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Klein J.,University Paul Sabatier | Klein J.,Institute Of Medecine Moleculaire Of Rangueil | Miravete M.,University Paul Sabatier | Miravete M.,Institute Of Medecine Moleculaire Of Rangueil | And 6 more authors.
Medecine/Sciences | Year: 2011

The incidence of chronic kidney disease leading to end-stage renal disease has significantly increased and may reach epidemic proportions over the next decade. Regardless of the initial insult, the progression of most forms of renal disease results in tubulo-interstitial fibrosis. This has been closely correlated to the future appearance of renal failure and has therefore been associated with poor long-term prognosis. New molecules and agents to limit the development of tubulo-interstitial fibrosis and slow down the progression towards end-stage renal disease are needed. In the past twenty years, many efforts have been made to understand the mechanisms of tubulo-intersititial fibrosis with the final goal to develop new therapeutic strategies. In this context, this review will focus on the mechanisms and factors involved in the development and the progression of renal fibrosis and will discuss the new promising therapeutic strategies in animal and humans.

Sanchez A.,University of Barcelona | Fernandez-Real J.,Institute Dinvestigacio Biomedica Of Girona | Vegas E.,University of Barcelona | Carmona F.,University of Barcelona | And 7 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

As the developments in high throughput technologies have become more common and accessible it is becoming usual to take several distinct simultaneous approaches to study the same problem. In practice, this means that data of different types (expression, proteins, metabolites...) may be available for the same study, highlighting the need for methods and tools to analyze them in a combined way. In recent years there have been developed many methods that allow for the integrated analysis of different types of data. Corresponding to a certain tradition in bioinformatics many methodologies are rooted in machine learning such as bayesian networks, support vector machines or graph-based methods. In contrast with the high number of applications from these fields, another that seems to have contributed less to "omic" data integration is multivariate statistics, which has however a long tradition in being used to combine and visualize multidimensional data. In this work, we discuss the application of multivariate statistical approaches to integrate bio-molecular information by using multiple factorial analysis. The techniques are applied to a real unpublished data set consisting of three different data types: clinical variables, expression microarrays and DNA Gel Electrophoretic bands. We show how these statistical techniques can be used to perform reduction dimension and then visualize data of one type useful to explain those from other types. Whereas this is more or less straightforward when we deal with two types of data it turns to be more complicated when the goal is to visualize simultaneously more than two types. Comparison between the approaches shows that the information they provide is complementary suggesting their combined use yields more information than simply using one of them. © 2012 Springer-Verlag.

PubMed | Institute Of Medecine Moleculaire Of Rangueil
Type: Journal Article | Journal: Journal of leukocyte biology | Year: 2010

TNF- is a pleiotropic cytokine involved in the regulation of various biological effects, including cell survival and proliferation, cell differentiation, and cell death. Moreover, TNF- triggers proinflammatory responses, essentially through its ability to promote the expression of various proinflammatory genes. Most of the biological effects initiated by TNF- rely on its ability to bind to and activate TNF-R1. As a consequence, molecular complexes are being formed, resulting from the recruitment of multiple adaptor proteins to the intracellular TNF-R1 DD. The adaptor protein FAN constitutively binds to a proximal membrane domain of TNF-R1 called NSD. Herein, the role of FAN in TNF--induced cell signaling and biological responses is discussed.

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