Effect of cholesterol on the dipole potential of lipid membranes

The membrane dipole potential, ψd, is an electrical potential difference with a value typically in the range 150 – 350 mV (positive in the membrane interior) which is located in the lipid headgroup region of the membrane, between the linkage of the hydrocarbon chains to the phospholipid glycerol backbone and the adjacent aqueous solution. At its physiological level in animal plasma membranes (up to 50 mol%), cholesterol makes a significant contribution to ψd of approximately 65 mV; the rest arising from other lipid components of the membrane, in particular phospholipids. Via its effect on ψd, cholesterol may modulate the activity of membrane proteins. This could occur through preferential stabilization of protein conformational states. Based on its effect on ψd, cholesterol would be expected to favour protein conformations associated with a small local hydrophobic membrane thickness. Via its membrane condensing effect, which also produces an increase in ψd, cholesterol could further modulate interactions of polybasic cytoplasmic extensions of membrane proteins, in particular P-type ATPases, with anionic lipid headgroups on the membrane surface, thus leading to enhanced conformational stabilization effects and changes to ion pumping activity.Australian Research Counci

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Shotgun sequencing of Yersinia enterocolitica strain W22703 (biotype 2, serotype O:9): genomic evidence for oscillation between invertebrates and mammals

<p>Abstract</p> <p>Background</p> <p><it>Yersinia enterocolitica </it>strains responsible for mild gastroenteritis in humans are very diverse with respect to their metabolic and virulence properties. Strain W22703 (biotype 2, serotype O:9) was recently identified to possess nematocidal and insecticidal activity. To better understand the relationship between pathogenicity towards insects and humans, we compared the W22703 genome with that of the highly pathogenic strain 8081 (biotype1B; serotype O:8), the only <it>Y. enterocolitica </it>strain sequenced so far.</p> <p>Results</p> <p>We used whole-genome shotgun data to assemble, annotate and analyse the sequence of strain W22703. Numerous factors assumed to contribute to enteric survival and pathogenesis, among them osmoregulated periplasmic glucan, hydrogenases, cobalamin-dependent pathways, iron uptake systems and the <it>Yersinia </it>genome island 1 (YGI-1) involved in tight adherence were identified to be common to the 8081 and W22703 genomes. However, sets of ~550 genes revealed to be specific for each of them in comparison to the other strain. The plasticity zone (PZ) of 142 kb in the W22703 genome carries an ancient flagellar cluster Flg-2 of ~40 kb, but it lacks the pathogenicity island YAPI_Ye, the secretion system <it>ysa </it>and <it>yts1</it>, and other virulence determinants of the 8081 PZ. Its composition underlines the prominent variability of this genome region and demonstrates its contribution to the higher pathogenicity of biotype 1B strains with respect to W22703. A novel type three secretion system of mosaic structure was found in the genome of W22703 that is absent in the sequenced strains of the human pathogenic <it>Yersinia </it>species, but conserved in the genomes of the apathogenic species. We identified several regions of differences in W22703 that mainly code for transporters, regulators, metabolic pathways, and defence factors.</p> <p>Conclusion</p> <p>The W22703 sequence analysis revealed a genome composition distinct from other pathogenic <it>Yersinia enterocolitica </it>strains, thus contributing novel data to the <it>Y. enterocolitica </it>pan-genome. This study also sheds further light on the strategies of this pathogen to cope with its environments.</p

Serotonin synthesis, release and reuptake in terminals: a mathematical model

<p>Abstract</p> <p>Background</p> <p>Serotonin is a neurotransmitter that has been linked to a wide variety of behaviors including feeding and body-weight regulation, social hierarchies, aggression and suicidality, obsessive compulsive disorder, alcoholism, anxiety, and affective disorders. Full understanding of serotonergic systems in the central nervous system involves genomics, neurochemistry, electrophysiology, and behavior. Though associations have been found between functions at these different levels, in most cases the causal mechanisms are unknown. The scientific issues are daunting but important for human health because of the use of selective serotonin reuptake inhibitors and other pharmacological agents to treat disorders in the serotonergic signaling system.</p> <p>Methods</p> <p>We construct a mathematical model of serotonin synthesis, release, and reuptake in a single serotonergic neuron terminal. The model includes the effects of autoreceptors, the transport of tryptophan into the terminal, and the metabolism of serotonin, as well as the dependence of release on the firing rate. The model is based on real physiology determined experimentally and is compared to experimental data.</p> <p>Results</p> <p>We compare the variations in serotonin and dopamine synthesis due to meals and find that dopamine synthesis is insensitive to the availability of tyrosine but serotonin synthesis is sensitive to the availability of tryptophan. We conduct <it>in silico </it>experiments on the clearance of extracellular serotonin, normally and in the presence of fluoxetine, and compare to experimental data. We study the effects of various polymorphisms in the genes for the serotonin transporter and for tryptophan hydroxylase on synthesis, release, and reuptake. We find that, because of the homeostatic feedback mechanisms of the autoreceptors, the polymorphisms have smaller effects than one expects. We compute the expected steady concentrations of serotonin transporter knockout mice and compare to experimental data. Finally, we study how the properties of the the serotonin transporter and the autoreceptors give rise to the time courses of extracellular serotonin in various projection regions after a dose of fluoxetine.</p> <p>Conclusions</p> <p>Serotonergic systems must respond robustly to important biological signals, while at the same time maintaining homeostasis in the face of normal biological fluctuations in inputs, expression levels, and firing rates. This is accomplished through the cooperative effect of many different homeostatic mechanisms including special properties of the serotonin transporters and the serotonin autoreceptors. Many difficult questions remain in order to fully understand how serotonin biochemistry affects serotonin electrophysiology and vice versa, and how both are changed in the presence of selective serotonin reuptake inhibitors. Mathematical models are useful tools for investigating some of these questions.</p

The Shear Stress-Induced Transcription Factor KLF2 Affects Dynamics and Angiopoietin-2 Content of Weibel-Palade Bodies

BACKGROUND: The shear-stress induced transcription factor KLF2 has been shown to induce an atheroprotective phenotype in endothelial cells (EC) that are exposed to prolonged laminar shear. In this study we characterized the effect of the shear stress-induced transcription factor KLF2 on regulation and composition of Weibel-Palade bodies (WPBs) using peripheral blood derived ECs. METHODOLOGY AND PRINCIPAL FINDINGS: Lentiviral expression of KLF2 resulted in a 4.5 fold increase in the number of WPBs per cell when compared to mock-transduced endothelial cells. Unexpectedly, the average length of WPBs was significantly reduced: in mock-transduced endothelial cells WPBs had an average length of 1.7 µm versus 1.3 µm in KLF2 expressing cells. Expression of KLF2 abolished the perinuclear clustering of WPBs observed following stimulation with cAMP-raising agonists such as epinephrine. Immunocytochemistry revealed that WPBs of KLF2 expressing ECs were positive for IL-6 and IL-8 (after their upregulation with IL-1β) but lacked angiopoietin-2 (Ang2), a regular component of WPBs. Stimulus-induced secretion of Ang2 in KLF2 expressing ECs was greatly reduced and IL-8 secretion was significantly lower. CONCLUSIONS AND SIGNIFICANCE: These data suggest that KLF2 expression leads to a change in size and composition of the regulated secretory compartment of endothelial cells and alters its response to physiological stimuli

The ELBA Force Field for Coarse-Grain Modeling of Lipid Membranes

A new coarse-grain model for molecular dynamics simulation of lipid membranes is presented. Following a simple and conventional approach, lipid molecules are modeled by spherical sites, each representing a group of several atoms. In contrast to common coarse-grain methods, two original (interdependent) features are here adopted. First, the main electrostatics are modeled explicitly by charges and dipoles, which interact realistically through a relative dielectric constant of unity (). Second, water molecules are represented individually through a new parametrization of the simple Stockmayer potential for polar fluids; each water molecule is therefore described by a single spherical site embedded with a point dipole. The force field is shown to accurately reproduce the main physical properties of single-species phospholipid bilayers comprising dioleoylphosphatidylcholine (DOPC) and dioleoylphosphatidylethanolamine (DOPE) in the liquid crystal phase, as well as distearoylphosphatidylcholine (DSPC) in the liquid crystal and gel phases. Insights are presented into fundamental properties and phenomena that can be difficult or impossible to study with alternative computational or experimental methods. For example, we investigate the internal pressure distribution, dipole potential, lipid diffusion, and spontaneous self-assembly. Simulations lasting up to 1.5 microseconds were conducted for systems of different sizes (128, 512 and 1058 lipids); this also allowed us to identify size-dependent artifacts that are expected to affect membrane simulations in general. Future extensions and applications are discussed, particularly in relation to the methodology's inherent multiscale capabilities

Movement consistency during repetitive tool use action

The consistency and repeatability of movement patterns has been of long-standing interest in locomotor biomechanics, but less well explored in other domains. Tool use is one of such a domain; while the complex dynamics of the human-tool-environment system have been approached from various angles, to date it remains unknown how the rhythmicity of repetitive tool-using action emerges. To examine whether the spontaneously adopted movement frequency is a variable susceptible to individual execution approaches or emerges as constant behaviour, we recorded sawing motion across a range of 14 experimental conditions using various manipulations. This was compared to free and pantomimed arm movements. We found that a mean (SD) sawing frequency of 2.0 (0.4) Hz was employed across experimental conditions. Most experimental conditions did not significantly affect the sawing frequency, signifying the robustness of this spontaneously emerging movement. Free horizontal arm translation and miming of sawing was performed at half the movement frequency with more than double the excursion distance, showing that not all arm movements spontaneously emerge at the observed sawing parameters. Observed movement frequencies across all conditions could be closely predicted from movement time reference data for generic arm movements found in the Methods Time Measurement literature, highlighting a generic biomechanical relationship between the time taken for a given distance travelled underlying the observed behaviour. We conclude that our findings lend support to the hypothesis that repetitive movements during tool use are executed according to generic and predictable musculoskeletal mechanics and constraints, albeit in the context of the general task (sawing) and environmental constraints such as friction, rather than being subject to task-specific control or individual cognitive schemata

ICAR: endoscopic skull‐base surgery

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