Nouvelles technologies de l’information et de la communication et anciens instruments de régulation : l’exemple d’Internet en France

L’étude de la jurisprudence française relative à Internet entre 1996 et 1997 montre que, loin d’être un « espace de non-droit », le réseau des réseaux est assujetti au respect de règles juridiques. Ainsi, l’exemple de l’exercice de la liberté d’expression met en exergue que le droit français peut s’appliquer à Internet comme à tout autre vecteur de communication, même si l’édifice jurisprudentiel paraît encore fragile. Quant aux droits intellectuels, le juge français, s’il doit respecter les textes applicables, doit également tenir compte de l’esprit libéral du réseau.A study of the case law regarding French language use on the Internet during 1996 and 1997 demonstrates that, far from being a non-legal issue, the network requires juridical boundaries. For example, the exercise of freedom of expression highlights the fact that French law can be applied to the Internet as it does to other vehicles of communication, regardless of the continuing ambiguity surrounding legal provision in this area. As to intellectual rights, in the French context, a judge is obliged to respect existing legal texts as well as the liberal spirit of the network

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data

International audienceBackground: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. Results: We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. Conclusion: We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles

Steroid Receptor RNA Activator (SRA) Modification by the Human Pseudouridine Synthase 1 (hPus1p): RNA Binding, Activity, and Atomic Model

The most abundant of the modified nucleosides, and once considered as the "fifth" nucleotide in RNA, is pseudouridine, which results from the action of pseudouridine synthases. Recently, the mammalian pseudouridine synthase 1 (hPus1p) has been reported to modulate class I and class II nuclear receptor responses through its ability to modify the Steroid receptor RNA Activator (SRA). These findings highlight a new level of regulation in nuclear receptor (NR)-mediated transcriptional responses. We have characterised the RNA association and activity of the human Pus1p enzyme with its unusual SRA substrate. We validate that the minimal RNA fragment within SRA, named H7, is necessary for both the association and modification by hPus1p. Furthermore, we have determined the crystal structure of the catalytic domain of hPus1p at 2.0 Å resolution, alone and in a complex with several molecules present during crystallisation. This model shows an extended C-terminal helix specifically found in the eukaryotic protein, which may prevent the enzyme from forming a homodimer, both in the crystal lattice and in solution. Our biochemical and structural data help to understand the hPus1p active site architecture, and detail its particular requirements with regard to one of its nuclear substrates, the non-coding RNA SRA

Overall views of the crystal structure of ΔhPus1p.

<p>(A) The ΔhPus1p monomer is shown in a cartoon representation and coloured according to secondary structure elements (red, α-helices; yellow, β-sheet; and green, loops). The C-terminal helices, which are specific to the hPus1p protein, are shown in blue. (B) Domain organization of ΔhPus1p. The N-terminal and C-terminal domains are orange and green, respectively; specific C-terminal helices are blue. The two domains form a cavity, which contains the active site. Two functionally important loops, the Forefinger and the Thumb loop are labelled. The catalytic amino acid residues are shown as sticks and coloured according to atom type (carbon, nitrogen, oxygen, and sulphur are yellow, blue, red, and gold, respectively) (C) Surface conservation and temperature factor representation of hPus1p. Conservation scores were determined with the ConSurf server (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094610#pone.0094610-Ashkenazy1" target="_blank">[42]</a>), and plotted on the surface of the ΔhPus1p structure (blue, variable; red, conserved,). The temperature factors of the refined model were plotted by the same method. Low B factors (stable) are blue and high B factors (flexible) are red. A dashed circle (yellow or red) indicates the location of the enzyme active site.</p

Close view of the active site residues from the ΔhPus1p and the ΔhPus1p D146A crystal structures.

<p>(A) A D-lysine molecule from the crystallisation condition is bound within the active site of molecule A of the ΔhPus1p enzyme. (B) A D-glutamate molecule is bound in the active site of molecule B of the same crystal structure. (C) A HEPES molecule is found in the active site of the inactive ΔhPus1p D146A crystal structure. Amino acids in the active site are shown and coloured as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094610#pone-0094610-g002" target="_blank">Figure 2B</a>. Bound molecules are shown as sticks and coloured according to atom type (carbon, nitrogen, oxygen, and sulphur are magenta, blue, red, and gold, respectively). Water molecules are shown as red spheres. Hydrogen bonds between the amino acids and the protein atoms are indicated as black dash lines.</p

Pseudouridine incorporation measured with various hPus1p enzymes.

a<p>The activities are in moles ψ/moles RNA with the mean of three separate assays and standard deviation (SD). With all of the substrates the maximum activity should be ∼1.0 moles ψ/moles RNA. Background levels of activity seen with Lac (β-galactosidase) have been subtracted. ND indicates that the combination of D146A ΔhPus1p D146A and the corresponding RNA substrate was not determined.</p

Affinity constants between hPus1p and the various H7 SRA substrates.

a<p>Listed are the standard deviation (SD) of two distinct binding experiments.</p>b<p>n represents the number of binding sites between the protein and the SRA fragment.</p><p>R² represents the coefficient of determination calculated for each fitting curve.</p

Human pseudouridine synthase 1 binds to various SRA constructs.

<p>(A) Secondary structures and sequences of the SRA constructs used in the binding tests with the putative U to Ψ position indicated. (B) Fluorescence anisotropy measurement of SRA binding to different hPus1p constructs. Fluorescein-conjugated SRA sequences were incubated with increasing amounts of the indicated proteins (full length hPus1, filled circle; ΔhPus1p, filled square; ΔhPus1p D146A, filled triangle). Normalized anisotropy values from 2–3 experiments were plotted against protein concentrations to measure binding affinities.</p

Data collection and refinement statistics.

<p>Values in parentheses are for highest-resolution shell.</p>a<p><i>R</i>_meas  =  ∑_hkl [N/(N−1)]^1/2 ∑_i |I(hkl)− |/∑_hkl ∑_I I(hkl), where N is the multiplicity of a given reflection.</p>b<p><i>R</i>_cryst  =  ∑||<i>F</i>_obs| − |<i>F</i>_calc||/∑|<i>F</i>_obs| for all reflections.</p>c<p><i>R</i>_free was calculated on the 5% of data excluded from refinement.</p

Logic programming reveals alteration of key transcription factors in multiple myeloma

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method