Topological and semantic Web based method for analyzing TGF-β signaling pathways

International audienceTargeting the deleterious effects of Transforming Growth Factor TGF-β without affecting its physiological role is the common goal of therapeutic strategies aiming at curing fibrosis, the final outcome of all chronic liver disease. The pleiotropic effects of TGF-β are linked to the complex nature of its activation and signaling net- works which understanding requires modeling approaches. Our group recently developed a model of TGF-beta signal propagation based on guarded transitions (ref, Andrieux et al, 2014). In this initial work, we explored the combinatorial complexity of cell signaling, developing a discrete formalism based on guarded transitions. We imported the whole database Pathway Interaction Database into a single unified model of signal transduction. We detected 16,000 chains of reactions linking TGF-β to at least one of 159 target genes in the nucleus. The size and complexity of this model place it beyond current understanding. Its analysis requires automated tools for identifying general patterns.Currently, we focus on designing one reasoning method based on Semantic Web technologies for the analysis of signaling pathways. Our method aims at leveraging external domain knowledge represented in biomedical ontologies and linked databases to rank these candidates. We consider a signaling pathway as a set of proteins involved in the respons of a cell to an external stimulus and influencing at least one gene. The underlying reasoning methods are based on graph topological analysis, formal concepts analysis (FCA) and semantic similarity and particularity measures. First, we determine the formal concepts, maximal bi-cliques, between proteins sets and genes. Then, to determine the biological relevance of theses gene clusters, we calculate a similarity score for each cluster based on Wang semantic similarity. Using such approaches, we identify groups of genes sharing signaling networks.Cibler les effets délétères du Transforming Growth Factor, TGF-β, sans affecter son rôle physiologique est l’objectif commun des stratégies thérapeutiques visant à guérir la fibrose, la conséquence finale de toutes les maladies chroniques du foie. Les effets pléiotropiques du TGF-β sont liés à la nature complexe de son activation et du réseaux de signalisation qu’il induit, et dont la compréhension nécessite des approches de modélisation. Notre équipe a développé un modèle de la propagation du signal induit par le TGF-β base ́ sur les transitions gardées. Le développement d’un formalisme discret base ́ sur les transitions gardées permet d’étudier la complexité combinatoire de la signalisation cellulaire. Nous avons formalise ́ l’intégralité de la base de données Pathway Interaction Database en un unique modèle de la propagation du signal. Nous avons détecté 16 000 chaines de réactions reliant le TGF-β à au moins l’un des 159 gènes cibles d’intérêt Pour identifier des propriétés au sein de ces résultats il est nécessaire d’utiliser des outils automatisés.Nous développons actuellement une méthode basée sur le Web sémantique pour l’analyse des voies de signalisation. Cette méthode vise à tirer parti des connaissances de domaine externe représentées dans les ontologies biomédicales et des bases de données pour classer ces candidats. Nous considérons qu’une voie de signalisation est un ensemble des protéines impliquées dans la réaction d’une cellule à un stimulus externe et qui influence au moins un gène. Les méthodes de raisonnement sous-jacentes sont basées sur l’analyse topologique, l’analyse formelle de concepts et les mesures de similarité et de particularité sémantique. Tout d’abord, nous déterminons les concepts formels, c’est-à-dire les bi-cliques maximales, entre les ensembles de protéines et les gènes. Puis, afin de déterminer la pertinence biologique de ces groupes de gènes, nous calculons un score de similarité pour chacun des groupes, base ́ sur la mesure de Wang. La finalité est d’identifier des groupes de gènes similaires influencés par un même ensemble de voies de signalisation

Increasing 3D Matrix Rigidity Strengthens Proliferation and Spheroid Development of Human Liver Cells in a Constant Growth Factor Environment

International audienceMechanical forces influence the growth and shape of virtually all tissues and organs. Recent studies show that increased cell contractibility, growth and differentiation might be normalized by modulating cell tensions. Particularly, the role of these tensions applied by the extracellular matrix during liver fibrosis could influence the hepatocarcinogenesis process. The objective of this study is to determine if 3D stiffness could influence growth and phenotype of normal and transformed hepatocytes and to integrate extracellular matrix (ECM) stiffness to tensional homeostasis. We have developed an appropriate 3D culture model: hepatic cells within three-dimensional collagen matrices with varying rigidity. Our results demonstrate that the rigidity influenced the cell phenotype and induced spheroid clusters development whereas in soft matrices, Huh7 transformed cells were less proliferative, well-spread and flattened. We confirmed that ERK1 played a predominant role over ERK2 in cisplatin-induced death, whereas ERK2 mainly controlled proliferation. As compared to 2D culture, 3D cultures are associated with epithelial markers expression. Interestingly, proliferation of normal hepatocytes was also induced in rigid gels. Furthermore, biotransformation activities are increased in 3D gels, where CYP1A2 enzyme can be highly induced/activated in primary culture of human hepatocytes embedded in the matrix. In conclusion, we demonstrated that increasing 3D rigidity could promote proliferation and spheroid developments of liver cells demonstrating that 3D collagen gels are an attractive tool for studying rigidity-dependent homeostasis of the liver cells embedded in the matrix and should be privileged for both chronic toxicological and pharmacological drug screening

The rule-based model approach. A Kappa model for hepatic stellate cells activation by TGFB1

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Traitement des signalements relatifs à l’intégrité scientifique

La liste des questions auxquelles ce manuel a cherché à répondre est très longue (près de 200 questions, dont plus d’une centaine dans les 10 fiches pratiques, et une cinquantaine d’ordre juridique) et donnera, aux acteurs de l’intégrité scientifique ainsi qu’aux responsables des opérateurs de recherche, une idée de la complexité et de la difficulté de cette mission centrale des RIS : celle consistant à instruire les dossiers de manquement (potentiel) à l’intégrité scientifique

Dynamic Regulation of Tgf-B Signaling by Tif1γ: A Computational Approach

TIF1γ (Transcriptional Intermediary Factor 1 γ) has been implicated in Smad-dependent signaling by Transforming Growth Factor beta (TGF-β). Paradoxically, TIF1γ functions both as a transcriptional repressor or as an alternative transcription factor that promotes TGF-β signaling. Using ordinary differential-equation models, we have investigated the effect of TIF1γ on the dynamics of TGF-β signaling. An integrative model that includes the formation of transient TIF1γ-Smad2-Smad4 ternary complexes is the only one that can account for TGF-β signaling compatible with the different observations reported for TIF1γ. In addition, our model predicts that varying TIF1γ/Smad4 ratios play a critical role in the modulation of the transcriptional signal induced by TGF-β, especially for short stimulation times that mediate higher threshold responses. Chromatin immunoprecipitation analyses and quantification of the expression of TGF-β target genes as a function TIF1γ/Smad4 ratios fully validate this hypothesis. Our integrative model, which successfully unifies the seemingly opposite roles of TIF1γ, also reveals how changing TIF1γ/Smad4 ratios affect the cellular response to stimulation by TGF-β, accounting for a highly graded determination of cell fate

Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods.</p> <p>Results</p> <p>Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values.</p> <p>Conclusions</p> <p>Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.</p

6th International Workshop on Static Analysis and Systems Biology (SASB 2015)

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