Pheromone Gradient Research Articles

The roles of yeast formins and their regulators Bud6 and Bil2 in the pheromone response.

In response to pheromone Saccharomyces cerevisiae extend a mating projection. This process depends on the formation of polarized actin cables which direct secretion to the mating tip and translocate the nucleus for karyogamy. Here, we demonstrate that proper mating projection formation requires the formin Bni1, as well as the actin nucleation promoting activities of Bud6, but not the formin Bnr1. Further, Bni1 is required for pheromone gradient tracking. Our work also reveals unexpected new functions for Bil2 in the pheromone response. Previously we identified Bil2 as a direct inhibitor of Bnr1 during vegetative cell growth. Here, we show that Bil2 has Bnr1-independent functions in spatially focusing Bni1-GFP at mating projection tips, and in vitro Bil2 and its binding partner Bud6 organize Bni1 into clusters that nucleate actin assembly. bil2∆ cells also display entangled Bni1-generated actin cable arrays and defects in secretory vesicle transport and nuclear positioning. At low pheromone concentrations, bil2∆ cells are delayed in establishing a polarity axis, and at high concentrations they prematurely form a second and a third mating projection. Together, these results suggest that Bil2 promotes the proper formation and timing of mating projections by organizing Bni1 and maintaining a persistent axis of polarized growth.

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.

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.

A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement.

Cells polarize their movement or growth toward external directional cues in many different contexts. For example, budding yeast cells grow toward potential mating partners in response to pheromone gradients. Directed growth is controlled by polarity factors that assemble into clusters at the cell membrane. The clusters assemble, disassemble, and move between different regions of the membrane before eventually forming a stable polarity site directed toward the pheromone source. Pathways that regulate clustering have been identified but the molecular mechanisms that regulate cluster mobility are not well understood. To gain insight into the contribution of chemical noise to cluster behavior we simulated clustering using the reaction-diffusion master equation (RDME) framework to account for molecular-level fluctuations. RDME simulations are a computationally efficient approximation, but their results can diverge from the underlying microscopic dynamics. We implemented novel concentration-dependent rate constants that improved the accuracy of RDME-based simulations, allowing us to efficiently investigate how cluster dynamics might be regulated. Molecular noise was effective in relocating clusters when the clusters contained low numbers of limiting polarity factors, and when Cdc42, the central polarity regulator, exhibited short dwell times at the polarity site. Cluster stabilization occurred when abundances or binding rates were altered to either lengthen dwell times or increase the number of polarity molecules in the cluster. We validated key results using full 3D particle-based simulations. Understanding the mechanisms cells use to regulate the dynamics of polarity clusters should provide insights into how cells dynamically track external directional cues.

Chemotactic movement of a polarity site enables yeast cells to find their mates

How small eukaryotic cells can interpret dynamic, noisy, and spatially complex chemical gradients to orient growth or movement is poorly understood. We address this question using Saccharomyces cerevisiae, where cells orient polarity up pheromone gradients during mating. Initial orientation is often incorrect, but polarity sites then move around the cortex in a search for partners. We find that this movement is biased by local pheromone gradients across the polarity site: that is, movement of the polarity site is chemotactic. A bottom-up computational model recapitulates this biased movement. The model reveals how even though pheromone-bound receptors do not mimic the shape of external pheromone gradients, nonlinear and stochastic effects combine to generate effective gradient tracking. This mechanism for gradient tracking may be applicable to any cell that searches for a target in a complex chemical landscape.

Phosphorylated Gβ is a directional cue during yeast gradient tracking.

Budding yeast cells interpret shallow pheromone gradients from cells of the opposite mating type, polarize their growth toward the pheromone source, and fuse at the chemotropic growth site. We previously proposed a deterministic, gradient-sensing model that explains how yeast cells switch from the intrinsically positioned default polarity site (DS) to the gradient-aligned chemotropic site (CS) at the plasma membrane. Because phosphorylation of the mating-specific Gβ subunit is thought to be important for this process, we developed a biosensor that bound to phosphorylated but not unphosphorylated Gβ and monitored its spatiotemporal dynamics to test key predictions of our gradient-sensing model. In mating cells, the biosensor colocalized with both Gβ and receptor reporters at the DS and then tracked with them to the CS. The biosensor concentrated on the leading side of the tracking Gβ and receptor peaks and was the first to arrive and stop tracking at the CS. Our data showed that the concentrated localization of phosphorylated Gβ correlated with the tracking direction and final position of the G protein and receptor, consistent with the idea that gradient-regulated phosphorylation and dephosphorylation of Gβ contributes to gradient sensing. Cells expressing a nonphosphorylatable mutant form of Gβ exhibited defects in gradient tracking, orientation toward mating partners, and mating efficiency.

Komagataella phaffii YPS1-5 encodes the alpha-factor degrading protease Bar1

Yeast mating pheromones are small secreted peptides required for efficient mating between cells of opposite mating type. Pheromone gradients allow the cells to detect potential mating partners. Secreted pheromone degrading proteases steepen local gradients and allow fast recovery from the pheromone signal. The methylotrophic yeast Komagataella phaffii is a preferentially haploid species. Only under nitrogen starvation, mating genes are activated and the cells are able to undergo a full sexual cycle of mating and sporulation. It has been shown that, similar to other yeasts, K. phaffii requires the mating pheromone and pheromone surface receptor genes for efficient mating. The analysis of so far uncharacterized mating-type-specific genes allowed us to identify the K. phaffii α-factor protease gene YPS1-5. It encodes an aspartic protease of the yapsin family and is upregulated only in a-type cells under mating conditions. The phenotype of K. phaffiia-type strains with a deletion in the protease gene was found to be highly similar to the phenotype of Saccharomyces cerevisiae α-factor protease BAR1 deletion strains. They are highly sensitive to α-factor pheromone in pheromone sensitivity assays and were found to mate with reduced efficiency. Based on our results, we propose to rename the gene into K. phaffii BAR1.

Mitotic and pheromone-specific intrinsic polarization cues interfere with gradient sensing in Saccharomyces cerevisiae

Polarity decisions are central to many processes, including mitosis and chemotropism. In Saccharomyces cerevisiae, budding and mating projection (MP) formation use an overlapping system of cortical landmarks that converges on the small G protein Cdc42. However, pheromone-gradient sensing must override the Rsr1-dependent internal polarity cues used for budding. Using this model system, we asked what happens when intrinsic and extrinsic spatial cues are not aligned. Is there competition, or collaboration? By live-cell microscopy and microfluidics techniques, we uncovered three previously overlooked features of this signaling system. First, the cytokinesis-associated polarization patch serves as a polarity landmark independently of all known cues. Second, the Rax1-Rax2 complex functions as a pheromone-promoted polarity cue in the distal pole of the cells. Third, internal cues remain active during pheromone-gradient tracking and can interfere with this process, biasing the location of MPs. Yeast defective in internal-cue utilization align significantly better than wild type with artificially generated pheromone gradients.

A Novel Stochastic Simulation Approach Enables Exploration of Mechanisms to Regulate Polarization Dynamics

Cells polarize in the direction of chemical gradients to orient growth or migration. When budding yeast cells are exposed to a source of mating pheromone, polarity factors assemble into highly dynamic clusters at the plasma membrane, sometimes but not always oriented up the pheromone gradient. The clusters assemble, disassemble, and move between different regions of the cell membrane before eventually forming a more concentrated cluster stably oriented towards the pheromone source. Pathways to generate and stabilize polarity factor clusters have been identified, but clustering dynamics are not well understood. Molecular fluctuations are likely to contribute to clustering dynamics. To gain insight into how they do so, we performed efficient stochastic simulations within the reaction-diffusion master equation framework. While less accurate than particle-based simulations, this approach has been improved by recently derived scale-dependent mesoscopic association rates. A remaining problem is that such mesoscopic rates perform poorly at the high local molecular concentrations typical of polarity systems. We therefore derived and tested novel mesoscopic rates that improve the accuracy of such simulations. We observed that although some existing polarity models failed to produce highly dynamic signaling clusters, a single additional reaction, proposed in another model, enabled dynamic clustering behavior. We investigate which reactions affect cluster dynamics, and validate our predictions using a more realistic particle-based model in a 3D geometry. Overall, our novel stochastic simulation approach allowed us to efficiently explore possible mechanisms by which cells may control polarization dynamics important for gradient detection.

Ratiometric GPCR signaling enables directional sensing in yeast.

Accurate detection of extracellular chemical gradients is essential for many cellular behaviors. Gradient sensing is challenging for small cells, which can experience little difference in ligand concentrations on the up-gradient and down-gradient sides of the cell. Nevertheless, the tiny cells of the yeast Saccharomyces cerevisiae reliably decode gradients of extracellular pheromones to find their mates. By imaging the behavior of polarity factors and pheromone receptors, we quantified the accuracy of initial polarization during mating encounters. We found that cells bias the orientation of initial polarity up-gradient, even though they have unevenly distributed receptors. Uneven receptor density means that the gradient of ligand-bound receptors does not accurately reflect the external pheromone gradient. Nevertheless, yeast cells appear to avoid being misled by responding to the fraction of occupied receptors rather than simply the concentration of ligand-bound receptors. Such ratiometric sensing also serves to amplify the gradient of active G protein. However, this process is quite error-prone, and initial errors are corrected during a subsequent indecisive phase in which polarity clusters exhibit erratic mobile behavior.

Quantitative proteomics reveals a Gα/MAPK signaling hub that controls pheromone-induced cellular polarization in yeast

The mating-specific yeast Gα controls pheromone signaling by sequestering Gβγ and by regulating the Fus3 MAP kinase. Disrupting Gα-Fus3 interaction leads to severe defects in chemotropism. Because Gα concentrates at the chemotropic growth site where Fus3 is required for the phosphorylation of two known targets, we screened for additional proteins whose phosphorylation depends on pheromone stimulation and Gα-Fus3 interaction. Using a mutant form of Gα severely defective in Fus3-binding, GαDSD, and quantitative mass spectrometry, fourteen proteins were identified as potential targets of Gα-recruited Fus3, ten of which were previously implicated in cell polarity and morphogenesis. To explore the biological relevance of these findings, we focused on the Spa2 polarisome protein, which was hypophosphorylated on multiple serine residues in pheromone-treated GαDSD cells. Six sites were mutagenized to create the Spa26XSA mutant protein. Spa26XSA exhibited increased affinity for Fus3, consistent with a kinase-substrate interaction, and Spa26XSA cells exhibited dramatic defects in gradient sensing and zygote formation. These results suggest that Gα promotes the phosphorylation of Spa2 by Fus3 at the cortex of pheromone-stimulated cells, and that this mechanism plays a role in chemotropism. How the Gα-Fus3 signaling hub affects the other putative targets identified here has yet to be determined. SignificancePreviously, interaction between the G alpha protein, Gpa1, and the MAPK of the pheromone response pathway, Fus3, was shown to be important for efficient sensing of the pheromone gradient and for the maintenance of cell polarity during mating. Here we show that the underlying molecular mechanisms involve the phosphorylation of specific cortical targets of Gpa1/Fus3. These have been identified by quantitative phosphoproteomics using a mutant of Gpa1, which is defective in interacting with Fus3. One of these targets is the polarisome protein Spa2. Alanine substitution of the Spa2 phosphorylation sites targeted by Gpa1/Fus3 lead to a dramatic defect in pheromone gradient sensing and zygote formation. These results reveal how the G alpha protein and the MAPK control cell polarity in a prototypical model system. Our results have wider significance as similar mechanisms exist in higher eukaryotes and are involved in important biological such as neuron development, immunity, and cancer cell metastasis.

Tracking of multiple cells with ant pheromone field evolution

Tracking of biological cells is necessary to improve the understanding of their growth and behavior. Most methods used in cell tracking are time consuming and inaccurate for large population density or closely interacting cells. In this paper, a fast and accurate ant-inspired estimating algorithm for tracking multiple cells is proposed that uses a dual prediction mechanism and a pheromone updating strategy. The dual prediction mechanism is novel and uses ant individual state prediction for a given colony as well as the corresponding pheromone field prediction from the previous frame to the current frame. Ant state prediction aims to guide ant clustering around cells of interest, while pheromone field prediction helps to accelerate the pheromone formation of the current frame using the Gaussian mixture model (GMM) approximation technique. To handle the problem of tracking closely interacting cells, we design a novel ant decision-making model based on the pheromone gradient information and heuristic function with two forms. The pheromone updating strategy is also a novel pheromone diffusion and deposit model to obtain the expected pheromone field for extracting cell states in collision and cohesion. We provide quantitative validation of the method using two challenging datasets characterized by cohesion and collision by comparing them with the results from recently reported approaches.

Yeast chemotropism: A paradigm shift in chemical gradient sensing

The ability of cells to direct their movement and growth in response to shallow chemical gradients is essential in the life cycles of all eukaryotic organisms. The signaling mechanisms underlying directional sensing in chemotactic cells have been well studied; however, relatively little is known about how chemotropic cells interpret chemical gradients. Recent studies of chemotropism in budding and fission yeast have revealed 2 quite different mechanisms-biased wandering of the polarity complex, and differential internalization of the receptor and G protein. Each of these mechanisms has been proposed to play a key role in decoding mating pheromone gradients. Here we explore how they may work together as 2 essential components of one gradient sensing machine.

Characterizing the Role of Yeast Cyclin Pcl1 in the Establishment of Pheromone Receptor Polarity

Chemotaxis (directed cell movement) and chemotropism (directed cell growth) are vital mechanisms essential to a variety of biological processes, including cell development, cancer metastasis, angiogenesis, and axon guidance. Both chemotaxis and chemotropism depend on the ability of the cell to interpret shallow and complex chemical gradients. One of the best‐studied models of eukaryotic directional sensing is the mating response of Saccharomyces cerevisiae (budding yeast). Yeast exist as two haploid mating types, MATa and MATα, that can sense pheromone gradients, chemotrop toward the closest mating partner, and eventually fuse at their tips to form diploid cells. In vegetative cells, the G protein coupled receptor (GPCR) that binds pheromone and its associated heterotrimeric G protein are uniformly distributed on the plasma membrane. Upon receptor activation, Gβγ is released from Gα and Gβ is rapidly phosphorylated on multiple residues. Free Gβγ signals through a MAP kinase cascade to trigger cell‐cycle arrest and changes in gene expression. Pheromone‐activated receptors are globally internalized and then reappear as polarized crescents that mark the chemotropic growth site, where the cell ultimately forms its mating projection. Previous studies have shown that the receptor crescents are visible before detectable actin polarization and that the receptors can polarize without actin‐dependent directed secretion [1]. On the contrary, inhibiting receptor internalization confers a moderate defect in gradient sensing [2]. A key question raised by these observations is how does the yeast cell interpret the external signals and establish the chemotropic growth site? From a directed genetic screen, we found that Pcl1 is critical for the establishment of receptor polarity. Pcl1 is a cyclin required by the cyclin dependent kinase Pho85, which has been implicated in polarization during budding. It is known that Gβ phosphorylation is crucial to receptor polarization, and Gβ is predicted to be a substrate of Pho85‐Pcl1 [3]. Additionally, our data indicated a genetic interaction between Gβ and Pcl1. Here, we show that Pcl1 localizes to the mating projection during the mating response. Moreover, Pcl1 and Gβ interacted directly on the plasma membrane of pheromone‐treated cells, as indicated by a Bimolecular fluorescence complementation assay. Lastly, Pho85 inactivation also appears to affect receptor polarity. Taken together, these results suggest that Pho85‐Pcl1 regulates polarization of the receptor by phosphorylating Gβ.Support or Funding InformationNational Science Foundation

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.

Directional ambiguity in trail-laying algorithms

We developed multi-agent models inspired by ant foraging. Previous research shows that ants obey Weber’s law and change their moving angles based on pheromone conditions. We constructed trail-laying models in which agents estimate the global pheromone gradients based on local pheromone gradients, which resulting in emergent Weber’s law. Agents were also admitted to use directional cues based on motions of other local agents to produce realistic curved paths. We also showed that re-estimation of pheromone gradients based on directional cues yields a balance between exploitation and exploration.

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.

Chemotaxis in the anisogamous brown alga Mutimo cylindricus (Cutleriaceae, Tilopteridales)

:Locomotion of female and male gametes of Mutimo cylindricus was analysed using a high-speed recording system. Female and male gametes morphologically differed in terms of their cell size and flagellar length. Male gametes swam significantly faster than female gametes; although, the flagellar waveforms were almost the same. Thus, the different swimming velocities of male and female gametes may have been due to differences in their cell size. Chemotactic male gametes treated with the sex pheromone from settled female gametes behaved in a similar manner to the male gametes of the isogamous Ectocarpus siliculosus. The results of the study revealed the following: (1) free-swimming male gametes exhibited thigmotaxis, (2) the beat frequency of the anterior flagellum and the swimming velocity decreased, (3) the deflection angle of the anterior flagellum increased, (4) the unilateral beat of the posterior flagellum was affected by the sex pheromone gradient, and (5) the tracking radius of male gametes around female gametes decreased.

Searching for a Mate: Pheromone-Directed Movement of the Benthic Diatom Seminavis robusta.

Diatoms are species-rich microalgae that often have a unique life cycle with vegetative cell size reduction followed by size restoration through sexual reproduction of two mating types (MT(+) and MT(-)). In the marine benthic diatom Seminavis robusta, mate-finding is mediated by an L-proline-derived diketopiperazine, a pheromone produced by the attracting mating type (MT(-)). Here, we investigate the movement patterns of cells of the opposite mating type (MT(+)) exposed to a pheromone gradient, using video monitoring and statistical modeling. We report that cells of the migrating mating type (MT(+)) respond to pheromone gradients by simultaneous chemotaxis and chemokinesis. Changes in movement behavior enable MT(+) cells to locate the direction of the pheromone source and to maximize their encounter rate towards it.

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.