Recombinant tumor-associated MUC1 glycoprotein impairs the differentiation and function of dendritic cells

Tumors exploit several strategies to evade immune recognition, including the production of a large number of immunosuppressive factors, which leads to reduced numbers and impaired functions of dendritic cells (DCs) in the vicinity of tumors. We have investigated whether a mucin released by tumor cells could be involved in causing these immunomodulating effects on DCs. We used a recombinant purified form of the MUC1 glycoprotein, an epithelial associated mucin that is overexpressed, aberrantly glycosylated, and shed during cancer transformation. The O-glycosylation profile of the recombinant MUC1 glycoprotein (ST-MUC1) resembled that expressed by epithelial tumors in vivo, consisting of large numbers of sialylated core 1 (sialyl-T, ST) oligosaccharides. When cultured in the presence of ST-MUC1, human monocyte-derived DCs displayed a modified phenotype with decreased expression of costimulatory molecules (CD86, CD40), Ag-presenting molecules (DR and CD1d), and differentiation markers (CD83). In contrast, markers associated with an immature phenotype, CD1a and CD206 (mannose receptor), were increased. This effect was already evident at day 4 of DC culture and was dose dependent. The modified phenotype of DCs corresponded to an altered balance in IL-12/IL-10 cytokine production, with DC expressing an IL-10(high)IL-12(low) phenotype after exposure to ST-MUC1. These DCs were defective in their ability to induce immune responses in both allogeneic and autologous settings, as detected in proliferation and ELISPOT assays. The altered DC differentiation and Ag presentation function induced by the soluble sialylated tumor-associated mucin may represent a mechanism by which epithelial tumors can escape immunosurveillance.     

Physiological measurements from native and transplanted mussel (Mytilus galloprovincialis) in the canals of Venice. Survival in air and condition index

The Venice Lagoon has been the focus of many environmental studies due to its heavy contaminant load derived from a variety of sources, including industrial activity, oil tanker traffic, and waste runoff from the mainland. In recent years, there has been increasing concern about water quality in the urban areas of Venice related to the discharge of untreated sewage directly into canals, adding to the pollutant load already existing in these areas. One way of gauging the impact of these chemicals is monitoring the local fauna. In the search for good indicators of water quality in the Venice urban area, two physiological indices for mussels-survival in air and condition index-have been evaluated. In 2002, a seasonal study was undertaken; mussels (Mytilus galloprovincialis) were collected in three sites located in the canals of the city and a clean reference site. At the same time, two transplantation experiments were performed, deploying farmed mussels for 1,2, 4, and 12 weeks in the historic centre of Venice and also at a reference site. Data from survival in air test and the condition index of native and transplanted mussels are reported. The results suggest an impact of the complex mixture of pollutants on mussel health status. Both native mussels and those transplanted to the urban area showed reduced survivability in air and decreased condition index values, indicating a less healthy status in animals from the canals of the city. Data are discussed in relation to pollutant bioaccumulation.     

Genetic characterization of p27kip1 and stathmin in controlling cell proliferation in vivo

The CDK inhibitor p27^kip1 is a critical regulator of cell cycle progression, but the mechanisms by which p27^kip1 controls cell proliferation in vivo are still not fully elucidated. We recently demonstrated that the microtubule destabilizing protein stathmin is a relevant p27^kip1 binding partner. To get more insights into the in vivo significance of this interaction, we generated p27^kip1 and stathmin double knock-out (DKO) mice. Interestingly, thorough characterization of DKO mice demonstrated that most of the phenotypes of p27^kip1 null mice linked to the hyper-proliferative behavior, such as the increased body and organ weight, the outgrowth of the retina basal layer and the development of pituitary adenomas, were reverted by co-ablation of stathmin. In vivo analyses showed a reduced proliferation rate in DKO compared to p27^kip1 null mice, linked, at molecular level, to decreased kinase activity of CDK4/6, rather than of CDK1 and CDK2. Gene expression profiling of mouse thymuses confirmed the phenotypes observed in vivo, showing that DKO clustered with WT more than with p27 knock-out tissue. Taken together, our results demonstrate that stathmin cooperates with p27^kip1 to control the early phase of G1 to S phase transition and that this function may be of particular relevance in the context of tumor progression.     

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).     

Levodopa-induced plasticity: a double-edged sword in Parkinson's disease?

The long-term replacement therapy with the dopamine (DA) precursor 3,4-dihydroxy-l-phenylalanine (L-DOPA) is a milestone in the treatment of Parkinson''s disease (PD). Although this drug precursor can be metabolized into the active neurotransmitter DA throughout the brain, its therapeutic benefit is due to restoring extracellular DA levels within the dorsal striatum, which lacks endogenous DA as a consequence of the neurodegenerative process induced by the disease. In the early phases of PD, L-DOPA treatment is able to restore both long-term depression (LTD) and long-term potentiation (LTP), two major forms of corticostriatal synaptic plasticity that are altered by dopaminergic denervation. However, unlike physiological DA transmission, this therapeutic approach in the advanced phase of the disease leads to abnormal peaks of DA, non-synaptically released, which are supposed to trigger behavioural sensitization, namely L-DOPA-induced dyskinesia. This condition is characterized by a loss of synaptic depotentiation, an inability to reverse previously induced LTP. In the advanced stages of PD, L-DOPA can also induce non-motor fluctuations with cognitive dysfunction and neuropsychiatric symptoms such as compulsive behaviours and impulse control disorders. Although the mechanisms underlying the role of L-DOPA in both motor and behavioural symptoms are still incompletely understood, recent data from electrophysiological and imaging studies have increased our understanding of the function of the brain areas involved and of the mechanisms implicated in both therapeutic and adverse actions of L-DOPA in PD patients.     

Endothelin-1 drives invadopodia and interaction with mesothelial cells through ILK

1. Download : Download high-res image (174KB)
2. Download : Download full-size image

     

Comparative LCA of concrete with recycled aggregates: a circular economy mindset in Europe

Purpose

Construction and demolition waste (C&DW) is the largest waste stream in the European Union (EU) and all over the world. Proper management of C&DW and recycled materials—including the correct handling of hazardous waste—can have major benefits in terms of sustainability and the quality of life. The Waste Framework Directive 2008/98/EC aims to have 70% of C&DW recycled by 2020. However, except for a few EU countries, only about 50% of C&DW is currently being recycled. In the present research, the environmental impact of concrete with recycled aggregates and with geopolymer mixtures is analysed. The aim of the present research is to propose a comparative LCA of concrete with recycled aggregates in the context of European politics.

Methods

Life cycle assessment (LCA) methodology is applied using Simapro© software. A cradle to grave analysis is carried out. The results are analysed based on the database Ecoinvent 3.3 and Impact 2002+.

Results

Results show that the concrete with 25% recycled aggregates is the best solution from an environmental point of view. Furthermore, geopolymer mixtures could be a valid alternative to reduce the phenomenon of “global warming”; however, the production of sodium silicate and sodium hydroxide has a great environmental impact.

Conclusions

A possible future implementation of the present study is certainly to carry out an overall assessment and to determine the most cost-effective option among the different competing alternatives through the life cycle cost analysis.