Biomedical subjects

Jacques Rougemont

Publications and source records attributed to Jacques Rougemont.

10 recordsLinked to original sources

PubMed2007

Early history of mammals is elucidated with the ENCODE multiple species sequencing data.

Understanding the early evolution of placental mammals is one of the most challenging issues in mammalian phylogeny. Here, we addressed this question by using the sequence data of the ENCODE consortium, which include 1% of mammalian genomes in 18 species belonging to all main mammalian lineages. Phylogenetic reconstructions based on an unprecedented amount of coding sequences taken from 218 genes resulted in a highly supported tree placing the root of Placentalia between Afrotheria and Exafroplacentalia (Afrotheria hypothesis). This topology was validated by the phylogenetic analysis of a new class of genomic phylogenetic markers, the conserved noncoding sequences. Applying the tests of alternative topologies on the coding sequence dataset resulted in the rejection of the Atlantogenata hypothesis (Xenarthra grouping with Afrotheria), while this test rejected the second alternative scenario, the Epitheria hypothesis (Xenarthra at the base), when using the noncoding sequence dataset. Thus, the two datasets support the Afrotheria hypothesis; however, none can reject both of the remaining topological alternatives.

Animals↗

PubMed2006

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies.

BACKGROUND: High throughput gene expression profiling (GEP) is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option.GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported. RESULTS: MAF (MicroArray Facility) is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking), data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF. CONCLUSION: MicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for shared facilities and industry service providers alike.

Cloning, Molecular↗

PubMed2006

Few crucial links assure checkpoint efficiency in the yeast cell-cycle network.

MOTIVATION: The ability of cells to complete mitosis with high fidelity relies on elaborate checkpoint mechanisms. We study S- and M-phase checkpoint responses in silico in the budding yeast with a stochastic dynamical model for the cell-cycle. We aim to provide an unbiased functional classification of network interactions that reflect the contribution of each link to checkpoint efficiency in the presence of cellular fluctuations. RESULTS: We developed an algorithm BNetDyn to compute stochastic dynamical trajectories for an input gene network and its structural perturbations. User specified output measures like the mutual information between trigger and output nodes are then evaluated on the stationary state of the Markov process. Systematic perturbations of the yeast cell-cycle model by Li et al. classify each link according to its effect on checkpoint efficiencies and stabilities of the main cell-cycle phases. This points to the crosstalk in the cascades downstream of the SBF/MBF transcription activator complexes as determinant for checkpoint optimality; a finding that consistently reflects recent experiments. Finally our stochastic analysis emphasizes how dynamical stability in the yeast cell-cycle network crucially relies on backward inhibitory circuits next to forward induction. AVAILABILITY: C++ source code and network models can be downloaded at http://www.vital-it.ch/Software/

Cell Cycle↗

PubMed2006

Nemo: an evolutionary and population genetics programming framework.

UNLABELLED: Nemo is an individual-based, genetically explicit and stochastic population computer program for the simulation of population genetics and life-history trait evolution in a metapopulation context. It comes as both a C++ programming framework and an executable program file. Its object-oriented programming design gives it the flexibility and extensibility needed to implement a large variety of forward-time evolutionary models. It provides developers with abstract models allowing them to implement their own life-history traits and life-cycle events. Nemo offers a large panel of population models, from the Island model to lattice models with demographic or environmental stochasticity and a variety of already implemented traits (deleterious mutations, neutral markers and more), life-cycle events (mating, dispersal, aging, selection, etc.) and output operators for saving data and statistics. It runs on all major computer platforms including parallel computing environments. AVAILABILITY: The source code, binaries and documentation are available under the GNU General Public License at http://nemo2.sourceforge.net.

Biological Evolution↗

PubMed2006

Collective synchronization in populations of globally coupled phase oscillators with drifting frequencies.

We generalize the Kuramoto model for coupled phase oscillators by allowing the frequencies to drift in time according to Ornstein-Uhlenbeck dynamics. Such drifting frequencies were recently measured in cellular populations of circadian oscillator and inspired our work. Linear stability analysis of the Fokker-Planck equation for an infinite population is amenable to exact solution and we show that the incoherent state is unstable past a critical coupling strength K(c)(gamma,sigma(f)), where gamma is the inverse characteristic drifting time and sigma(f) the asymptotic frequency dispersion. Expectedly K(c)agrees with the noisy Kuramoto model in the large gamma (Schmolukowski) limit but increases slower as gamma decreases. Asymptotic expansion of the solution for gamma-->0 shows that the noiseless Kuramoto model with Gaussian frequency distribution is recovered in that limit. Thus varying a single parameter allows us to interpolate smoothly between two regimes: one dominated by the frequency dispersion and the other by phase diffusion.

Biophysics↗

PubMed2005

Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes.

Inter-individual differences in gene expression are likely to account for an important fraction of phenotypic differences, including susceptibility to common disorders. Recent studies have shown extensive variation in gene expression levels in humans and other organisms, and that a fraction of this variation is under genetic control. We investigated the patterns of gene expression variation in a 25 Mb region of human chromosome 21, which has been associated with many Down syndrome (DS) phenotypes. Taqman real-time PCR was used to measure expression variation of 41 genes in lymphoblastoid cells of 40 unrelated individuals. For 25 genes found to be differentially expressed, additional analysis was performed in 10 CEPH families to determine heritabilities and map loci harboring regulatory variation. Seventy-six percent of the differentially expressed genes had significant heritabilities, and genomewide linkage analysis led to the identification of significant eQTLs for nine genes. Most eQTLs were in trans, with the best result (P=7.46 x 10(-8)) obtained for TMEM1 on chromosome 12q24.33. A cis-eQTL identified for CCT8 was validated by performing an association study in 60 individuals from the HapMap project. SNP rs965951 located within CCT8 was found to be significantly associated with its expression levels (P=2.5 x 10(-5)) confirming cis-regulatory variation. The results of our study provide a representative view of expression variation of chromosome 21 genes, identify loci involved in their regulation and suggest that genes, for which expression differences are significantly larger than 1.5-fold in control samples, are unlikely to be involved in DS-phenotypes present in all affected individuals.

Chromosome Mapping↗

PubMed2005

Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.

Breast cancer is a heterogeneous disease. Comprehensive gene expression profiles obtained using DNA microarrays have revealed previously indistinguishable subtypes of noninflammatory breast cancer (NIBC) related to different features of mammary epithelial biology and significantly associated with survival. Inflammatory breast cancer (IBC) is a rare, particular, and aggressive form of disease. Here we have investigated whether the five molecular subtypes described for NIBC (luminal A and B, basal, ERBB2 overexpressing, and normal breast-like) were also present in IBC. We monitored the RNA expression of approximately 8,000 genes in 83 breast tissue samples including 37 IBC, 44 NIBC, and 2 normal breast samples. Hierarchical clustering identified the five subtypes of breast cancer in both NIBC and IBC samples. These subtypes were highly similar to those defined in previous studies and associated with similar histoclinical features. The robustness of this classification was confirmed by the use of both alternative gene set and analysis method, and the results were corroborated at the protein level. Furthermore, we show that the differences in gene expression between NIBC and IBC and between IBC with and without pathologic complete response that we have recently reported persist in each subtype. Our results show that the expression signatures defining molecular subtypes of NIBC are also present in IBC. Obtained using different patient series and different microarray platforms, they reinforce confidence in the expression-based molecular taxonomy but also give evidence for its universality in breast cancer, independently of a specific clinical form.

Adenocarcinoma↗

PubMed2005

Protein expression profiling identifies subclasses of breast cancer and predicts prognosis.

Breast cancer is a heterogeneous disease whose evolution is difficult to predict by using classic histoclinical prognostic factors. Prognostic classification can benefit from molecular analyses such as large-scale expression profiling. Using immunohistochemistry on tissue microarrays, we have monitored the expression of 26 selected proteins in more than 1,600 cancer samples from 552 consecutive patients with early breast cancer. Both an unsupervised approach and a new supervised method were used to analyze these profiles. Hierarchical clustering identified relevant clusters of coexpressed proteins and clusters of tumors. We delineated protein clusters associated with the estrogen receptor and with proliferation. Tumor clusters correlated with several histoclinical features of samples, including 5-year metastasis-free survival (MFS), and with the recently proposed pathophysiologic taxonomy of disease. The supervised method identified a set of 21 proteins whose combined expression significantly correlated to MFS in a learning set of 368 patients (P < 0.0001) and in a validation set of 184 patients (P < 0.0001). Among the 552 patients, the 5-year MFS was 90% for patients classified in the "good-prognosis class" and 61% for those classified in the "poor-prognosis class" (P < 0.0001). This difference remained significant when the molecular grouping was applied according to lymph node or estrogen receptor status, as well as the type of adjuvant systemic therapy. In multivariate analysis, the 21-protein set was the strongest independent predictor of clinical outcome. These results show that protein expression profiling may be a clinically useful approach to assess breast cancer heterogeneity and prognosis in stage I, II, or III disease.

Adult↗

PubMed2004

Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy.

Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer with a 5-year survival limited to approximately 40%. Diagnosis, based on clinical and/or pathological criteria, may be difficult. Optimal systemic neoadjuvant therapy and accurate predictors of pathological response have yet to be defined for increasing response rate and survival. Using DNA microarrrays containing approximately 8,000 genes, we profiled breast cancer samples from 81 patients, including 37 with IBC and 44 with noninflammatory breast cancer (NIBC). Global unsupervised hierarchical clustering was able to some extent to distinguish IBC and NIBC cases and revealed subclasses of IBC. Supervised analysis identified a 109-gene set the expression of which discriminated IBC from NIBC samples. This molecular signature was validated in an independent series of 26 samples, with an overall performance accuracy of 85%. Discriminator genes were associated with various cellular processes possibly related to the aggressiveness of IBC, including signal transduction, cell motility, adhesion, and angiogenesis. A similar approach, with leave-one-out cross-validation, identified an 85-gene set that divided IBC patients with significantly different pathological complete response rate (70% in one group and 0% in the other group). These results show the potential of gene expression profiling to contribute to a better understanding of IBC, and to provide new diagnostic and predictive factors for IBC, as well as for potential therapeutic targets.

Adult↗

PubMed2003

DNA microarray data and contextual analysis of correlation graphs.

BACKGROUND: DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation. RESULTS: We study networks of co-expressed genes obtained from DNA microarray experiments. The mathematical concept of curvature on graphs is used to group genes or samples into clusters to which relevant gene or sample annotations are automatically assigned. Application to publicly available yeast and human lymphoma data demonstrates the reliability of the method in spite of its simplicity, especially with respect to the small number of parameters involved. CONCLUSIONS: We provide a method for automatically determining relevant gene clusters among the many genes monitored with microarrays. The automatic annotations and the graphical interface improve the readability of the data. A C++ implementation, called Trixy, is available from http://tagc.univ-mrs.fr/bioinformatics/trixy.html.

Algorithms↗