Accueil > Publications > Recherche par années > Années 2000 > 2009


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A functional and regulatory network associated with PIP expression in human breast cancer

The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes.

Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP-] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters.

Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator

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A Restrained Molecular Dynamics Empirical Approach for Generating a Small Set of Structures Representative of the Internal Flexibility of a Receptor.

Multiple protein structure methods have been proposed for incorporating protein flexibility in molecular docking. One approach for docking ligands onto a rigid receptor is to use an ensemble of multiple rigid structures determined experimentally by X-ray or NMR spectroscopy or generated by numerical simulations. In this work we present all empirical method for generating a wide range of conformational states of a wobbling receptor using restrained Molecular Dynamics simulations (MD) and we propose a partitioning protocol for selecting a few representative conformations of the binding site from restrained MID sampling.

Defining a large number of protein structures is computationally expensive when the MD simulations use an explicit solvent representation. For computational efficiency, solvent effect is therefore represented by an ensemble of restraints applied on a subset of specific atoms, using a distance-dependent permittivity function. The parameters used for the restraints and the permittivity are described. Several 100 ns restrained MD simulations are performed using different sets of parameters. In order to optimize the parameters, the results are compared to a 30 ns MD simulation in explicit solvent. Conformational sampling is speeded up by a factor of around 10-20 when performing restrained MD simulations. A partitioning k-means algorithm is applied to select representative structures of the receptor binding site. The methodology was evaluated on the ligand binding domain of the flexible Peroxysome Proliferator-Activated Receptor-gamma (PPAR gamma).

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A stepwise 2 ’-hydroxyl activation mechanism for the bacterial transcription termination factor Rho helicase.

The bacterial Rho factor is a ring-shaped ATP-dependent helicase that tracks along RNA transcripts and disrupts RNA-DNA duplexes and transcription complexes in its path. Using combinatorial nucleotide analog interference mapping (NAIM), we explore the topology and dynamics of functional Rho-RNA complexes and reveal the RNA-dependent stepping mechanism of Rho helicase. Periodic Gaussian distributions of NAIM signals show that Rho forms uneven productive interactions with the track nucleotides and disrupts RNA-DNA duplexes in a succession of large ( approximately 7-nucleotide-long) discrete steps triggered by 2’-hydroxyl activation events. This periodic 2’-OH-dependent activation does not depend on the RNA-DNA pairing energy but is finely tuned by sequence-dependent interactions with the RNA track. These features explain the strict RNA specificity and contextual efficiency of the enzyme and provide a new paradigm for conditional tracking by a helicase ring.

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