Analysis of the diffusion of Ras2 in Saccharomyces cerevisiae using fluorescence recovery after photobleaching

Evaluation of Lipids and Lipid-Related Transcripts in Human and Ovine Theca Cells and an in Vitro Mouse Model Exposed to the Obesogen Chemical Tributyltin

BACKGROUND

Exposure to obesogenic chemicals has been reported to result in enhanced adipogenesis, higher adipose tissue accumulation, and reduced ovarian hormonal synthesis and follicular function. We have reported that organotins [tributyltin (TBT) and triphenyltin (TPT)] dysregulate cholesterol trafficking in ovarian theca cells, but, whether organotins also exert lipogenic effects on ovarian cells remains unexplored.

OBJECTIVE

We investigated if environmentally relevant exposures to organotins [TBT, TPT, or dibutyltin (DBT)] induce lipid dysregulation in ovarian theca cells and the role of the liver X receptor (LXR) in this effect. We also tested the effect of TBT on oocyte maturation and neutral lipid accumulation, and lipid-related transcript expression in cumulus cells and preimplantation embryos.

METHODS

Primary theca cell cultures derived from human and ovine ovaries were exposed to TBT, TPT, or DBT (1, 10, or 50   ng / ml ). The effect of these chemical exposures on neutral lipid accumulation, lipid abundance and composition, lipid homeostasis-related gene expression, and cytokine secretion was evaluated using liquid chromatography-mass spectrometry (LC-MS), inhibitor-based methods, cytokine secretion, and lipid ontology analyses. We also exposed murine cumulus-oocyte complexes to TBT and evaluated oocyte maturation, embryo development, and lipid homeostasis-related mRNA expression in cumulus cells and blastocysts.

RESULTS

Exposure to TBT resulted in higher intracellular neutral lipids in human and ovine primary theca cells. In ovine theca cells, this effect was dose-dependent, independent of cell stage, and partially mediated by LXR. DBT and TPT resulted in higher intracellular neutral lipids but to a lesser extent in comparison with TBT. More than 140 lipids and 9 cytokines were dysregulated in TBT-exposed human theca cells. Expression of genes involved in lipogenesis and fatty acid synthesis were higher in theca cells, as well as in cumulus cells and blastocysts exposed to TBT. However, TBT did not impact the rates of oocyte maturation or blastocyst development.

DISCUSSION

TBT induced dyslipidemia in primary human and ovine theca cells, which may be responsible for some of the TBT-induced fertility dysregulations reported in rodent models of TBT exposure. https://doi.org/10.1289/EHP13955.

Multiscale model of the physiological control of myocardial perfusion to delineate putative metabolic feedback mechanisms

Coronary blood flow is tightly regulated to ensure that myocardial oxygen delivery meets local metabolic demand via the concurrent action of myogenic, neural and metabolic mechanisms. Although several competing hypotheses exist, the specific nature of the local metabolic mechanism(s) remains poorly defined. To gain insights into the viability of putative metabolic feedback mechanisms and into the co-ordinated action of parallel regulatory mechanisms, we applied a multiscale modelling framework to analyse experimental data on coronary pressure, flow and myocardial oxygen delivery in the porcine heart in vivo. The modelling framework integrates a previously established lumped-parameter model of myocardial perfusion used to account for transmural haemodynamic variations and a simple vessel mechanics model used to simulate the vascular tone in each of three myocardial layers. Vascular tone in the resistance vessel mechanics model is governed by input stimuli from the myogenic, metabolic and autonomic control mechanisms. Seven competing formulations of the metabolic feedback mechanism are implemented in the modelling framework, and associated model simulations are compared with experimental data on coronary pressures and flows under a range of experimental conditions designed to interrogate the governing control mechanisms. Analysis identifies a maximally probable metabolic mechanism among the seven tested models, in which production of a metabolic signalling factor is proportional to myocardial oxygen consumption and delivery is proportional to flow. Finally, the identified model is validated based on comparisons of simulations with data on the myocardial perfusion response to conscious exercise that were not used for model identification. KEY POINTS: Although several competing hypotheses exist, we lack knowledge of specific nature of the metabolic mechanism(s) governing regional myocardial perfusion. Moreover, we lack an understanding of how parallel myogenic, adrenergic/autonomic and metabolic mechanisms work together to regulatory oxygen delivery in the beating heart. We have developed a multiscale modelling framework to test competing hypotheses against experimental data on coronary pressure, flow and myocardial oxygen delivery in the porcine heart in vivo. The analysis identifies a maximally probable metabolic mechanism among seven tested models, in which the production of a metabolic signalling factor is proportional to myocardial oxygen consumption and delivery is proportional to flow.

Membrane thickness, lipid phase and sterol type are determining factors in the permeability of membranes to small solutes

Cell membranes provide a selective semi-permeable barrier to the passive transport of molecules. This property differs greatly between organisms. While the cytoplasmic membrane of bacterial cells is highly permeable for weak acids and glycerol, yeasts can maintain large concentration gradients. Here we show that such differences can arise from the physical state of the plasma membrane. By combining stopped-flow kinetic measurements with molecular dynamics simulations, we performed a systematic analysis of the permeability of a variety of small molecules through synthetic membranes of different lipid composition to obtain detailed molecular insight into the permeation mechanisms. While membrane thickness is an important parameter for the permeability through fluid membranes, the largest differences occur when the membranes transit from the liquid-disordered to liquid-ordered and/or to gel state, which is in agreement with previous work on passive diffusion of water. By comparing our results with in vivo measurements from yeast, we conclude that the yeast membrane exists in a highly ordered and rigid state, which is comparable to synthetic saturated DPPC-sterol membranes.

Membrane permeability of small molecules depends on the composition of the lipid bilayer. Here, authors compare permeability measured on membranes in different physical states and conclude that the yeast membrane exists in a highly ordered phase.

Cell patterning by secretion-induced plasma membrane flows

Cells self-organize using reaction-diffusion and fluid-flow principles. Whether bulk membrane flows contribute to cell patterning has not been established. Here, using mathematical modeling, optogenetics, and synthetic probes, we show that polarized exocytosis causes lateral membrane flows away from regions of membrane insertion. Plasma membrane–associated proteins with sufficiently low diffusion and/or detachment rates couple to the flows and deplete from areas of exocytosis. In rod-shaped fission yeast cells, zones of Cdc42 GTPase activity driving polarized exocytosis are limited by GTPase activating proteins (GAPs). We show that membrane flows pattern the GAP Rga4 distribution and that coupling of a synthetic GAP to membrane flows is sufficient to establish the rod shape. Thus, membrane flows induced by Cdc42-dependent exocytosis form a negative feedback restricting the zone of Cdc42 activity.

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning-based estimation in FRAP. LAY DESCRIPTION: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy-based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed-up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions.

A Highly Accurate Pixel-Based FRAP Model Based on Spectral-Domain Numerical Methods

We introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluorescence recovery after photobleaching data. The model covers pure diffusion and diffusion and binding (reaction-diffusion) with immobile binding sites, as well as arbitrary bleach region shapes. Fitting of the model is supported using both conventional recovery-curve-based estimation and pixel-based estimation, in which all individual pixels in the data are utilized. The model explicitly accounts for multiple bleach frames, diffusion (and binding) during bleaching, and bleaching during imaging. To our knowledge, no other fluorescence recovery after photobleaching framework incorporates all these model features and estimation methods. We thoroughly validate the model by comparison to stochastic simulations of particle dynamics and find it to be highly accurate. We perform simulation studies to compare recovery-curve-based estimation and pixel-based estimation in realistic settings and show that pixel-based estimation is the better method for parameter estimation as well as for distinguishing pure diffusion from diffusion and binding. We show that accounting for multiple bleach frames is important and that the effect of neglecting this is qualitatively different for the two estimation methods. We perform a simple experimental validation showing that pixel-based estimation provides better agreement with literature values than recovery-curve-based estimation and that accounting for multiple bleach frames improves the result. Further, the software developed in this work is freely available online.

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries-both in two and three dimensions-namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation-called the Digital Confocal Microscopy Suite or DCMS-can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.

Exploration and stabilization of Ras1 mating zone: A mechanism with positive and negative feedbacks

In mating fission yeast cells, sensing and response to extracellular pheromone concentrations occurs through an exploratory Cdc42 patch that stochastically samples the cell cortex before stabilizing towards a mating partner. Active Ras1 (Ras1-GTP), an upstream regulator of Cdc42, and Gap1, the GTPase-activating protein for Ras1, localize at the patch. We developed a reaction-diffusion model of Ras1 patch appearance and disappearance with a positive feedback by a Guanine nucleotide Exchange Factor (GEF) and Gap1 inhibition. The model is based on new estimates of Ras1-GDP, Ras1-GTP and Gap1 diffusion coefficients and rates of cytoplasmic exchange studied by FRAP. The model reproduces exploratory patch behavior and lack of Ras1 patch in cells lacking Gap1. Transition to a stable patch can occur by change of Gap1 rates constants or local increase of the positive feedback rate constants. The model predicts that the patch size and number of patches depend on the strength of positive and negative feedbacks. Measurements of Ras1 patch size and number in cells overexpressing the Ras1 GEF or Gap1 are consistent with the model.

Unicellular fission yeasts mate by fusing with partners of the opposite mating type. Each pair member grows towards its selected partner that signals its presence through secreted pheromone. The process of partner selection occurs through an exploratory patch (containing activated signaling protein Cdc42 and upstream regulator Ras1) that assembles and disassembles on the cell cortex, stabilizing in regions of higher opposite pheromone concentration. We present a computational model of the molecular mechanisms driving the dynamical pattern of patch exploration and stabilization. The model is based on reaction and diffusion along the curved cell membrane, with diffusion coefficients measured experimentally. In the model, a positive Ras1 activation feedback loop generates a patch containing most of the activating protein (Ras1 GEF). The fast diffusing inhibitor Gap1 that is recruited locally from the cytoplasm spreads on the cell membrane, limiting patch size and causing its decay. Spontaneous reinitiation of Ras1 activation elsewhere on the cortex provides a mechanism for exploration. Transition of the system’s behavior to that of a single stable patch is possible upon simulated pheromone sensing. The computational model provides predictions for the number of patches and patch size dependence on parameters that we tested experimentally.

Steric exclusion and protein conformation determine the localization of plasma membrane transporters

The plasma membrane (PM) of Saccharomyces cerevisiae contains membrane compartments, MCC/eisosomes and MCPs, named after the protein residents Can1 and Pma1, respectively. Using high-resolution fluorescence microscopy techniques we show that Can1 and the homologous transporter Lyp1 are able to diffuse into the MCC/eisosomes, where a limited number of proteins are conditionally trapped at the (outer) edge of the compartment. Upon addition of substrate, the immobilized proteins diffuse away from the MCC/eisosomes, presumably after taking a different conformation in the substrate-bound state. Our data indicate that the mobile fraction of all integral plasma membrane proteins tested shows extremely slow Brownian diffusion through most of the PM. We also show that proteins with large cytoplasmic domains, such as Pma1 and synthetic chimera of Can1 and Lyp1, are excluded from the MCC/eisosomes. We hypothesize that the distinct localization patterns found for these integral membrane proteins in S. cerevisiae arises from a combination of slow lateral diffusion, steric exclusion, and conditional trapping in membrane compartments.

Multi-Fractional Analysis of Molecular Diffusion in Polymer Multilayers by FRAP: A New Simulation-Based Approach

Comprehensive analysis of the multifractional molecular diffusion provides a deeper understanding of the diffusion phenomenon in the fields of material science, molecular and cell biology, advanced biomaterials, etc. Fluorescence recovery after photobleaching (FRAP) is commonly employed to probe the molecular diffusion. Despite FRAP being a very popular method, it is not easy to assess multifractional molecular diffusion due to limited possibilities of approaches for analysis. Here we present a novel simulation-optimization-based approach (S-approach) that significantly broadens possibilities of the analysis. In the S-approach, possible fluorescence recovery scenarios are primarily simulated and afterward compared with a real measurement while optimizing parameters of a model until a sufficient match is achieved. This makes it possible to reveal multifractional molecular diffusion. Fluorescent latex particles of different size and fluorescein isothiocyanate in an aqueous medium were utilized as test systems. Finally, the S-approach has been used to evaluate diffusion of cytochrome c loaded into multilayers made of hyaluronan and polylysine. Software for evaluation of multifractional molecular diffusion by S-approach has been developed aiming to offer maximal versatility and user-friendly way for analysis.

Fluorescence recovery after photobleaching in material and life sciences: putting theory into practice

Fluorescence recovery after photobleaching (FRAP) is a versatile tool for determining diffusion and interaction/binding properties in biological and material sciences. An understanding of the mechanisms controlling the diffusion requires a deep understanding of structure–interaction–diffusion relationships. In cell biology, for instance, this applies to the movement of proteins and lipids in the plasma membrane, cytoplasm and nucleus. In industrial applications related to pharmaceutics, foods, textiles, hygiene products and cosmetics, the diffusion of solutes and solvent molecules contributes strongly to the properties and functionality of the final product. All these systems are heterogeneous, and accurate quantification of the mass transport processes at the local level is therefore essential to the understanding of the properties of soft (bio)materials. FRAP is a commonly used fluorescence microscopy-based technique to determine local molecular transport at the micrometer scale. A brief high-intensity laser pulse is locally applied to the sample, causing substantial photobleaching of the fluorescent molecules within the illuminated area. This causes a local concentration gradient of fluorescent molecules, leading to diffusional influx of intact fluorophores from the local surroundings into the bleached area. Quantitative information on the molecular transport can be extracted from the time evolution of the fluorescence recovery in the bleached area using a suitable model. A multitude of FRAP models has been developed over the years, each based on specific assumptions. This makes it challenging for the non-specialist to decide which model is best suited for a particular application. Furthermore, there are many subtleties in performing accurate FRAP experiments. For these reasons, this review aims to provide an extensive tutorial covering the essential theoretical and practical aspects so as to enable accurate quantitative FRAP experiments for molecular transport measurements in soft (bio)materials.

Ato protein interactions in yeast plasma membrane revealed by fluorescence lifetime imaging (FLIM)

Each of the three plasma membrane Ato proteins is involved in ammonium signalling and the development of yeast colonies. This suggests that although these proteins are homologous, they do not functionally substitute for each other, but may form a functional complex. Here, we present a detailed combined FRET, FLIM and photobleaching study, which enabled us to detect interactions between Ato proteins found in distinct compartments of yeast cells. We thus show that the proteins Ato1p and Ato2p interact and can form complexes when present in the plasma membrane. No interaction was detected between Ato1p and Ato3p or Ato2p and Ato3p. In addition, using specially prepared strains, we were able to detect an interaction between molecules of the same Ato protein, namely Ato1p-Ato1p and Ato3p-Ato3p, but not Ato2p-Ato2p.

Phosphatidylserine is polarized and required for proper Cdc42 localization and for development of cell polarity

Polarity is key to the function of eukaryotic cells. On the establishment of a polarity axis, cells can vectorially target secretion, generating an asymmetric distribution of plasma membrane proteins. From Saccharomyces cerevisiae to mammals, the small GTPase Cdc42 is a pivotal regulator of polarity. We used a fluorescent probe to visualize the distribution of phosphatidylserine in live S. cerevisiae. Remarkably, phosphatidylserine was polarized in the plasma membrane, accumulating in bud necks, the bud cortex and the tips of mating projections. Polarization required vectorial delivery of phosphatidylserine-containing secretory vesicles, and phosphatidylserine was largely excluded from endocytic vesicles, contributing to its polarized retention. Mutants lacking phosphatidylserine synthase had impaired polarization of the Cdc42 complex, leading to a delay in bud emergence, and defective mating. The addition of lysophosphatidylserine resulted in resynthesis and polarization of phosphatidylserine, as well as repolarization of Cdc42. The results indicate that phosphatidylserine--and presumably its polarization--are required for optimal Cdc42 targeting and activation during cell division and mating.

Roles of Ras1 membrane localization during Candida albicans hyphal growth and farnesol response

Many Ras GTPases localize to membranes via C-terminal farnesylation and palmitoylation, and localization regulates function. In Candida albicans, a fungal pathogen of humans, Ras1 links environmental cues to morphogenesis. Here, we report the localization and membrane dynamics of Ras1, and we characterize the roles of conserved C-terminal cysteine residues, C287 and C288, which are predicted sites of palmitoylation and farnesylation, respectively. GFP-Ras1 is localized uniformly to plasma membranes in both yeast and hyphae, yet Ras1 plasma membrane mobility was reduced in hyphae compared to that in yeast. Ras1-C288S was mislocalized to the cytoplasm and could not support hyphal development. Ras1-C287S was present primarily on endomembranes, and strains expressing ras1-C287S were delayed or defective in hyphal induction depending on the medium used. Cells bearing constitutively activated Ras1-C287S or Ras1-C288S, due to a G13V substitution, showed increased filamentation, suggesting that lipid modifications are differentially important for Ras1 activation and effector interactions. The C. albicans autoregulatory molecule, farnesol, inhibits Ras1 signaling through adenylate cyclase and bears structural similarities to the farnesyl molecule that modifies Ras1. At lower concentrations of farnesol, hyphal growth was inhibited but Ras1 plasma membrane association was not altered; higher concentrations of farnesol led to mislocalization of Ras1 and another G protein, Rac1. Furthermore, farnesol inhibited hyphal growth mediated by cytosolic Ras1-C288SG13V, suggesting that farnesol does not act through mechanisms that depend on Ras1 farnesylation. Our findings imply that Ras1 is farnesylated and palmitoylated, and that the Ras1 stimulation of adenylate cyclase-dependent phenotypes can occur in the absence of these lipid modifications.

Studying Smad2 intranuclear diffusion dynamics by mathematical modelling of FRAP experiments

We combine Fluorescence Recovery After Photobleaching (FRAP) experiments with mathematical modelling to study the dynamics inside the nucleus of both the TGF-β-sensitive transcriptional regulator Smad2, and Green-Fluorescent Protein (GFP). We show how combining modelling with bleaching strips of different areas allows a rigorous test of whether or not a protein is moving via diffusion as a single species. As noted recently by others, it is important to consider diffusion during the bleaching process. Neglecting it can cause serious error. Also, it is possible to use the bleaching process itself to provide an extra consistency test to the models predicting the recovery. With our method we show that the dynamics of GFP are consistent with it diffusing as a single species in a uniform environment in which flow is negligible. In contrast, the dynamics of the intracellular signal transducer Smad2 are never consistent with it moving as a single species via simple diffusion in a homogeneous environment without flow. Adding TGF-β slows down the dynamics of Smad2 but even without TGF-β, the Smad2 dynamics are influenced by one or more of: association, flow, and inhomogeneity in space of the dynamics. We suggest that the dynamics inside cells of many proteins may be poorly described by simple diffusion of a single species, and that our methodology provides a general and powerful way to test this hypothesis.