Hfsp Journal最新文献

The organization of cytoplasm in excitable cells was a largely ignored factor when mathematical models were developed to understand intracellular calcium and secretory behavior. Here we employed a combination of fluorescent evanescent and transmitted light microscopy to explore the F-actin cytoskeletal organization in the vicinity of secretory sites in cultured bovine chromaffin cells. This technique and confocal fluorescent microscopy show chromaffin granules associated with the borders of cortical cytoskeletal cages forming an intricate tridimensional network. Furthermore, the overexpression of SNAP-25 in these cells also reveals the association of secretory machinery clusters with the borders of these cytoskeletal cages. The importance of these F-actin cage borders is stressed when granules appear to interact and remain associated during exocytosis visualized in acridin orange loaded vesicles. These results will prompt us to propose a model of cytoskeletal cages, where the secretory machinery is associated with its borders. Both the calcium level and the secretory response are enhanced in this geometrical arrangement when compared with a random distribution of the secretory machinery that is not restricted to the borders of the cage.

In mammals the concentration of blood glucose is kept close to 5 mmol∕l. Different cell types in the islet of Langerhans participate in the control of glucose homeostasis. β-cells, the most frequent type in pancreatic islets, are responsible for the synthesis, storage, and release of insulin. Insulin, released with increases in blood glucose promotes glucose uptake into the cells. In response to glucose changes, pancreatic α-, β-, and δ-cells regulate their electrical activity and Ca(2+) signals to release glucagon, insulin, and somatostatin, respectively. While all these signaling steps are stimulated in hypoglycemic conditions in α-cells, the activation of these events require higher glucose concentrations in β and also in δ-cells. The stimulus-secretion coupling process and intracellular Ca(2+) ([Ca(2+)](i)) dynamics that allow β-cells to secrete is well-accepted. Conversely, the mechanisms that regulate α- and δ-cell secretion are still under study. Here, we will consider the glucose-induced signaling mechanisms in each cell type and the mathematical models that explain Ca(2+) dynamics.

DURING SHORT BURSTS OF NEURONAL ACTIVITY, CHANGES IN THE EFFICACY OF NEUROTRANSMITTER RELEASE ARE GOVERNED PRIMARILY BY TWO COUNTERACTING PROCESSES: (1) Ca(2+)-dependent elevations of vesicle release probability and (2) depletion of synaptic vesicles. The dynamic interplay of both processes contributes to the expression of activity-dependent synaptic plasticity. Here, we exploited various facets of short-term plasticity at the Drosophila neuromuscular junction to dissect these two processes. This enabled us to rigorously analyze different models of synaptic vesicle pools in terms of their size and mobilization properties. Independent of the specific model, we estimate approximately 300 readily releasable vesicles with an average release probability of approximately 50% in 1 mM extracellular calcium ( approximately 5% in 0.4 mM extracellular calcium) under resting conditions. The models also helped interpreting the altered short-term plasticity of the previously reported mutant of the active zone component Bruchpilot (BRP). Finally, our results were independently confirmed through fluctuation analysis. Our data reveal that the altered short-term plasticity observed in BRP mutants cannot be accounted for by delocalized Ca(2+) channels alone and thus suggest an additional role of BRP in short-term plasticity.

Calcium is a ubiquitous second messenger that mediates vital physiological responses such as fertilization, secretion, gene expression, or apoptosis. Given this variety of processes mediated by Ca(2+), these signals are highly organized both in time and space to ensure reliability and specificity. This review deals with the spatiotemporal organization of the Ca(2+) signaling pathway in electrically nonexcitable cells in which InsP(3) receptors are by far the most important Ca(2+) channels. We focus on the aspects of this highly regulated dynamical system for which an interplay between experiments and modeling is particularly fruitful. In particular, the importance of the relative densities of the different InsP(3) receptor subtypes will be discussed on the basis of a modeling approach linking the steady-state behaviors of these channels in electrophysiological experiments with their behavior in a cellular environment. Also, the interplay between InsP(3) metabolism and Ca(2+) oscillations will be considered. Finally, we discuss the relationships between stochastic openings of the Ca(2+) releasing channels at the microscopic level and the coordinated, regular behavior observed at the whole cell level on the basis of a combined experimental and modeling approach.

Pancreatic β-cells release insulin in response to increased glucose levels. Compared to isolated β-cells, β-cells embedded within the islets of Langerhans network exhibit a coordinated and greater insulin secretion response to glucose. This coordinated activity is considered to rely on gap-junctions. We investigated the β-cell electrophysiology and the calcium dynamics in islets in response to glucose gradients. While at constant glucose the network of β-cells fires in a correlated fashion, a glucose gradient induces a sharp division into an active and an inactive part. We hypothesized that this sharp transition is mediated by the specific properties of the gap-junctions. We used a mathematical model of the β-cell electrophysiology in islets to discuss possible origins of this sharp transition in electrical activity. In silico, gap-junctions were required for such a transition. However, the small width of transition was only found when a stochastic variability of the expression of key transmembrane proteins, such as the ATP-dependent potassium channel, was included. The agreement with experimental data was further improved by assuming a delay of gap-junction currents, which points to a role of spatial constraints in the β-cell. This result clearly demonstrates the power of mathematical modeling in disentangling causal relationships in complex systems.

Understanding causal relationships between genotypes and phenotypes is a long-standing aim in genetics. In addition to high-throughput technologies that allow the measurement of many DNA variants it is possible to measure gene expression in specific tissues using array technology. "Systems genetics" is an emerging discipline that combines dense data on genotypes, gene expression, and outcome phenotypes to answer fundamental questions about causal pathways from genotype to phenotype. A recent paper by Chen et al. [Mol. Syst. Biol. 5, 310 (2009)] addressed the question of whether relative levels of mRNA expression help to elucidate causal paths from genotype to phenotype, using drug resistance in yeast as a model. The authors show that data on genetic markers and on gene expression, measured in a drug-free environment, can be combined to predict the growth of a yeast strain in the presence of a drug. They argue that their prediction can be used to identify causal pathways and for a subset of the genes used in prediction, the authors demonstrate that these genes cause an effect on drug sensitivity by deleting the gene or overexpressing it or swapping alleles between strains of yeast. This approach can also be applied to other species, including humans, and may become a tool in the study of personalized medicine.

This article reviews multiphase descriptions of the fluid mechanics of cytoplasm in crawling cells and growing bacterial biofilms. These two systems involve gels, which are mixtures composed of a polymer network permeated by water. The fluid mechanics of these systems is essential to their biological function and structure. Their mathematical descriptions must account for the mechanics of the polymer, the water, and the interaction between these two phases. This review focuses on multiphase flow models because this framework is natural for including the relative motion between the phases, the exchange of material between phases, and the additional stresses within the network that arise from nonspecific chemical interactions and the action of molecular motors. These models have been successful in accounting for how different forces are generated and transmitted to achieve cell motion and biofilm growth and they have demonstrated how emergent structures develop though the interactions of the two phases. A short description of multiphase flow models of tumor growth is included to highlight the flexibility of the model in describing diverse biological applications.

Proteins form the basis of a wide range of biological materials such as hair, skin, bone, spider silk, or cells, which play an important role in providing key functions to biological systems. The focus of this article is to discuss how protein materials are capable of balancing multiple, seemingly incompatible properties such as strength, robustness, and adaptability. To illustrate this, we review bottom-up materiomics studies focused on the mechanical behavior of protein materials at multiple scales, from nano to macro. We focus on alpha-helix based intermediate filament proteins as a model system to explain why the utilization of hierarchical structural features is vital to their ability to combine strength, robustness, and adaptability. Experimental studies demonstrating the activation of angiogenesis, the growth of new blood vessels, are presented as an example of how adaptability of structure in biological tissue is achieved through changes in gene expression that result in an altered material structure. We analyze the concepts in light of the universality and diversity of the structural makeup of protein materials and discuss the findings in the context of potential fundamental evolutionary principles that control their nanoscale structure. We conclude with a discussion of multiscale science in biology and de novo materials design.

Neural activity can be captured by state-of-the-art optical imaging methods although the analysis of the resulting data sets is often manual and not standardized. Therefore, laboratories using large-scale calcium imaging eagerly await software toolboxes that can automate the process of identifying cells and inferring spikes. An algorithm proposed and implemented in a recent paper by Mukamel et al. [Neuron 63, 747-760 (2009)] used independent component analysis and offers significant improvements over conventional methods. The approach should be widely applicable, as tested with data obtained from the mouse cerebellum, neocortex, and spinal cord. The emergence of analysis tools in parallel with the rapid advances in optical imaging is an exciting development that will stimulate new discoveries and further elucidate the functions of neural circuits.