Knowledge of the complete nucleotide sequence of the mouse TCRAD locus allows an accurate determination V-J rearrangement status. Using multiplex genomic PCR assays and real time PCR analysis, we report a comprehensive and systematic analysis of the V-J recombination of TCR α chain in normal mouse thymocytes during development. These respective qualitative and quantitative approaches give rise to four major points describing the control of gene rearrangements. (a) The V-J recombination pattern is not random during ontogeny and generates a limited TCR α repertoire; (b) V-J rearrangement control is intrinsic to the thymus; (c) each V gene rearranges to a set of contiguous J segments with a gaussian-like frequency; (d) there are more rearrangements involving V genes at the 3′ side than 5′ end of V region. Taken together, this reflects a preferential association of V and J gene segments according to their respective positions in the locus, indicating that accessibility of both V and J regions is coordinately regulated, but in different ways. These results provide a new insight into TCR α repertoire size and suggest a scenario for V usage during differentiation.

IMGT/GeneInfo is a user-friendly online information system that provides information on data resulting from the complex mechanisms of immunoglobulin (IG) and T cell receptor (TR) V(D)J recombinations. For the first time, it is possible to visualize all the rearrangement parameters on a single page. IMGT/GeneInfo is part of the international ImMunoGeneTics information system (IMGT), a high-quality integrated knowledge resource specializing in IG, TR, major histocompatibility complex (MHC), and related proteins of the immune system of human and other vertebrate species. The IMGT/GeneInfo system was developed by the TIMC and ICH laboratories (with the collaboration of LIGM), and is the first example of an external system being incorporated into IMGT. In this paper, we report the first part of this work. IMGT/GeneInfo_TR deals with the human and mouse TRA/TRD and TRB loci of the TR. Data handling and visualization are complementary to the current data and tools in IMGT, and will subsequently allow the modelling of V(D)J gene use, and thus, to predict non-standard recombination profiles which may eventually be found in conditions such as leukaemias or lymphomas. Access to IMGT/GeneInfo is free and can be found at http://imgt.cines.fr/GeneInfo.

We deal in this paper with the concept of genetic regulation network. The genes expression observed through the bio-array imaging allows the geneticist to obtain the intergenic interaction matrix W of the network. The interaction graph G associated to W presents in general interesting features like connected components, gardens of Eden, positive and negative circuits (or loops), and minimal components having 1 positive and 1 negative loop called regulons. Depending on parameters values like the connectivity coefficient K(W) and the mean inhibition weight I (W), the genetic regulation network can present several dynamical behaviours (fixed configuration, limit cycle of configurations) called attractors, when the observation time increases. We give some examples of such genetic regulation networks and analyse their dynamical properties and their biological consequences.

This paper proposes a study of biological regulatory networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

T-Cell antigen Receptor (TR) repertoire is generated through rearrangements of V and J genes encoding α and β chains. The quantification and frequency for every V-J combination during ontogeny and development of the immune system remain to be precisely established. We have addressed this issue by building a model able to account for Vα-Jα gene rearrangements during thymus development of mice. So we developed a numerical model on the whole TRA/TRD locus, based on experimental data, to estimate how Vα and Jα genes become accessible to rearrangements. The progressive opening of the locus to V-J gene recombinations is modeled through windows of accessibility of different sizes and with different speeds of progression. Furthermore, the possibility of successive secondary V-J rearrangements was included in the modelling. The model points out some unbalanced V-J associations resulting from a preferential access to gene rearrangements and from a non-uniform partition of the accessibility of the J genes, depending on their location in the locus. The model shows that 3 to 4 successive rearrangements are sufficient to explain the use of all the V and J genes of the locus. Finally, the model provides information on both the kinetics of rearrangements and frequencies of each V-J associations. The model accounts for the essential features of the observed rearrangements on the TRA/TRD locus and may provide a reference for the repertoire of the V-J combinatorial diversity.

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The new tools available for gene expression studies are essentially the bio-array methods using a large variety of physical detectors (isotopes, fluorescent markers, ultrasounds...). Here we present first rapidly an image-processing method independent of the detector type, dealing with the noise and with the peaks overlapping, the peaks revealing the detector activity (isotopic in the presented example), correlated with the gene expression. After this primary step of bio-array image processing, we can extract information about causal influence (activation or inhibition) a gene can exert on other genes, leading to clusters of genes co-expression in which we extract an interaction matrix M and an associated interaction graph G explaining the genetic regulatory dynamics correlated to the studied tissue function. We give two examples of such interaction matrices and graphs (the flowering genetic regulatory network of Arabidopsis thaliana and the lytic/lysogenic operon of the phage Mu) and after some theoretical rigorous results recently obtained concerning the asymptotic states generated by the genetic networks having a given interaction matrix and reciprocally concerning the minimal (in the sense of having a minimal number of non-zero coefficients) matrices having given stationary stable states.