Spectral analysis of gene expression profiles using gene networks

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation

Hyperactivation of DNA-PK by Double-Strand Break Mimicking Molecules Disorganizes DNA Damage Response

Cellular response to DNA damage involves the coordinated activation of cell cycle checkpoints and DNA repair. The early steps of DNA damage recognition and signaling in mammalian cells are not yet fully understood. To investigate the regulation of the DNA damage response (DDR), we designed short and stabilized double stranded DNA molecules (Dbait) mimicking double-strand breaks. We compared the response induced by these molecules to the response induced by ionizing radiation. We show that stable 32-bp long Dbait, induce pan-nuclear phosphorylation of DDR components such as H2AX, Rpa32, Chk1, Chk2, Nbs1 and p53 in various cell lines. However, individual cell analyses reveal that differences exist in the cellular responses to Dbait compared to irradiation. Responses to Dbait: (i) are dependent only on DNA-PK kinase activity and not on ATM, (ii) result in a phosphorylation signal lasting several days and (iii) are distributed in the treated population in an “all-or-none” pattern, in a Dbait-concentration threshold dependant manner. Moreover, despite extensive phosphorylation of the DNA-PK downstream targets, Dbait treated cells continue to proliferate without showing cell cycle delay or apoptosis. Dbait treatment prior to irradiation impaired foci formation of Nbs1, 53BP1 and Rad51 at DNA damage sites and inhibited non-homologous end joining as well as homologous recombination. Together, our results suggest that the hyperactivation of DNA-PK is insufficient for complete execution of the DDR but induces a “false” DNA damage signaling that disorganizes the DNA repair system

Modelling of complex biological systems in the context of genomics: an account of a multidisciplinary thematic seminar held in Montpellier (France) in April 2005

International audienceno abstrac

Inferring biological networks with output kernel trees

International audienc

Optimizing the Design of Oligonucleotides for Homology Directed Gene Targeting

BACKGROUND: Gene targeting depends on the ability of cells to use homologous recombination to integrate exogenous DNA into their own genome. A robust mechanistic model of homologous recombination is necessary to fully exploit gene targeting for therapeutic benefit. METHODOLOGY/PRINCIPAL FINDINGS: In this work, our recently developed numerical simulation model for homology search is employed to develop rules for the design of oligonucleotides used in gene targeting. A Metropolis Monte-Carlo algorithm is used to predict the pairing dynamics of an oligonucleotide with the target double-stranded DNA. The model calculates the base-alignment between a long, target double-stranded DNA and a probe nucleoprotein filament comprised of homologous recombination proteins (Rad51 or RecA) polymerized on a single strand DNA. In this study, we considered different sizes of oligonucleotides containing 1 or 3 base heterologies with the target; different positions on the probe were tested to investigate the effect of the mismatch position on the pairing dynamics and stability. We show that the optimal design is a compromise between the mean time to reach a perfect alignment between the two molecules and the stability of the complex. CONCLUSION AND SIGNIFICANCE: A single heterology can be placed anywhere without significantly affecting the stability of the triplex. In the case of three consecutive heterologies, our modeling recommends using long oligonucleotides (at least 35 bases) in which the heterologous sequences are positioned at an intermediate position. Oligonucleotides should not contain more than 10% consecutive heterologies to guarantee a stable pairing with the target dsDNA. Theoretical modeling cannot replace experiments, but we believe that our model can considerably accelerate optimization of oligonucleotides for gene therapy by predicting their pairing dynamics with the target dsDNA

Classification of microarray data using gene networks

BACKGROUND: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. RESULTS: We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. CONCLUSION: Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results

French Roadmap for complex Systems 2008-2009

This second issue of the French Complex Systems Roadmap is the outcome of the Entretiens de Cargese 2008, an interdisciplinary brainstorming session organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It capitalizes on the first roadmap and gathers contributions of more than 70 scientists from major French institutions. The aim of this roadmap is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in the complex systems sciences and complexity science to the public, political and industrial spheres