A quantitative characterization of the yeast heterotrimeric G protein cycle

Comparative mathematical modeling reveals the differential effects of high-fat diet and ketogenic diet on the PI3K-Akt signaling pathway in heart

BACKGROUND

Obesity is a global health concern associated with increased risk of diseases like cardiovascular conditions including ischemic heart disease, a leading cause of mortality. The ketogenic diet (KD) has potential therapeutic applications in managing obesity and related disorders. However, the intricate effects of KD on diverse physiological conditions remain incompletely understood. The PI3K-Akt signaling pathway is critical for heart health, and its dysregulation implicates numerous cardiac diseases.

METHODS

We developed comprehensive mathematical models of the PI3K-Akt signaling pathway under high-fat diet (HFD) and KD conditions to elucidate their differential impacts and quantify apoptosis. Simulations and sensitivity analysis were performed.

RESULTS

Simulations demonstrate that KD can reduce the activation of key molecules like Erk and Trp53 to mitigate apoptosis compared to HFD. Findings align with experimental data, highlighting the potential cardiac benefits of KD. Sensitivity analysis identifies regulators like Trp53 and Bcl2l1 that critically influence apoptosis under HFD.

CONCLUSIONS

Mathematical modeling provides quantitative insights into the contrasting effects of HFD and KD on cardiac PI3K-Akt signaling and apoptosis. Findings have implications for precision nutrition and developing novel therapeutic strategies to address obesity-related cardiovascular diseases.

Kinetic Model for the Desensitization of G Protein-Coupled Receptor

Desensitization of G-protein-coupled receptors (GPCR) is a general regulatory mechanism adopted by biological organisms against overstimulation of G protein signaling. Although the details of the mechanism are extensively studied, it is not easy to gain an overarching understanding of the process constituted by a multitude of molecular events with vastly differing time scales. To offer a semiquantitative yet predictive understanding of the mechanism, we formulate a kinetic model for the G protein signaling and desensitization by considering essential biochemical steps from ligand binding to receptor internalization. The internalization, followed by receptor depletion from the plasma membrane, attenuates the downstream signal. Together with the kinetic model and its full numerics of the expression derived for the dose-response relation, an approximated form of the expression clarifies the role played by the individual biochemical processes and allows us to identify four distinct regimes for the downregulation that emerge from the balance between phosphorylation, dephosphorylation, and the cellular level of β-arrestin.

A quantitative pharmacology model for cannabinoid CB1 receptor mediated by Gi/Gs protein competition

BACKGROUND AND PURPOSE

Orthosteric agonism of the CB1 receptor normally associates with Gi signalling resulting in a net inhibition of cAMP production. Empirical evidence shows CB1 causes a net cAMP stimulation through Gs coupling under two conditions: co-stimulation with the D2 receptor and high-level CB1 expression. Two hypotheses have been proposed to account for these paradoxical effects, (1) Gi is consumed by coupling to D2 or extra CB1 and excess CB1 binds to Gs and (2), the formation of dimers CB1 -CB1 or CB1 -D2 switches Gi/Gs preference. This study explored the mechanisms of Gi/Gs preference based on a mathematical model of the CB1 receptor.

EXPERIMENTAL APPROACH

The model was based on Hypothesis 1 and known mechanisms. The model was calibrated to align with multiple types of data (cAMP, Gi dissociation and internalisation). The key step of Hypothesis 1 was examined by simulation from the model. An experiment was proposed to distinguish Hypothesis 1 and 2.

KEY RESULTS

The model successfully descripted multiple types of data under Hypothesis 1. Simulations from the model indicated that precoupling of G protein with receptors is necessary for this hypothesis. The model designed experiments to distinguish Hypothesis 1 and 2 by increasing Gi & Gs in parallel with CB1 overexpression. The two hypotheses result in distinct cAMP responses.

CONCLUSION AND IMPLICATIONS

A mathematical model of CB1 -regulated Gi/Gs pathways was developed. It indicated Hypothesis 1 is feasible and G protein precoupling is a key step causing cAMP signalling switch. The model-designed experiments provided guides for future experimentation.

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

Reshaping the yeast galactose regulon via GPCR signaling cascade

Dynamically regulated systems are preferable to control metabolic pathways for an improved strain performance with better productivity. Here, we harnessed to the G protein-coupled receptor (GPCR) signaling pathway to reshape the yeast galactose regulon. The galactose-regulated (GAL) system was coupled with the GPCR signaling pathway for mating pheromone via a synthetic transcription factor. In this study, we refabricated the dynamic range, sensitivity, and response time of the GAL system to α factor by modulating the key components of the GPCR signaling cascade. A series of engineered yeasts with self-secretion of α factor were constructed to achieve quorum-sensing behaviors. In addition, we also repurposed the GAL system to make it responsive to heat shock. Taken together, our work showcases the great potential of synthetic biology in creating user-defined metabolic controls. We envision that the plasticity of our genetic design would be of significant interest for the future fabrication of novel gene expression systems.

Particle-based simulations reveal two positive feedback loops allow relocation and stabilization of the polarity site during yeast mating

Many cells adjust the direction of polarized growth or migration in response to external directional cues. The yeast Saccharomyces cerevisiae orient their cell fronts (also called polarity sites) up pheromone gradients in the course of mating. However, the initial polarity site is often not oriented towards the eventual mating partner, and cells relocate the polarity site in an indecisive manner before developing a stable orientation. During this reorientation phase, the polarity site displays erratic assembly-disassembly behavior and moves around the cell cortex. The mechanisms underlying this dynamic behavior remain poorly understood. Particle-based simulations of the core polarity circuit revealed that molecular-level fluctuations are unlikely to overcome the strong positive feedback required for polarization and generate relocating polarity sites. Surprisingly, inclusion of a second pathway that promotes polarity site orientation generated relocating polarity sites with properties similar to those observed experimentally. This pathway forms a second positive feedback loop involving the recruitment of receptors to the cell membrane and couples polarity establishment to gradient sensing. This second positive feedback loop also allows cells to stabilize their polarity site once the site is aligned with the pheromone gradient.

Single-cell directional sensing from just a few receptor binding events

Identifying the directionality of signaling sources from noisy input to membrane receptors is an essential task performed by many cell types. A variety of models have been proposed to explain directional sensing in cells. However, many of these require significant computational and memory capacities for the cell. We propose and analyze a simple mechanism in which a cell adopts the direction associated with the first few membrane binding events. This model yields an accurate angular estimate to the source long before steady state is reached in biologically relevant scenarios. Our proposed mechanism allows for reliable estimates of the directionality of external signals using temporal information and assumes minimal computational capacities of the cell.

Chemotropism and Cell-Cell Fusion in Fungi

Fungi exhibit an enormous variety of morphologies, including yeast colonies, hyphal mycelia, and elaborate fruiting bodies. This diversity arises through a combination of polar growth, cell division, and cell fusion. Because fungal cells are nonmotile and surrounded by a protective cell wall that is essential for cell integrity, potential fusion partners must grow toward each other until they touch and then degrade the intervening cell walls without impacting cell integrity. Here, we review recent progress on understanding how fungi overcome these challenges. Extracellular chemoattractants, including small peptide pheromones, mediate communication between potential fusion partners, promoting the local activation of core cell polarity regulators to orient polar growth and cell wall degradation. However, in crowded environments, pheromone gradients can be complex and potentially confusing, raising the question of how cells can effectively find their partners. Recent findings suggest that the cell polarity circuit exhibits searching behavior that can respond to pheromone cues through a remarkably flexible and effective strategy called exploratory polarization.

Orientation of Cell Polarity by Chemical Gradients

Accurate decoding of spatial chemical landscapes is critical for many cell functions. Eukaryotic cells decode local chemical gradients to orient growth or movement in productive directions. Recent work on yeast model systems, whose gradient sensing pathways display much less complexity than those in animal cells, has suggested new paradigms for how these very small cells successfully exploit information in noisy and dynamic pheromone gradients to identify their mates. Pheromone receptors regulate a polarity circuit centered on the conserved Rho-family GTPase, Cdc42. The polarity circuit contains both positive and negative feedback pathways, allowing spontaneous symmetry breaking and also polarity site disassembly and relocation. Cdc42 orients the actin cytoskeleton, leading to focused vesicle traffic that promotes movement of the polarity site and also reshapes the cortical distribution of receptors at the cell surface. In this article, we review the advances from work on yeasts and compare them with the excitable signaling pathways that have been revealed in chemotactic animal cells. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The neural G protein Gαo tagged with GFP at an internal loop is functional in C. elegans

Gα_o is the alpha subunit of the major heterotrimeric G protein in neurons and mediates signaling by every known neurotransmitter, yet the signaling mechanisms activated by Gα_o remain to be fully elucidated. Genetic analysis in Caenorhabditis elegans has shown that Gα_o signaling inhibits neuronal activity and neurotransmitter release, but studies of the molecular mechanisms underlying these effects have been limited by lack of tools to complement genetic studies with other experimental approaches. Here, we demonstrate that inserting the green fluorescent protein (GFP) into an internal loop of the Gα_o protein results in a tagged protein that is functional in vivo and that facilitates cell biological and biochemical studies of Gα_o. Transgenic expression of Gα_o-GFP rescues the defects caused by loss of endogenous Gα_o in assays of egg laying and locomotion behaviors. Defects in body morphology caused by loss of Gα_o are also rescued by Gα_o-GFP. The Gα_o-GFP protein is localized to the plasma membrane of neurons, mimicking localization of endogenous Gα_o. Using GFP as an epitope tag, Gα_o-GFP can be immunoprecipitated from C. elegans lysates to purify Gα_o protein complexes. The Gα_o-GFP transgene reported in this study enables studies involving in vivo localization and biochemical purification of Gα_o to compliment the already well-developed genetic analysis of Gα_o signaling.

Exploratory polarization facilitates mating partner selection in Saccharomyces cerevisiae

Yeast decode pheromone gradients to locate mating partners, providing a model for chemotropism. How yeast polarize toward a single partner in crowded environments is unclear. Initially, cells often polarize in unproductive directions, but then they relocate the polarity site until two partners’ polarity sites align, whereupon the cells “commit” to each other by stabilizing polarity to promote fusion. Here we address the role of the early mobile polarity sites. We found that commitment by either partner failed if just one partner was defective in generating, orienting, or stabilizing its mobile polarity sites. Mobile polarity sites were enriched for pheromone receptors and G proteins, and we suggest that such sites engage in an exploratory search of the local pheromone landscape, stabilizing only when they detect elevated pheromone levels. Mobile polarity sites were also enriched for pheromone secretion factors, and simulations suggest that only focal secretion at polarity sites would produce high pheromone concentrations at the partner’s polarity site, triggering commitment.

Functional and Structural Analysis of a Novel Acyltransferase from Pathogenic Phytophthora melonis

This investigation characterizes an acyltransferase enzyme responsible for the pathogenicity of Phytophthora melonis. The protein was characterized in vitro for its physicochemical properties. The biochemical characterization, including thermal and pH stability, revealed the 35 °C temperature and 7.0 pH as the optimum conditions for the enzyme. Applying the Tween-80 solution enhanced the activity up to 124.9%. Comprehensive structural annotation revealed two domains, A (ranging from residues 260 to 620) and B (ranging from 141 to 219). Domain A had transglutaminase (T-Gase) elicitor properties, while B possessed antifreeze features. Rigorous sequence characterization of the acyltransferase tagged it as a low-temperature-resistant protein. Further, the taxonomic distribution analysis of the protein highlighted three genera in Oomycetes, i.e., Pythium, Phytophthora, and Plasmopara, bearing this protein. However, some taxonomic groups other than Oomycetes (i.e., archaea and bacteria) also contained the protein. Functional studies of structurally analogous proteins spanned 10 different taxonomic groups. These revealed TGase elicitors (10%), phytopathogen effector proteins RxLR (4%), transporter family proteins (3%), and endonucleases (1%). Other analogues having one percent of their individual share were HIV tat-specific factor 1, protocadherin fat 4, transcription factor 1, and 3-hydroxyisobutyrate dehydrogenase. Because the plant infection by P. melonis is a complex process regulated by a profusion of extracellular signals secreted by both host plants and the pathogen, this study will be of help in interpreting the cross-talk in the host-pathogen system.

G protein-coupled receptor-G protein interactions: a single-molecule perspective

G protein-coupled receptors (GPCRs) regulate many cellular and physiological processes, responding to a diverse range of extracellular stimuli including hormones, neurotransmitters, odorants, and light. Decades of biochemical and pharmacological studies have provided fundamental insights into the mechanisms of GPCR signaling. Thanks to recent advances in structural biology, we now possess an atomistic understanding of receptor activation and G protein coupling. However, how GPCRs and G proteins interact in living cells to confer signaling efficiency and specificity remains insufficiently understood. The development of advanced optical methods, including single-molecule microscopy, has provided the means to study receptors and G proteins in living cells with unprecedented spatio-temporal resolution. The results of these studies reveal an unexpected level of complexity, whereby GPCRs undergo transient interactions among themselves as well as with G proteins and structural elements of the plasma membrane to form short-lived signaling nanodomains that likely confer both rapidity and specificity to GPCR signaling. These findings may provide new strategies to pharmaceutically modulate GPCR function, which might eventually pave the way to innovative drugs for common diseases such as diabetes or heart failure.

Phosphorylation of the Gα protein Gpa2 promotes protein kinase A signaling in yeast

Heterotrimeric G proteins are important molecular switches that facilitate transmission of a variety of signals from the outside to the inside of cells. G proteins are highly conserved, enabling study of their regulatory mechanisms in model organisms such as the budding yeast Saccharomyces cerevisiae Gpa2 is a yeast Gα protein that functions in the nutrient signaling pathway. Using Phos-tag, a highly specific phosphate binding tag for separating phosphorylated proteins, we found that Gpa2 undergoes phosphorylation and that its level of phosphorylation is markedly increased upon nitrogen starvation. We also observed that phosphorylation of Gpa2 depends on glycogen synthase kinase (GSK). Disrupting GSK activity diminishes Gpa2 phosphorylation levels in vivo, and the purified GSK isoforms Mck1 and Ygk3 are capable of phosphorylating Gpa2 in vitro Functionally, phosphorylation enhanced plasma membrane localization of Gpa2 and promoted nitrogen starvation-induced activation of protein kinase A. Together, the findings of our study reveal a mechanism by which GSK- and nutrient-dependent phosphorylation regulates subcellular localization of Gpa2 and its ability to activate downstream signaling.

Mating yeast cells use an intrinsic polarity site to assemble a pheromone-gradient tracking machine

The mating of budding yeast depends on chemotropism, a fundamental cellular process. The two yeast mating types secrete peptide pheromones that bind to GPCRs on cells of the opposite type. Cells find and contact a partner by determining the direction of the pheromone source and polarizing their growth toward it. Actin-directed secretion to the chemotropic growth site (CS) generates a mating projection. When pheromone-stimulated cells are unable to sense a gradient, they form mating projections where they would have budded in the next cell cycle, at a position called the default polarity site (DS). Numerous models have been proposed to explain yeast gradient sensing, but none address how cells reliably switch from the intrinsically determined DS to the gradient-aligned CS, despite a weak spatial signal. Here we demonstrate that, in mating cells, the initially uniform receptor and G protein first polarize to the DS, then redistribute along the plasma membrane until they reach the CS. Our data indicate that signaling, polarity, and trafficking proteins localize to the DS during assembly of what we call the gradient tracking machine (GTM). Differential activation of the receptor triggers feedback mechanisms that bias exocytosis upgradient and endocytosis downgradient, thus enabling redistribution of the GTM toward the pheromone source. The GTM stabilizes when the receptor peak centers at the CS and the endocytic machinery surrounds it. A computational model simulates GTM tracking and stabilization and correctly predicts that its assembly at a single site contributes to mating fidelity.

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

Quantitative morphological phenomics of rice G protein mutants portend autoimmunity

The heterotrimeric G protein complex, composed of Gα, Gβ, and Gγ subunits, plays some role in structural development in plants but this role could be indirect because loss-of-function mutations do not alter the body plan and post-embryonic organs differ only morphologically and not in their identity. This uncertainty has been compounded by the fact that loss of the Gβ subunit in cereals, but not Arabidopsis, is seedling lethal and that loss of maize Gα subunit confers prolificacy of a reproductive organ. In this study, we comprehensively profiled the root and shoot structural traits of rice Gα-null and viable Gβ-RNAi "knockdown" mutants, and found anomalous morphologies caused by Gβ-RNAi that are distinct from the Arabidopsis orthologue. The rice Gβ-RNAi mutant exhibited reduced radial growth of aerial parts as well as a more compact root architecture, among which smaller root mass seems mainly due to increased necrosis when grown on soil. In addition, three dimensional analyses of rice root system architecture revealed that the smaller root architecture of Gβ-RNAi plant is also due to both reduced root elongation and adventitious root formation. This contrasts to the Arabidopsis Gβ-null mutation that promotes cell proliferation. There is elevated cell senescence activity both visualized by Evans Blue staining and inferred from an expression analysis of cell-death marker genes. We propose that the morphological phenotypes of rice Gβ-RNAi plants are predominantly associated with the mediation of various stresses and cell senescence, consistent with an indirect role for Arabidopsis Gβ in development where the orthologous gene ablation mainly confers altered cell proliferation. We also elaborate our speculative working hypothesis that cell division is a type of stress and as such due to impairment in responding to stress in the G protein mutants, manifests as altered morphology and architecture but not an altered body plan or organ identities.

Quantitative analysis of the yeast pheromone pathway

The pheromone response pathway of the yeast Saccharomyces cerevisiae is a well-established model for the study of G proteins and mitogen-activated protein kinase (MAPK) cascades. Our longstanding ability to combine sophisticated genetic approaches with established functional assays has provided a thorough understanding of signalling mechanisms and regulation. In this report, we compare new and established methods used to quantify pheromone-dependent MAPK phosphorylation, transcriptional induction, mating morphogenesis, and gradient tracking. These include both single-cell and population-based assays of activity. We describe several technical advances, provide example data for benchmark mutants, highlight important differences between newer and established methodologies, and compare the advantages and disadvantages of each as applied to the yeast model. Quantitative measurements of pathway activity have been used to develop mathematical models and reveal new regulatory mechanisms in yeast. It is our expectation that experimental and computational approaches developed in yeast may eventually be adapted to human systems biology and pharmacology.

Engineering a Model Cell for Rational Tuning of GPCR Signaling

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems.

Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization

A common challenge in systems biology is quantifying the effects of unknown parameters and estimating parameter values from data. For many systems, this task is computationally intractable due to expensive model evaluations and large numbers of parameters. In this work, we investigate a new method for performing sensitivity analysis and parameter estimation of complex biological models using techniques from uncertainty quantification. The primary advance is a significant improvement in computational efficiency from the replacement of model simulation by evaluation of a polynomial surrogate model. We demonstrate the method on two models of mating in budding yeast: a smaller ODE model of the heterotrimeric G-protein cycle, and a larger spatial model of pheromone-induced cell polarization. A small number of model simulations are used to fit the polynomial surrogates, which are then used to calculate global parameter sensitivities. The surrogate models also allow rapid Bayesian inference of the parameters via Markov chain Monte Carlo (MCMC) by eliminating model simulations at each step. Application to the ODE model shows results consistent with published single-point estimates for the model and data, with the added benefit of calculating the correlations between pairs of parameters. On the larger PDE model, the surrogate models allowed convergence for the distribution of 15 parameters, which otherwise would have been computationally prohibitive using simulations at each MCMC step. We inferred parameter distributions that in certain cases peaked at values different from published values, and showed that a wide range of parameters would permit polarization in the model. Strikingly our results suggested different diffusion constants for active versus inactive Cdc42 to achieve good polarization, which is consistent with experimental observations in another yeast species S. pombe.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α-Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data.

Host-induced gene silencing of a regulator of G protein signalling gene (VdRGS1) confers resistance to Verticillium wilt in cotton

Verticillium wilt (VW), caused by soil-borne fungi of the genus Verticillium, is a serious disease affecting a wide range of plants and leading to a constant and major challenge to agriculture worldwide. Cotton (Gossypium hirsutum) is the world's most important natural textile fibre and oil crop. VW of cotton is a highly devastating vascular disease; however, few resistant germplasms have been reported in cotton. An increasing number of studies have shown that RNA interference (RNAi)-based host-induced gene silencing (HIGS) is an effective strategy for improving plant resistance to pathogens by silencing genes essential for the pathogenicity of these pathogens. Here, we have identified and characterized multifunctional regulators of G protein signalling (RGS) in the Verticillium dahliae virulence strain, Vd8. Of eight VdRGS genes, VdRGS1 showed the most significant increase in expression in V. dahliae after treating with the roots of cotton seedlings. Based on the phenotype detection of VdRGS1 deletion and complementation mutants, we found that VdRGS1 played crucial roles in spore production, hyphal development, microsclerotia formation and pathogenicity. Tobacco rattle virus-mediated HIGS in cotton plants silenced VdRGS1 transcripts in invaded V. dahliae strains and enhanced broad-spectrum resistance to cotton VW. Our data demonstrate that VdRGS1 is a conserved and essential gene for V. dahliae virulence. HIGS of VdRGS1 provides effective control against V. dahliae infection and could obtain the durable disease resistance in cotton and in other VW-susceptible host crops by developing the stable transformants.

Systems Level Modeling of the Cell Cycle Using Budding Yeast

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

GPCR-Gα protein precoupling: Interaction between Ste2p, a yeast GPCR, and Gpa1p, its Gα protein, is formed before ligand binding via the Ste2p C-terminal domain and the Gpa1p N-terminal domain

G protein coupled receptors bind ligands that initiate intracellular signaling cascades via heterotrimeric G proteins. In this study, involvement of the N-terminal residues of yeast G-alpha (Gpa1p) with the C-terminal residues of a full-length or C-terminally truncated Ste2p were investigated using bioluminescence resonance energy transfer (BRET), a non-radiative energy transfer phenomenon where protein-protein interactions can be quantified between a donor bioluminescent molecule and a suitable acceptor fluorophore. Constitutive and position-dependent BRET signal was observed in the absence of agonist (α-factor). Upon the activation of the receptors with α-factor, no significant change in BRET signal was observed. The location of Ste2p-Gpa1p heterodimer was investigated using confocal fluorescence microscopy and bimolecular fluorescence complementation (BiFC) assay, a technique where two non-fluorescent fragments of a fluorescent protein reassemble in vivo to restore fluorescence property thereby directly reporting a protein-protein interaction. BiFC experiments resulted in a dimerization signal intracellularly during biosynthesis on the endoplasmic reticulum (ER) and on the plasma membrane (PM). The constitutive BRET and BiFC signals observed on ER between Ste2p and Gpa1p in their quiescent and activated states are indicative of pre-coupling between these two proteins. This study is the first to show that the extreme N-terminus of yeast G protein alpha subunit is in close proximity to its receptor. The data suggests a pre-coupled heterodimer prior to receptor activation. The images presented in this study are the first direct in vivo evidence showing the localization of receptor - G protein heterodimers during biosynthesis and before reaching the plasma membrane.

Testing the limits of gradient sensing

The ability to detect a chemical gradient is fundamental to many cellular processes. In multicellular organisms gradient sensing plays an important role in many physiological processes such as wound healing and development. Unicellular organisms use gradient sensing to move (chemotaxis) or grow (chemotropism) towards a favorable environment. Some cells are capable of detecting extremely shallow gradients, even in the presence of significant molecular-level noise. For example, yeast have been reported to detect pheromone gradients as shallow as 0.1 nM/μm. Noise reduction mechanisms, such as time-averaging and the internalization of pheromone molecules, have been proposed to explain how yeast cells filter fluctuations and detect shallow gradients. Here, we use a Particle-Based Reaction-Diffusion model of ligand-receptor dynamics to test the effectiveness of these mechanisms and to determine the limits of gradient sensing. In particular, we develop novel simulation methods for establishing chemical gradients that not only allow us to study gradient sensing under steady-state conditions, but also take into account transient effects as the gradient forms. Based on reported measurements of reaction rates, our results indicate neither time-averaging nor receptor endocytosis significantly improves the cell’s accuracy in detecting gradients over time scales associated with the initiation of polarized growth. Additionally, our results demonstrate the physical barrier of the cell membrane sharpens chemical gradients across the cell. While our studies are motivated by the mating response of yeast, we believe our results and simulation methods will find applications in many different contexts.

In order to survive, many organisms must not only be able to detect the presence of a chemical compound, but also in which direction that compound increases or decreases in concentration. For example, bacteria cells prefer to move towards areas with high sugar concentrations. The process by which cells determine the direction of a chemical gradient is called “Gradient Sensing”. Of particular interest is the gradient sensing capability of yeast cells. These cells have been observed detecting the direction of extremely shallow gradients, which produce only a 2% difference in the number of molecules across the cell. Because the molecular-level noise is much larger than this signal, it is unclear what noise-reduction mechanism the cell employs to reduce the noise and detect the signal. We developed a 3D computational simulation platform to calculate and study the exact positions of molecules during this process. Our platform utilizes High Performance Computing clusters and GPGPUs. We find that, of the two prevailing models in the literature, neither time-averaging nor receptor endocytosis sufficiently reduces molecular noise for yeast cells to reliably detect chemical gradients before they initiate polarized growth. This finding implies yeast must possess a mechanism for reorienting the direction of growth after cell polarization has occurred. We also find the cell membrane and similarly, any other physical barrier nearby the cell can improve the cell’s likelihood of detecting the gradient. Our simulation methods and results will be applicable in other areas of research.

Mathematical modeling reveals differential regulation of MAPK activity by phosphatase proteins in the yeast pheromone response pathway

To prevent indefinite cellular responses to external signals, cells utilize various adaptation mechanisms.

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.

Sexual pheromone modulates the frequency of cytosolic Ca2+ bursts in Saccharomyces cerevisiae

Transient and highly regulated elevations of cytosolic Ca²⁺ control a variety of cellular processes. Bulk measurements using radioactive Ca²⁺ and the luminescent sensor aequorin have shown that in response to pheromone, budding yeast cells undergo a rise of cytosolic Ca²⁺ that is mediated by two import systems composed of the Mid1-Cch1-Ecm7 protein complex and the Fig1 protein. Although this response has been widely studied, there is no treatment of Ca²⁺ dynamics at the single-cell level. Here, using protein calcium indicators, we show that both vegetative and pheromone-treated yeast cells exhibit discrete and asynchronous Ca²⁺ bursts. Most bursts reach maximal amplitude in 1-10 s, range between 7 and 30 s, and decay in a way that fits a single-exponential model. In vegetative cells, bursts are scarce but preferentially occur when cells are transitioning G1 and S phases. On pheromone presence, Ca²⁺ burst occurrence increases dramatically, persisting during cell growth polarization. Pheromone concentration modulates burst frequency in a mechanism that depends on Mid1, Fig1, and a third, unidentified, import system. We also show that the calcineurin-responsive transcription factor Crz1 undergoes nuclear localization bursts during the pheromone response.

A framework for discrete stochastic simulation on 3D moving boundary domains

We have developed a method for modeling spatial stochastic biochemical reactions in complex, three-dimensional, and time-dependent domains using the reaction-diffusion master equation formalism. In particular, we look to address the fully coupled problems that arise in systems biology where the shape and mechanical properties of a cell are determined by the state of the biochemistry and vice versa. To validate our method and characterize the error involved, we compare our results for a carefully constructed test problem to those of a microscale implementation. We demonstrate the effectiveness of our method by simulating a model of polarization and shmoo formation during the mating of yeast. The method is generally applicable to problems in systems biology where biochemistry and mechanics are coupled, and spatial stochastic effects are critical.

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.

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.

A Synthetic Multicellular Memory Device

Changing environments pose a challenge to living organisms. Cells need to gather and process incoming information, adapting to changes in predictable ways. This requires in particular the presence of memory, which allows different internal states to be stored. Biological memory can be stored by switches that retain information on past and present events. Synthetic biologists have implemented a number of memory devices for biological applications, mostly in single cells. It has been shown that the use of multicellular consortia provides interesting advantages to implement biological circuits. Here we show how to build a synthetic biological memory switch using an eukaryotic consortium. We engineered yeast cells that can communicate and retain memory of changes in the extracellular environment. These cells were able to produce and secrete a pheromone and sense a different pheromone following NOT logic. When the two strains were cocultured, they behaved as a double-negative-feedback motif with memory. In addition, we showed that memory can be effectively changed by the use of external inputs. Further optimization of these modules and addition of other cells could lead to new multicellular circuits that exhibit memory over a broad range of biological inputs.

Modelling of Yeast Mating Reveals Robustness Strategies for Cell-Cell Interactions

Mating of budding yeast cells is a model system for studying cell-cell interactions. Haploid yeast cells secrete mating pheromones that are sensed by the partner which responds by growing a mating projection toward the source. The two projections meet and fuse to form the diploid. Successful mating relies on precise coordination of dynamic extracellular signals, signaling pathways, and cell shape changes in a noisy background. It remains elusive how cells mate accurately and efficiently in a natural multi-cell environment. Here we present the first stochastic model of multiple mating cells whose morphologies are driven by pheromone gradients and intracellular signals. Our novel computational framework encompassed a moving boundary method for modeling both a-cells and α-cells and their cell shape changes, the extracellular diffusion of mating pheromones dynamically coupled with cell polarization, and both external and internal noise. Quantification of mating efficiency was developed and tested for different model parameters. Computer simulations revealed important robustness strategies for mating in the presence of noise. These strategies included the polarized secretion of pheromone, the presence of the α-factor protease Bar1, and the regulation of sensing sensitivity; all were consistent with data in the literature. In addition, we investigated mating discrimination, the ability of an a-cell to distinguish between α-cells either making or not making α-factor, and mating competition, in which multiple a-cells compete to mate with one α-cell. Our simulations were consistent with previous experimental results. Moreover, we performed a combination of simulations and experiments to estimate the diffusion rate of the pheromone a-factor. In summary, we constructed a framework for simulating yeast mating with multiple cells in a noisy environment, and used this framework to reproduce mating behaviors and to identify strategies for robust cell-cell interactions.

One of the riddles of Nature is how cells interact with one another to create complex cellular networks such as the neural networks in the brain. Forming precise connections between irregularly shaped cells is a challenge for biology. We developed computational methods for simulating these complex cell-cell interactions. We applied these methods to investigate yeast mating in which two yeast cells grow projections that meet and fuse guided by pheromone attractants. The simulations described molecules both inside and outside of the cell, and represented the continually changing shapes of the cells. We found that positioning the secretion and sensing of pheromones at the same location on the cell surface was important. Other key factors for robust mating included secreting a protein that removed excess pheromone from outside of the cell so that the signal would not be too strong. An important advance was being able to simulate as many as five cells in complex mating arrangements. Taken together we used our novel computational methods to describe in greater detail the yeast mating process, and more generally, interactions among cells changing their shapes in response to their neighbors.

Gβ promotes pheromone receptor polarization and yeast chemotropism by inhibiting receptor phosphorylation

Gradient-directed cell migration (chemotaxis) and growth (chemotropism) are processes that are essential to the development and life cycles of all species. Cells use surface receptors to sense the shallow chemical gradients that elicit chemotaxis and chemotropism. Slight asymmetries in receptor activation are amplified by downstream signaling systems, which ultimately induce dynamic reorganization of the cytoskeleton. During the mating response of budding yeast, a model chemotropic system, the pheromone receptors on the plasma membrane polarize to the side of the cell closest to the stimulus. Although receptor polarization occurs before and independently of actin cable-dependent delivery of vesicles to the plasma membrane (directed secretion), it requires receptor internalization. Phosphorylation of pheromone receptors by yeast casein kinase 1 or 2 (Yck1/2) stimulates their internalization. We showed that the pheromone-responsive Gβγ dimer promotes the polarization of the pheromone receptor by interacting with Yck1/2 and locally inhibiting receptor phosphorylation. We also found that receptor phosphorylation is essential for chemotropism, independently of its role in inducing receptor internalization. A mathematical model supports the idea that the interaction between Gβγ and Yck1/2 results in differential phosphorylation and internalization of the pheromone receptor and accounts for its polarization before the initiation of directed secretion.

Heterotrimeric G protein signaling via GIV/Girdin: Breaking the rules of engagement, space, and time

Canonical signal transduction via heterotrimeric G proteins is spatially and temporally restricted, that is, triggered exclusively at the plasma membrane (PM), only by agonist activation of G protein-coupled receptors (GPCRs) via a process that completes within a few hundred milliseconds. Recently, a rapidly emerging paradigm has revealed a non-canonical pathway for activation of heterotrimeric G proteins by the non-receptor guanidine-nucleotide exchange factor (GEF), GIV/Girdin. This pathway has distinctive temporal and spatial features and an unusual profile of receptor engagement: diverse classes of receptors, not just GPCRs can engage with GIV to trigger such activation. Such activation is spatially and temporally unrestricted, that is, can occur both at the PM and on internal membranes discontinuous with the PM, and can continue for prolonged periods of time. Here, we provide the most complete up-to-date review of the molecular mechanisms that govern the unique spatiotemporal aspects of non-canonical G protein activation by GIV and the relevance of this new paradigm in health and disease.

The untapped potential of tyrosine-based G protein signaling

Tyrosine-based and trimeric G protein-based signaling are the two most widely studied and distinct mechanisms for signal transduction in eukaryotes. How each of them relay signals across the plasma membrane independently of each other has been extensively characterized; however, an understanding of how they work together remained obscure. Recently, a rapidly emerging paradigm has revealed that tyrosine based signals are relayed via G proteins, and that the cross-talk between the two hubs are more robustly and sophisticatedly integrated than was previously imagined. More importantly, by straddling the two signaling hubs that are most frequently targeted for their therapeutic significance, the tyrosine-based G-protein signaling pathway has its own growing list of pathophysiologic importance, both as therapeutic target in a variety of disease states, and by paving the way for personalized medicine. The fundamental principles of this emerging paradigm and its pharmacologic potential are discussed.

Resonance Energy Transfer-Based Approaches to Study GPCRs

Since their discovery, G protein-coupled receptors (GPCRs) constitute one of the most studied proteins leading to important discoveries and perspectives in terms of their biology and implication in physiology and pathophysiology. This is mostly linked to the remarkable advances in the development and application of the biophysical resonance energy transfer (RET)-based approaches, including bioluminescence and fluorescence resonance energy transfer (BRET and FRET, respectively). Indeed, BRET and FRET have been extensively applied to study different aspects of GPCR functioning such as their activation and regulation either statically or dynamically, in real-time and intact cells. Consequently, our view on GPCRs has considerably changed opening new challenges for the study of GPCRs in their native tissues in the aim to get more knowledge on how these receptors control the biological responses. Moreover, the technological aspect of this field of research promises further developments for robust and reliable new RET-based assays that may be compatible with high-throughput screening as well as drug discovery programs.

A proteomic analysis reveals the interaction of GluK1 ionotropic kainate receptor subunits with Go proteins

Kainate receptors (KARs) are found ubiquitously in the CNS and are present presynaptically and postsynaptically regulating synaptic transmission and excitability. Functional studies have proven that KARs act as ion channels as well as potentially activating G-proteins, thus indicating the existance of a dual signaling system for KARs. Nevertheless, it is not clear how these ion channels activate G-proteins and which of the KAR subunits is involved. Here we performed a proteomic analysis to define proteins that interact with the C-terminal domain of GluK1 and we identified a variety of proteins with many different functions, including a Go α subunit. These interactions were verified through distinct in vitro and in vivo assays, and the activation of the Go protein by GluK1 was validated in bioluminescence resonance energy transfer experiments, while the specificity of this association was confirmed in GluK1-deficient mice. These data reveal components of the KAR interactome, and they show that GluK1 and Go proteins are natural partners, accounting for the metabotropic effects of KARs.

Alternative cell polarity behaviours arise from changes in G-protein spatial dynamics

Yeast cells form a single mating projection when exposed to mating pheromone, a classic example of cell polarity. Prolonged treatment with pheromone or specific mutations results in alternative cell polarity behaviours. The authors performed mathematical modelling to investigate these unusual cell morphologies from the perspective of balancing spatial amplification (i.e. positive feedback that localises components) with spatial tracking (i.e. negative feedback that allows sensing of gradient). First, they used generic models of cell polarity to explore different cell polarity behaviours that arose from changes in the model spatial dynamics. By exploring the positive and negative feedback loops in each stage of a two-stage model, they simulated a variety of cell morphologies including single bending projections, single straight projections, periodic multiple projections and simultaneous double projections. In the second half of the study, they used a two-stage mechanistic model of yeast cell polarity focusing on G-protein signalling to integrate the modelling results more closely with the authors' previously published experimental observations. In summary, the combination of modelling and experiments describes how yeast cells exhibit a diversity of cell morphologies arising from two-stage G-protein signalling dynamics modulated by positive and negative feedbacks.

Adaptive evolution of signaling partners

Proteins that interact coevolve their structures. When mutation disrupts the interaction, compensation by the partner occurs to restore interaction otherwise counterselection occurs. We show in this study how a destabilizing mutation in one protein is compensated by a stabilizing mutation in its protein partner and their coevolving path. The pathway in this case and likely a general principle of coevolution is that the compensatory change must tolerate both the original and derived structures with equivalence in function and activity. Evolution of the structure of signaling elements in a network is constrained by specific protein pair interactions, by requisite conformational changes, and by catalytic activity. The heterotrimeric G protein-coupled signaling is a paragon of this protein interaction/function complexity and our deep understanding of this pathway in diverse organisms lends itself to evolutionary study. Regulators of G protein Signaling (RGS) proteins accelerate the intrinsic GTP hydrolysis rate of the Gα subunit of the heterotrimeric G protein complex. An important RGS-contact site is a hydroxyl-bearing residue on the switch I region of Gα subunits in animals and most plants, such as Arabidopsis. The exception is the grasses (e.g., rice, maize, sugarcane, millets); these plants have Gα subunits that replaced the critical hydroxyl-bearing threonine with a destabilizing asparagine shown to disrupt interaction between Arabidopsis RGS protein (AtRGS1) and the grass Gα subunit. With one known exception (Setaria italica), grasses do not encode RGS genes. One parsimonious deduction is that the RGS gene was lost in the ancestor to the grasses and then recently acquired horizontally in the lineage S. italica from a nongrass monocot. Like all investigated grasses, S. italica has the Gα subunit with the destabilizing asparagine residue in the protein interface but, unlike other known grass genomes, still encodes an expressed RGS gene, SiRGS1. SiRGS1 accelerates GTP hydrolysis at similar concentration of both Gα subunits containing either the stabilizing (AtGPA1) or destabilizing (RGA1) interface residue. SiRGS1 does not use the hydroxyl-bearing residue on Gα to promote GAP activity and has a larger Gα-interface pocket fitting to the destabilizing Gα. These findings indicate that SiRGS1 adapted to a deleterious mutation on Gα using existing polymorphism in the RGS protein population.

Multimodular biosensors reveal a novel platform for activation of G proteins by growth factor receptors

Environmental cues are transmitted to the interior of the cell via a complex network of signaling hubs. Receptor tyrosine kinases (RTKs) and trimeric G proteins are two such major signaling hubs in eukaryotes. Conventionally, canonical signal transduction via trimeric G proteins is thought to be triggered exclusively by G protein-coupled receptors. Here we used molecular engineering to develop modular fluorescent biosensors that exploit the remarkable specificity of bimolecular recognition, i.e., of both G proteins and RTKs, and reveal the workings of a novel platform for activation of G proteins by RTKs in single living cells. Comprised of the unique modular makeup of guanidine exchange factor Gα-interacting vesicle-associated protein (GIV)/girdin, a guanidine exchange factor that links G proteins to a variety of RTKs, these biosensors provide direct evidence that RTK-GIV-Gαi ternary complexes are formed in living cells and that Gαi is transactivated within minutes after growth factor stimulation at the plasma membrane. Thus, GIV-derived biosensors provide a versatile strategy for visualizing, monitoring, and manipulating the dynamic association of Gαi with RTKs for noncanonical transactivation of G proteins in cells and illuminate a fundamental signaling event regulated by GIV during diverse cellular processes and pathophysiologic states.

Roles of regulated internalization in the polarization of cell surface receptors

Cell polarization, the generation of cellular asymmetries, is a fundamental biological process. Polarity of different molecules can arise through several mechanisms. Among these, internalization has been shown to play an important role in the polarization of cell surface receptors. The internalization of cell surface receptors can be upregulated upon ligand binding. Additional regulatory mechanism can downregulate the internalization process. Here we describe a general model, which incorporates these two opposing processes, to study the role of internalization in the establishment of cell polarity. We find that the competition between these two processes is sufficient to induce receptor polarization. Our results show that regulated internalization provides additional regulation on polarization as well. In addition, we discuss applications of our model to the yeast system, which shows the capability and potential of the model.

The structure of dynamic GPCR signaling networks

G-protein-coupled receptors (GPCRs) stimulate signaling networks that control a variety of critical physiological processes. Static information on the map of interacting signaling molecules at the basis of many cellular processes exists, but little is known about the dynamic operation of these networks. Here we focus on two questions. First, Is the network architecture underlying GPCR-activated cellular processes unique in comparison with others such as transcriptional networks? We discuss how spatially localized GPCR signaling requires uniquely organized networks to execute polarized cell responses. Second, What approaches overcome challenges in deciphering spatiotemporally dynamic networks that govern cell behavior? We focus on recently developed microfluidic and optical approaches that allow GPCR signaling pathways to be triggered and perturbed with spatially and temporally variant input while simultaneously visualizing molecular and cellular responses. When integrated with mathematical modeling, these approaches can help identify design principles that govern cell responses to extracellular signals. We outline why optical approaches that allow the behavior of a selected cell to be orchestrated continually are particularly well suited for probing network organization in single cells.

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.

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.

Short-term desensitization of muscarinic K+ current in the heart

Acetylcholine (ACh) rapidly increases cardiac K(+) currents (IKACh) by activating muscarinic K(+) (KACh) channels followed by a gradual amplitude decrease within seconds. This phenomenon is called short-term desensitization and its precise mechanism and physiological role are still unclear. We constructed a mathematical model for IKACh to examine the conditions required to reconstitute short-term desensitization. Two conditions were crucial: two distinct muscarinic receptors (m2Rs) with different affinities for ACh, which conferred an IKACh response over a wide range of ACh concentrations, and two distinct KACh channels with different affinities for the G-protein βγ subunits, which contributed to reconstitution of the temporal behavior of IKACh. Under these conditions, the model quantitatively reproduced several unique properties of short-term desensitization observed in myocytes: 1), the peak and quasi-steady states with 0.01-100 μM [ACh]; 2), effects of ACh preperfusion; and 3), recovery from short-term desensitization. In the presence of 10 μM ACh, the IKACh model conferred recurring spontaneous firing after asystole of 8.9 s and 10.7 s for the Demir and Kurata sinoatrial node models, respectively. Therefore, two different populations of KACh channels and m2Rs may participate in short-term desensitization of IKACh in native myocytes, and may be responsible for vagal escape at nodal cells.

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.