Background: The transforming growth factor beta is known to have pleiotropic effects, including differentiation, proliferation and apoptosis. However the underlying mechanisms remain poorly understood. The regulation and effect of TGF-β signaling is complex and highly depends on specific protein context. In liver, we have recently showed that the disintegrin and metalloproteinase ADAM12 interacts with TGF-β receptors and modulates their trafficking among membranes, a crucial point in TGF-β signaling and development of fibrosis. The present study aims to better understand how ADAM12 impacts on TGF-β receptors trafficking and TGF-β signaling.

BackgroundRegulation 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.ResultsHere, 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.ConclusionsUsing unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.

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