Degeneration and regeneration of peripheral nerves: role of thrombin and its receptor PAR-1

The peripheral nervous system has a striking regeneration potential and after damage extensive changes in the differentiation state both of the injured neurons and of the Schwann cells are observed. Schwann cells, in particular, undergo a large scale change in gene expression becoming able to support axonal regeneration. Nerve injury is generally associated to inflammation and activation of the coagulation cascade. Thrombin acts as a polyfunctional signalling molecule exerting its physiological function through soluble target proteins and G-protein-coupled receptors, the protease-activated receptors (PARs) [1]. Recently, we have demonstrated that the activation of the main thrombin receptor, PAR-1, in Schwann cells favours their regenerative potential determining the release of factors which promote axonal regrowth [2]. The pro-regenerative potential of thrombin seems to be exerted in a narrow range of concentrations (pM-nM range). In fact, our preliminary data indicate that high levels of thrombin in the micromolar range slow down Schwann cell proliferation and induce cell death. On the contrary, PAR-1 activating peptides mimic the pro-survival but not the pro-apoptotic effects of thrombin. Controlling thrombin concentration may preserve neuronal health during nerve injury and represent a novel target for pharmacologic therapies

PAR1 activation induces the release by Schwann cells of factors promoting cell survival and neuritogenesis

Protease-activated receptor 1 (PAR1) is a member of a family of four G-protein-coupled receptors which are activated by proteolytic cleavage of their N-terminal extracellular domain. The expression and the role of PAR1 in peripheral nervous system (PNS) is still poorly investigated, although high PAR1 mRNA expression was found in the dorsal root ganglia and in the non-compacted Schwann cell myelin microvilli at the nodes of Ranvier. Schwann cells (SCs) are the principal population of glial cells of the PNS which myelinate axons and play a key role in axonal regeneration and remyelination. Aim of the present study was to determine if the activation of PAR1 affects the neurotrophic properties of SCs. By double immunofluorescence we observed a specific staining for PAR1 in S100ȕ-positive cells of rat sciatic nerve and sciatic teased fibers. Moreover, PAR1 was highly expressed in SC cultures obtained from both neonatal and adult rat sciatic nerves. When PAR1 specific agonists were added to these cultures an increased proliferation rate was observed. Moreover, the conditioned medium obtained from primary SCs treated with PAR1 agonists increased cell survival and neurite outgrowth on PC12 cells respect to controls. By proteomics, western blot and RT-PCR analyses we identified five proteins which are released by SCs following PAR1 stimulation: Macrophage migration inhibitory factor (Mif), Aldose reductase (Akr1b1), Matrix metalloproteinase-2 (Mmp2), Syndecan-4 (Sdc) and Decorin (Dcn). Conversely, a significant decrease in the level of three proteins was observed: Complement C1r subcomponent (C1r) and Complement component 1 Q subcomponent-bindingprotein (C1qbp). When PAR1 expression was silenced by siRNA the observed pro-survival and neurotrophic properties of SCs appear to be reduced respect to controls. References PAR1 activation affects the neurotrophic properties of Schwann cells. Pompili E1, Fabrizi C2, Somma F2, Correani V3, Maras B3, Schininà ME3, Ciraci V2, Artico M4, Fornai F5, Fumagalli L2. 2017 Jan 4;79:23-33. doi: 10.1016/j.mcn.2017.01.001.Schwann cells (SCs) regulate a wide variety of axonal functions in the peripheral nervous system, providing a supportive growth environment following nerve injury (1). Here we show that rat SCs express the protease-activated receptor-1 (PAR1) both in vivo and in vitro. PAR1 is a G-protein coupled receptor eliciting cellular responses to thrombin and other proteases (2). To investigate if PAR1 activation affects the neurotrophic properties of SCs, this receptor was activated by a specific agonist peptide (TFLLR) and the conditioned medium was transferred to PC12 pheocromocytoma cells for assessing cell survival and neurite outgrowth. Culture medium from SCs treated with 10 µM TFLLR reduced significantly the release of LDH and increased the viability of PC12 cells with respect to the medium of the untreated SCs. Furthermore, conditioned medium from TFLLR-treated SCs increased neurite outgrowth on PC12 cells respect to control medium from untreated cells. To identify putative neurotrophic candidates we performed proteomic analysis on SC secretoma and real time PCR experiments after PAR1 activation. Stimulation of SCs with TFLLR increased specifically the release of a subset of five proteins: Macrophage migration inhibitory factor (Mif), Aldose reductase (Akr1b1), Matrix metalloproteinase-2 (Mmp2), Syndecan-4 (Sdc) and Decorin (Dcn). At the same time there was a significant decrease in the level of three proteins: Complement C1r subcomponent (C1r), Complement component 1 Q subcomponent-binding protein (C1qbp) and Angiogenic factor with G patch and FHA domains 1 (Aggf1). These data indicate that PAR1 stimulation does induce the release by SCs of factors promoting cell survival and neuritogenesis. Among these proteins, Mif, Sdc, Dcn and Mmp2 are of particular interest

Output-based incentive regulation: benchmarkingwith quality of supply in electricity distribution

Incentive regulation is moving towards new schemes where standard efficiency mechanisms are combined with output-based incentives (related to quality of supply, sustainability and innovation). Assessing performance of regulated utilities requires models capable to account for these different regulatory objectives. Benchmarking analysis has been in use for a long time; however, whether these models should ncorporate even quality as an additional regulated output is still a matter of debate. In this paper we study how benchmarking DEA models can be designed to correctly accommodate all regulated variables, including continuity of supply. To this end, we discuss different models to measure technical efficiency, using a comprehensive and balanced panel for 115 electricity distribution Zones, that belong to the largest Italian utility. Our results show that, for our data set, quality significantly affects efficiency scores . We thus claim that the effect of additional regulated outputs should always be tested in benchmarking models

Секвенційні числення композиційно-номінативних модальних логік функціонально-екваційного рівня

Досліджуються композиційно-номінативні модальні та темпоральні логіки функціонально-екваційного рівня. На основі властивостей відношення логічного наслідку для множин формул збудовані числення секвенційного типу для загальних та темпоральних композиційно-номінативних модальних логік такого рівня. Для побудованих числень доведені теореми коректності та повноти

the stability of plagioclase in the upper mantle subsolidus experiments on fertile and depleted lherzolite

Plagioclase peridotites are important markers of processes that characterize the petrological and tectonic evolution of the lithospheric mantle in extensional tectonic settings. Studies on equilibrated plagioclase peridotites have documented continuous chemical changes in mantle minerals in response to plagioclase crystallization, potentially tracing the re-equilibration of mantle peridotites up to very low pressure.This experimental study provides new constraints on the stability of plagioclase in mantle peridotites as a function of bulk composition, and the compositional and modal changes in minerals occurring within the plagioclase stability field as a function of P^T^bulk composition. Subsolidus experiments have been performed at pressures ranging from 0·25 to 1·0 GPa, and temperatures ranging from 900 to 12008C on fertile and depleted anhydrous lherzolites modelled in the system TiO2^Cr2O3^Na2O^CaO^FeO^ MgO^Al2O3^SiO2 (Ti,Cr-NCFMAS). In the fertile lherzolite (Na2O/CaO 1⁄4 0·08; XCr 1⁄4 0·07) a plagioclase-bearing assemblage is stable up to 0·7 GPa, 10008C and 0·8 GPa, 11008C, whereas in the depleted lherzolite (Na2O/CaO 1⁄4 0·09; XCr 1⁄4 0·10) the upper limit of plagioclase stability is shifted to lower pressure.The boundary between plagioclase lherzolite and spinel lherzolite has a positive slope in P^T space. In a complex chemical system, the plagioclase-out boundary is multivariant and sensitive to the XCr value [XCr 1⁄4 Cr/(Cr þAl)] of spinel.This latter is controlled by the reaction MgCr2O4 þ CaAl2Si2O8 1⁄4 MgCrAlSiO6 þ CaCrAlSiO6, which is a function of the Cr^Al partitioning between spinel and pyroxenes, and varies with the XCr value and chromite/ anorthite normative ratio of the bulk composition. Within the plagioclase stability field, the Al content of pyroxenes decreases, coupled with an increase in the anorthite content in plagioclase, and Ti and XCr in spinel with decreasing pressure; these chemical variations are combined with systematic changes in modal mineralogy governed by a continuous reaction involving both plagioclase and spinel. As a consequence, the composition of plagioclase varies significantly over a rather narrow pressure range and is similar at the same P^T conditions in the investigated bulk-rocks.This suggests the potential application of plagioclase composition as a geobarometer for plagioclase peridotites

Modelization of Thermal Fluctuations in G Protein-Coupled Receptors

We simulate the electrical properties of a device realized by a G protein coupled receptor (GPCR), embedded in its membrane and in contact with two metallic electrodes through which an external voltage is applied. To this purpose, recently, we have proposed a model based on a coarse graining description, which describes the protein as a network of elementary impedances. The network is built from the knowledge of the positions of the C-alpha atoms of the amino acids, which represent the nodes of the network. Since the elementary impedances are taken depending of the inter-nodes distance, the conformational change of the receptor induced by the capture of the ligand results in a variation of the network impedance. On the other hand, the fluctuations of the atomic positions due to thermal motion imply an impedance noise, whose level is crucial to the purpose of an electrical detection of the ligand capture by the GPCR. Here, in particular, we address this issue by presenting a computational study of the impedance noise due to thermal fluctuations of the atomic positions within a rhodopsin molecule. In our model, the C-alpha atoms are treated as independent, isotropic, harmonic oscillators, with amplitude depending on the temperature and on the position within the protein (alpha-helix or loop). The relative fluctuation of the impedance is then calculated for different temperatures.Comment: 5 pages, 2 figures, Proceeding of the 18-th International Conference on Fluctuations and Noise, 19-23 September 2005, Salamanca, Spain -minor proofreadings

Deep learning in population genetics

KK is supported by a grant from the Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE), GSC 81, within the project GENOMIE QADOP. We acknowledge the support of Imperial College London - TUM Partnership award.Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.Publisher PDFPeer reviewe

Simulation-supported framework for job shop scheduling with genetic algorithm

The Job Shop Scheduling Problem (JSSP) is recognized to be one of the most difficult scheduling problems, being NP-complete. During years, many different solving techniques were developed: some techniques are focused on the development of optimization algorithms, whilst others are based on simulation models. Since the 80s, it was recognized that a combination of the two could be of big advantage, matching advantages from both sides. However, this research stream has not been followed to a great extent. The goal of this study is to propose a novel scheduling tool able to match these two really different techniques in one common framework in order to fill this gap in literature. The base of the framework is composed by a genetic algorithm (GA) and a simulation model is introduced into the evaluation of the fitness function, due to the inability of GAs in taking into account the real performances of a production system. An additional purpose of this research is to improve the collaboration between academic and industrial worlds on the topic, through an application of the novel scheduling framework to an industrial case. The implementation to the industrial case also suggested an improvement of the tool: The introduction of the stochasticity into the proposed scheduling framework in order to consider the variable nature of the production systems

Autophagy in trimethyltin-induced neurodegeneration

Autophagy is a degradative process playing an important role in removing misfolded or aggregated proteins, clearing damaged organelles, such as mitochondria and endoplasmic reticulum, as well as eliminating intracellular pathogens. The autophagic process is important for balancing sources of energy at critical developmental stages and in response to nutrient stress. Recently, autophagy has been involved in the pathophysiology of neurodegenerative diseases although its beneficial (pro-survival) or detrimental (pro-death) role remains controversial. In the present review, we discuss the role of autophagy following intoxication with trimethyltin (TMT), an organotin compound that induces severe hippocampal neurodegeneration associated with astrocyte and microglia activation. TMT is considered a useful tool to study the molecular mechanisms occurring in human neurodegenerative diseases such as Alzheimer’s disease and temporal lobe epilepsy. This is also relevant in the field of environmental safety, since organotin compounds are used as heat stabilizers in polyvinyl chloride polymers, industrial and agricultural biocides, and as industrial chemical catalysts

Setting network tariffs with heterogeneous firms: The case of natural gas distribution

The appropriate treatment of firm heterogeneity plays a crucial role in the application of benchmarking analyses for regulatory purposes. Within the realm of two-step approaches, this paper challenges the widespread adoption of single-variable clustering: heterogeneity has often multiple sources, which calls for more sophisticated clustering methodologies. In fact, reliable cluster-specific rankings provide firms’ management with more realistic objectives as well as freedom to identify the appropriate strategies to improve efficiency. In order to provide regulatory guidance on this issue, we use a unique dataset of detailed accounting data and unbundled network-related costs for a panel of Italian gas distributors and we test two alternative methods: a hybrid clustering procedure (HCP) and a latent class model (LCM). Our results show that HCP and LCM perform better than size segmentation in the identification of classes, thereby leading to more reliable production frontiers, but do not support a conclusive preference for one or the other method. While both methods are sensitive to outliers, LCMs seem to provide deeper insights on the drivers of firm inefficiency. However, they also present stationarity and convergence issues, which might favour the implementation of HCP methods. Furthermore, the degree of discretionary judgement in the modelling decisions (e.g., model specification and choice of the partition) is slightly higher with LCMs than with HCP. In this respect, the HCP, with its lower modelling and analytical complexity, may feature as a more appealing option, facilitating the interactions between regulator and firm managers