Dual functions of DP1 promote biphasic Wnt-on and Wnt-off states during anteroposterior neural patterning.

DP1, a dimerization partner protein of the transcription factor E2F, is known to inhibit Wnt/β-catenin signalling along with E2F, although the function of DP1 itself was not well characterized. Here, we present a novel dual regulatory mechanism of Wnt/β-catenin signalling by DP1 independent from E2F. DP1 negatively regulates Wnt/β-catenin signalling by inhibiting Dvl-Axin interaction and by enhancing poly-ubiquitination of β-catenin. In contrast, DP1 positively modulates the signalling upon Wnt stimulation, via increasing cytosolic β-catenin and antagonizing the kinase activity of NLK. In Xenopus embryos, DP1 exerts both positive and negative roles in Wnt/β-catenin signalling during anteroposterior neural patterning. From subcellular localization analyses, we suggest that the dual roles of DP1 in Wnt/β-catenin signalling are endowed by differential nucleocytoplasmic localizations. We propose that these dual functions of DP1 can promote and stabilize biphasic Wnt-on and Wnt-off states in response to a gradual gradient of Wnt/β-catenin signalling to determine differential cell fates.

Multi-faceted functions of secretory IgA at mucosal surfaces.

Secretory IgA (SIgA) plays an important role in the protection and homeostatic regulation of intestinal, respiratory, and urogenital mucosal epithelia separating the outside environment from the inside of the body. This primary function of SIgA is referred to as immune exclusion, a process that limits the access of numerous microorganisms and mucosal antigens to these thin and vulnerable mucosal barriers. SIgA has been shown to be involved in avoiding opportunistic pathogens to enter and disseminate in the systemic compartment, as well as tightly controlling the necessary symbiotic relationship existing between commensals and the host. Clearance by peristalsis appears thus as one of the numerous mechanisms whereby SIgA fulfills its function at mucosal surfaces. Sampling of antigen-SIgA complexes by microfold (M) cells, intimate contact occurring with Peyer's patch dendritic cells (DC), down-regulation of inflammatory processes, modulation of epithelial, and DC responsiveness are some of the recently identified processes to which the contribution of SIgA has been underscored. This review aims at presenting, with emphasis at the biochemical level, how the molecular complexity of SIgA can serve these multiple and non-redundant modes of action.

Current approaches and challenges for the metabolite profiling of complex natural extracts.

Metabolite profiling is critical in many aspects of the life sciences, particularly natural product research. Obtaining precise information on the chemical composition of complex natural extracts (metabolomes) that are primarily obtained from plants or microorganisms is a challenging task that requires sophisticated, advanced analytical methods. In this respect, significant advances in hyphenated chromatographic techniques (LC-MS, GC-MS and LC-NMR in particular), as well as data mining and processing methods, have occurred over the last decade. Together, these tools, in combination with bioassay profiling methods, serve an important role in metabolomics for the purposes of both peak annotation and dereplication in natural product research. In this review, a survey of the techniques that are used for generic and comprehensive profiling of secondary metabolites in natural extracts is provided. The various approaches (chromatographic methods: LC-MS, GC-MS, and LC-NMR and direct spectroscopic methods: NMR and DIMS) are discussed with respect to their resolution and sensitivity for extract profiling. In addition the structural information that can be generated through these techniques or in combination, is compared in relation to the identification of metabolites in complex mixtures. Analytical strategies with applications to natural extracts and novel methods that have strong potential, regardless of how often they are used, are discussed with respect to their potential applications and future trends.

The functions of sleep.

Here we report on The Satellite Symposium on Sleep Function that was held in Lausanne during 6(th) FENS forum and brought together neuroscientists from basic and clinical sleep research. We illustrate the principal questions that arose during this interdisciplinary gathering and introduce the contents of nine review articles on aspects of sleep that are contained in this Special Issue of the European Journal of Neuroscience.

Functions of peroxisome proliferator-activated receptors (PPAR) in skin homeostasis.

The peroxisome proliferator-activated receptors (PPAR) are ligand-activated transcription factors that belong to the nuclear hormone receptor family. Three isotypes (PPAR alpha, PPAR beta or delta, and PPAR gamma) with distinct tissue distributions and cellular functions have been found in vertebrates. All three PPAR isotypes are expressed in rodent and human skin. They were initially investigated for a possible function in the establishment of the permeability barrier in skin because of their known function in lipid metabolism in other cell types. In vitro studies using specific PPAR agonists and in vivo gene disruption approaches in mice indeed suggest an important contribution of PPAR alpha in the formation of the epidermal barrier and in sebocyte differentiation. The PPAR gamma isotype plays a role in stimulating sebocyte development and lipogenesis, but does not appear to contribute to epidermal tissue differentiation. The third isotype, PPAR beta, regulates the late stages of sebaceous cell differentiation, and is the most effective isotype in stimulating lipid production in these cells, both in rodents and in humans. In addition, PPAR beta activation has pro-differentiating effects in keratinocytes under normal and inflammatory conditions. Finally, preliminary studies also point to a potential role of PPAR in hair follicle growth and in melanocyte differentiation. By their diverse biological effects on cell proliferation and differentiation in the skin, PPAR agonists or antagonists may offer interesting opportunities for the treatment of various skin disorders characterized by inflammation, cell hyperproliferation, and aberrant differentiation.

Unique and conserved functions of B cell-activating factor of the TNF family (BAFF) in the chicken.

The chicken represents the best-characterized animal model for B cell development in the so-called gut-associated lymphoid tissue (GALT) and the molecular processes leading to B cell receptor diversification in this species are well investigated. However, the mechanisms regulating B cell development and homeostasis in GALT species are largely unknown. Here we investigate the role played by the avian homologue of B cell-activating factor of the tumor necrosis factor family (BAFF). Flow cytometric analysis showed that the receptor for chicken B cell-activating factor of the tumor necrosis factor family (chBAFF) is expressed by mature and immature B cells. Unlike murine and human BAFF, chBAFF is primarily produced by B cells both in peripheral lymphoid organs and in the bursa of Fabricius, the chicken's unique primary lymphoid organ. In vitro and in vivo studies revealed that chBAFF is required for mature B cell survival. In addition, in vivo neutralization with a decoy receptor led to a reduction of the size and number of B cell follicles in the bursa, demonstrating that, in contrast to humans and mice, in chickens BAFF is also required for the development of immature B cells. Collectively, we show that chBAFF has phylogenetically conserved functions in mature B cell homeostasis but displays unique and thus far unknown properties in the regulation of B cell development in birds.

The role of incidental variables of time in mood assessment

Determining what influences mood is important for theories of emotion and research onsubjective well-being. We consider three sets of factors: activities in which people areengaged; individual differences; and incidental variables that capture when mood ismeasured, e.g., time-of-day. These three factors were investigated simultaneously in a studyinvolving 168 part-time students who each responded 30 times in an experience samplingstudy conducted over 10 working days. Respondents assessed mood on a simple bipolarscale from 1 (very negative) to 10 (very positive). Activities had significant effects but,with the possible exception of variability in the expression of mood, no systematicindividual differences were detected. Diurnal effects, similar to those already reported inthe literature, were found as was an overall Friday effect. However, these effects weresmall. Lastly, the weather had little or no influence. We conclude that simple measures ofoverall mood are not greatly affected by incidental variables.

Method to assess and optimise dependability of complex macro-systems: application to a railway signalling system

Achieving a high degree of dependability in complex macro-systems is challenging. Because of the large number of components and numerous independent teams involved, an overview of the global system performance is usually lacking to support both design and operation adequately. A functional failure mode, effects and criticality analysis (FMECA) approach is proposed to address the dependability optimisation of large and complex systems. The basic inductive model FMECA has been enriched to include considerations such as operational procedures, alarm systems. environmental and human factors, as well as operation in degraded mode. Its implementation on a commercial software tool allows an active linking between the functional layers of the system and facilitates data processing and retrieval, which enables to contribute actively to the system optimisation. The proposed methodology has been applied to optimise dependability in a railway signalling system. Signalling systems are typical example of large complex systems made of multiple hierarchical layers. The proposed approach appears appropriate to assess the global risk- and availability-level of the system as well as to identify its vulnerabilities. This enriched-FMECA approach enables to overcome some of the limitations and pitfalls previously reported with classical FMECA approaches.

Theory for correlation functions of processes driven by external colored noise

We study steady-state correlation functions of nonlinear stochastic processes driven by external colored noise. We present a methodology that provides explicit expressions of correlation functions approximating simultaneously short- and long-time regimes. The non-Markov nature is reduced to an effective Markovian formulation, and the nonlinearities are treated systematically by means of double expansions in high and low frequencies. We also derive some exact expressions for the coefficients of these expansions for arbitrary noise by means of a generalization of projection-operator techniques.

Intensity correlation functions of dye lasers: Comparison of colored-gain-noise and colored-loss-noise models

The intensity correlation functions C(t) for the colored-gain-noise model of dye lasers are analyzed and compared with those for the loss-noise model. For correlation times ¿ larger than the deterministic relaxation time td, we show with the use of the adiabatic approximation that C(t) values coincide for both models. For small correlation times we use a method that provides explicit expressions of non-Markovian correlation functions, approximating simultaneously short- and long-time behaviors. Comparison with numerical simulations shows excellent results simultaneously for short- and long-time regimes. It is found that, when the correlation time of the noise increases, differences between the gain- and loss-noise models tend to disappear. The decay of C(t) for both models can be described by a time scale that approaches the deterministic relaxation time. However, in contrast with the loss-noise model, a secondary time scale remains for large times for the gain-noise model, which could allow one to distinguish between both models.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.