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

Automated line scan analysis to quantify biosensor activity at the cell edge

Biosensors are valuable tools used to image the subcellular localization and kinetics of protein activity in living cells. Signaling at the edge of motile cells that regulates cell protrusion and retraction is important in many aspects of cell physiology, and frequently studied using biosensors. However, quantitation and interpretation is limited by the heterogeneity of this signaling behavior; automated analytical approaches are required to systematically extract large data sets from biosensor studies for statistical analysis. Here we describe an automated analysis to relate the velocity at specific points along the cell edge with biosensor activity in adjoining regions. Time series of biosensor images are processed to interpolate a smooth edge of the cell at each time point. Profiles of biosensor activity (‘line scans’) are then calculated along lines perpendicular to the cell edge. An energy minimization method is used to calculate a velocity associated with each line scan. Sorting line scans by the proximal velocity has generated novel biological insights, as exemplified by analysis of the Src merobody biosensor. With the large data sets that can be generated automatically by this program, conclusions can be drawn that are not apparent from qualitative or ‘manual’ quantitative techniques. Our ‘LineScan’ software includes a graphical user interface (GUI) to facilitate application in other studies. It is available at hahnlab.com and is exemplified here in a study using the RhoC FLARE biosensor

Spatial models of pattern formation during phagocytosis

Phagocytosis, the biological process in which cells ingest large particles such as bacteria, is a key component of the innate immune response. Fcγ receptor (FcγR)-mediated phagocytosis is initiated when these receptors are activated after binding immunoglobulin G (IgG). Receptor activation initiates a signaling cascade that leads to the formation of the phagocytic cup and culminates with ingestion of the foreign particle. In the experimental system termed “frustrated phagocytosis”, cells attempt to internalize micropatterned disks of IgG. Cells that engage in frustrated phagocytosis form “rosettes” of actin-enriched structures called podosomes around the IgG disk. The mechanism that generates the rosette pattern is unknown. We present data that supports the involvement of Cdc42, a member of the Rho family of GTPases, in pattern formation. Cdc42 acts downstream of receptor activation, upstream of actin polymerization, and is known to play a role in polarity establishment. Reaction-diffusion models for GTPase spatiotemporal dynamics exist. We demonstrate how the addition of negative feedback and minor changes to these models can generate the experimentally observed rosette pattern of podosomes. We show that this pattern formation can occur through two general mechanisms. In the first mechanism, an intermediate species forms a ring of high activity around the IgG disk, which then promotes rosette organization. The second mechanism does not require initial ring formation but relies on spatial gradients of intermediate chemical species that are selectively activated over the IgG patch. Finally, we analyze the models to suggest experiments to test their validity

Enabled Negatively Regulates Diaphanous-Driven Actin Dynamics In Vitro and In Vivo

Actin regulators facilitate cell migration by controlling cell protrusion architecture and dynamics. As the behavior of individual actin regulators becomes clear, we must address why cells require multiple regulators with similar functions and how they cooperate to create diverse protrusions. We characterized Diaphanous (Dia) and Enabled (Ena) as a model, using complementary approaches: cell culture, biophysical analysis, and Drosophila morphogenesis. We found that Dia and Ena have distinct biochemical properties that contribute to the different protrusion morphologies each induces. Dia is a more processive, faster elongator, paralleling the long, stable filopodia it induces in vivo, while Ena promotes filopodia with more dynamic changes in number, length, and lifetime. Acting together, Ena and Dia induce protrusions distinct from those induced by either alone, with Ena reducing Dia-driven protrusion length and number. Consistent with this, EnaEVH1 binds Dia directly and inhibits DiaFH1FH2-mediated nucleation in vitro. Finally, Ena rescues hemocyte migration defects caused by activated Dia

Automated line scan analysis to quantify biosensor activity at the cell edge

Biosensors are valuable tools used to image the subcellular localization and kinetics of protein activity in living cells. Signaling at the edge of motile cells that regulates cell protrusion and retraction is important in many aspects of cell physiology, and frequently studied using biosensors. However, quantitation and interpretation is limited by the heterogeneity of this signaling behavior; automated analytical approaches are required to systematically extract large data sets from biosensor studies for statistical analysis. Here we describe an automated analysis to relate the velocity at specific points along the cell edge with biosensor activity in adjoining regions. Time series of biosensor images are processed to interpolate a smooth edge of the cell at each time point. Profiles of biosensor activity (‘line scans’) are then calculated along lines perpendicular to the cell edge. An energy minimization method is used to calculate a velocity associated with each line scan. Sorting line scans by the proximal velocity has generated novel biological insights, as exemplified by analysis of the Src merobody biosensor. With the large data sets that can be generated automatically by this program, conclusions can be drawn that are not apparent from qualitative or ‘manual’ quantitative techniques. Our ‘LineScan’ software includes a graphical user interface (GUI) to facilitate application in other studies. It is available at hahnlab.com and is exemplified here in a study using the RhoC FLARE biosensor

Mollusca: Bivalvia

Bivalvia (Lamellibranchiata, Pelecypoda) is a large class of laterally compressed animals characterized by two calcified variably flattened to deeply cupped valves that are attached to each other at the dorsal surface with teeth spanned by a flexible hinge ligament. Bivalve larvae are amazingly similar from fertilization until metamorphosis which makes learning larval anatomy easy, but identification of larvae from different species difficult when samples are collected from the wild environment. Based on their overall morphology, bivalves are separated into five subclasses but are practically placed into the following groups: clams, cockles, mussels, scallops, and oysters. This chapter discusses the anatomy of the clam and then describes important anatomic differences in oysters, scallops, mussels, and cockles. The tubules in scallops have been described as more acinar-like. The absorptive cells in histologic sections often appear enlarged, are highly vacuolated and have been described as adipocyte-like