Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Gender Differences in Publication Output: Towards an Unbiased Metric of Research Performance

We examined the publication records of a cohort of 168 life scientists in the field of ecology and evolutionary biology to assess gender differences in research performance. Clear discrepancies in publication rate between men and women appear very early in their careers and this has consequences for the subsequent citation of their work. We show that a recently proposed index designed to rank scientists fairly is in fact strongly biased against female researchers, and advocate a modified index to assess men and women on a more equitable basis

Multi-scale investigation of uranium attenuation by arsenic at an abandoned uranium mine, South Terras

Detailed mineralogical analysis of soils from the UK’s historical key uranium mine, South Terras, was performed to elucidate the mechanisms of uranium degradation and migration in the 86 years since abandonment. Soils were sampled from the surface (0 – 2 cm) and near-surface (25 cm) in two distinct areas of ore processing activities. Bulk soil analysis revealed the presence of high concentrations of uranium (<1690 ppm), arsenic (1830 ppm) and beryllium (~250 ppm), suggesting pedogenic weathering of the country rock and ore extraction processes to be the mechanisms of uranium ore degradation. Micro-focus XRF analysis indicated the association of uranium with arsenic, phosphate and copper; µ-XRD data confirmed the presence of the uranyl-arsenate minerals metazeunerite (Cu(UO2)2(AsO4)2·8H2O) and metatorbernite (Cu(UO2)2(PO4)2·8H2O) to be ubiquitous. Our data are consistent with the solid solution of these two uranyl-mica minerals, not previously observed at uranium-contaminated sites. Crystallites of uranyl-mica minerals were observed to coat particles of jarosite and muscovite, suggesting that the mobility of uranium from degraded ores is attenuated by co-precipitation with arsenic and phosphate, which was not previously considered at this site

Genetic changes that increase 5-hydroxymethyl furfural resistance in ethanol-producing Escherichia coli LY180

The ability of a biocatalyst to tolerate furan inhibitors present in hemicellulose hydrolysates is important for the production of renewable chemicals. This study shows EMFR9, a furfural-tolerant mutant of ethanologenic E. coli LY180, has also acquired tolerance to 5-hydroxymethyl furfural (5-HMF). The mechanism of action of 5-HMF and furfural appear similar. Furan tolerance results primarily from lower expression of yqhD and dkgA, two furan reductases with a low Km for NADPH. Furan tolerance was also increased by adding plasmids encoding a NADPH/NADH transhydrogenase (pntAB). Together, these results support the hypothesis that the NADPH-dependent reduction of furans by YqhD and DkgA inhibits growth by competing with biosynthesis for this limiting cofactor

The female menstrual cycle does not influence testosterone concentrations in male partners

<p>Abstract</p> <p>Background</p> <p>The time of ovulation has since long been believed to be concealed to male heterosexual partners. Recent studies have, however, called for revision of this notion. For example, male testosterone concentrations have been shown to increase in response to olfactory ovulation cues, which could be biologically relevant by increasing sexual drive and aggressiveness. However, this phenomenon has not previously been investigated in real-life human settings. We therefore thought it of interest to test the hypothesis that males' salivary testosterone concentrations are influenced by phases of their female partners' menstrual cycle; expecting a testosterone peak at ovulation.</p> <p>Methods</p> <p>Thirty young, healthy, heterosexual couples were recruited. During the course of 30-40 days, the women registered menses and ovulation, while the men registered sexual activity, physical exercise, alcohol intake and illness (confounders), and obtained daily saliva samples for testosterone measurements. All data, including the registered confounders, were subjected to multiple regression analysis.</p> <p>Results</p> <p>In contrast to the hypothesis, the ovulation did not affect the testosterone levels, and the resulting testosterone profile during the menstrual cycle was on the average flat. The specific main hypothesis, that male testosterone levels on the day of ovulation would be higher than day 4 of the cycle, was clearly contradicted by a type II error(β)-analysis (< 14.3% difference in normalized testosterone concentration; β = 0.05).</p> <p>Conclusions</p> <p>Even though an ovulation-related salivary testosterone peak was observed in individual cases, no significant effect was found on a group level.</p

Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation

Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms

Systems Imaging of the Immune Synapse

Three-dimensional live cell imaging of the interaction of T cells with antigen presenting cells (APC) visualizes the subcellular distributions of signaling intermediates during T cell activation at thousands of resolved positions within a cell. These information-rich maps of local protein concentrations are a valuable resource in understanding T cell signaling. Here, we describe a protocol for the efficient acquisition of such imaging data and their computational processing to create four-dimensional maps of local concentrations. This protocol allows quantitative analysis of T cell signaling as it occurs inside live cells with resolution in time and space across thousands of cells

The desmosomal cadherin desmoglein-3 acts as a keratinocyte anti-stress protein via suppression of p53

Desmoglein-3 (Dsg3), the Pemphigus Vulgaris (PV) antigen (PVA), plays an essential role in keratinocyte cell–cell adhesion and regulates various signaling pathways involved in the progression and metastasis of cancer where it is upregulated. We show here that expression of Dsg3 impacts on the expression and function of p53, a key transcription factor governing the responses to cellular stress. Dsg3 depletion increased p53 expression and activity, an effect enhanced by treating cells with UVB, mechanical stress and genotoxic drugs, whilst increased Dsg3 expression resulted in the opposite effects. Such a pathway in the negative regulation of p53 by Dsg3 was Dsg3 specific since neither E-cadherin nor desmoplakin knockdown caused similar effects. Analysis of Dsg3−/− mouse skin also indicated an increase of p53/p21WAF1/CIP1 and cleaved caspase-3 relative to Dsg3+/− controls. Finally, we evaluated whether this pathway was operational in the autoimmune disease PV in which Dsg3 serves as a major antigen involved in blistering pathogenesis. We uncovered increased p53 with diffuse cytoplasmic and/or nuclear staining in the oral mucosa of patients, including cells surrounding blisters and the pre-lesional regions. This finding was verified by in vitro studies where treatment of keratinocytes with PV sera, as well as a characterized pathogenic antibody specifically targeting Dsg3, evoked pronounced p53 expression and activity accompanied by disruption of cell–cell adhesion. Collectively, our findings suggThe study was supported by the Barts and The London School of Medicine and Dentistry and Guizhou Medical University, China. The animal work was supported by Deutsche Forschungsgemeinschaft (TR-SFB 156). Jutamas Uttagomol was supported by a scholarship from Naresuan University, Thailand

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types