulation of Far1 by phosphorylation allows rapid cell-cycle arrest and reentry, whereas slow Far1 synthesis reinforces arrest. We expect coherent feedforward regulation to be frequently implemented at reversible cellular transitions because this network motif can achieve the ostensibly conflicting aims of arrest stability and rapid reversibility without loss of signaling information.