Negative Regulation of Notch Signaling by Xylose

The Notch signaling pathway controls a large number of processes during animal development and adult homeostasis. One of the conserved post-translational modifications of the Notch receptors is the addition of an O-linked glucose to epidermal growth factor-like (EGF) repeats with a C-X-S-X-(P/A)-C motif by Protein O-glucosyltransferase 1 (POGLUT1; Rumi in Drosophila). Genetic experiments in flies and mice, and in vivo structure-function analysis in flies indicate that O-glucose residues promote Notch signaling. The O-glucose residues on mammalian Notch1 and Notch2 proteins are efficiently extended by the addition of one or two xylose residues through the function of specific mammalian xylosyltransferases. However, the contribution of xylosylation to Notch signaling is not known. Here, we identify the Drosophila enzyme Shams responsible for the addition of xylose to O-glucose on EGF repeats. Surprisingly, loss- and gain-of-function experiments strongly suggest that xylose negatively regulates Notch signaling, opposite to the role played by glucose residues. Mass spectrometric analysis of Drosophila Notch indicates that addition of xylose to O-glucosylated Notch EGF repeats is limited to EGF14–20. A Notch transgene with mutations in the O-glucosylation sites of Notch EGF16–20 recapitulates the shams loss-of-function phenotypes, and suppresses the phenotypes caused by the overexpression of human xylosyltransferases. Antibody staining in animals with decreased Notch xylosylation indicates that xylose residues on EGF16–20 negatively regulate the surface expression of the Notch receptor. Our studies uncover a specific role for xylose in the regulation of the Drosophila Notch signaling, and suggest a previously unrecognized regulatory role for EGF16–20 of Notch.     

A preliminary assessment of the habitat selection of two Palaearctic migrant passerine species in West Africa

We investigated the habitat selected by two Palaearctic migrants (Pied Flycatcher, Ficedula hypoleuca, Willow Warbler, Phylloscopus trochilus) in a patchy landscape in Ivory Coast and compared it with the habitat selection of Afrotropical species in the same foraging guilds. Transect counts were used to test the hypothesis that migrants use more open and more seasonal habitats and have a broader use of habitats compared with resident species. Habitats compared were, in order of decreasing tree density, gallery forest, an isolated forest and bush/tree savanna. The isolated forest had the most pronounced seasonal changes (deciduous trees) and was the one with the most diverse vegetation structure. The habitat where both migrants were most frequent was the isolated forest, and thus occurred in the habitat with the most pronounced seasonal change. Diversity of habitats selected was highest in migrants but in the Pied Flycatcher this was possibly an artefact due to subdominant individuals being excluded from the preferred habitat by territorial birds. Potential competition for habitat with Afrotropical species was found to be low.     

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

A combinatorial approach to the structure elucidation of a pyoverdine siderophore produced by a Pseudomonas putida isolate and the use of pyoverdine as a taxonomic marker for typing P. putida subspecies

The structure of a pyoverdine produced by Pseudomonas putida, W15Oct28, was elucidated by combining mass spectrometric methods and bioinformatics by the analysis of non-ribosomal peptide synthetase genes present in the newly sequenced genome. The only form of pyoverdine produced by P. putida W15Oct28 is characterized to contain α-ketoglutaric acid as acyl side chain, a dihydropyoverdine chromophore, and a 12 amino acid peptide chain. The peptide chain is unique among all pyoverdines produced by Pseudomonas subspecies strains. It was characterized as –l-Asp-l-Ala-d-AOHOrn-l-Thr-Gly-c[l-Thr(O-)-l-Hse-d-Hya-l-Ser-l-Orn-l-Hse-l-Ser-O-]. The chemical formula and the detected and calculated molecular weight of this pyoverdine are: C₆₅H₉₃N₁₇O₃₂, detected mass 1624.6404 Da, calculated mass 1624.6245. Additionally, pyoverdine structures from both literature reports and bioinformatics prediction of the genome sequenced P. putida strains are summarized allowing us to propose a scheme based on pyoverdines structures as tool for the phylogeny of P. putida. This study shows the strength of the combination of in silico analysis together with analytical data and literature mining in determining the structure of secondary metabolites such as peptidic siderophores.     

Human proximity and habitat fragmentation are key drivers of the rangewide bonobo distribution

Habitat loss and hunting threaten bonobos (Pan paniscus), Endangered (IUCN) great apes endemic to lowland rainforests of the Democratic Republic of Congo. Conservation planning requires a current, data-driven, rangewide map of probable bonobo distribution and an understanding of key attributes of areas used by bonobos. We present a rangewide suitability model for bonobos based on a maximum entropy algorithm in which data associated with locations of bonobo nests helped predict suitable conditions across the species’ entire range. We systematically evaluated available biotic and abiotic factors, including a bonobo-specific forest fragmentation layer (forest edge density), and produced a final model revealing the importance of simple threat-based factors in a data poor environment. We confronted the issue of survey bias in presence-only models and devised a novel evaluation approach applicable to other taxa by comparing models built with data from geographically distinct sub-regions that had higher survey effort. The model’s classification accuracy was high (AUC = 0.82). Distance from agriculture and forest edge density best predicted bonobo occurrence with bonobo nests more likely to occur farther from agriculture and in areas of lower edge density. These results suggest that bonobos either avoid areas of higher human activity, fragmented forests, or both, and that humans reduce the effective habitat of bonobos. The model results contribute to an increased understanding of threats to bonobo populations, as well as help identify priority areas for future surveys and determine core bonobo protection areas.     

Natalizumab Treatment Reduces Fatigue in Multiple Sclerosis. Results from the TYNERGY Trial; A Study in the Real Life Setting

Fatigue is a significant symptom in multiple sclerosis (MS) patients. First-generation disease modifying therapies (DMTs) are at best moderately effective to improve fatigue. Observations from small cohorts have indicated that natalizumab, an antibody targeting VLA-4, may reduce MS-related fatigue. The TYNERGY study aimed to further evaluate the effects of natalizumab treatment on MS-related fatigue. In this one-armed clinical trial including 195 MS patients, natalizumab was prescribed in a real-life setting, and a validated questionnaire, the Fatigue Scale for Motor and Cognitive functions (FSMC), was used both before and after 12 months of treatment to evaluate a possible change in the fatigue experienced by the patients. In the treated cohort all measured variables, that is, fatigue score, quality of life, sleepiness, depression, cognition, and disability progression were improved from baseline (all p values<0.0001). Walking speed as measured by the six-minute walk-test also increased at month 12 (p = 0.0016). All patients were aware of the nature of the treatment agent, and of the study outcomes.

Conclusion

Natalizumab, as used in a real-life setting, might improve MS-related fatigue based on the results from this one-armed un-controlled stud. Also other parameters related to patients'' quality of life seemed to improve with natalizumab treatment.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00884481","term_id":"NCT00884481"}}NCT00884481     

Molecular mechanisms regulating formation,trafficking and processing of annular gap junctions

Internalization of gap junction plaques results in the formation of annular gap junction vesicles. The factors that regulate the coordinated internalization of the gap junction plaques to form annular gap junction vesicles, and the subsequent events involved in annular gap junction processing have only relatively recently been investigated in detail. However it is becoming clear that while annular gap junction vesicles have been demonstrated to be degraded by autophagosomal and endo-lysosomal pathways, they undergo a number of additional processing events. Here, we characterize the morphology of the annular gap junction vesicle and review the current knowledge of the processes involved in their formation, fission, fusion, and degradation. In addition, we address the possibility for connexin protein recycling back to the plasma membrane to contribute to gap junction formation and intercellular communication. Information on gap junction plaque removal from the plasma membrane and the subsequent processing of annular gap junction vesicles is critical to our understanding of cell-cell communication as it relates to events regulating development, cell homeostasis, unstable proliferation of cancer cells, wound healing, changes in the ischemic heart, and many other physiological and pathological cellular phenomena.