Biomedical subjects

Gaëlle Lelandais

Publications and source records attributed to Gaëlle Lelandais.

5 recordsLinked to original sources

PubMed2006

The central role of PDR1 in the foundation of yeast drug resistance.

The widespread pleiotropic drug resistance (PDR) phenomenon is well described as the long term selection of genetic variants expressing constitutively high levels of membrane transporters involved in drug efflux. However, the transcriptional cascades leading to the PDR phenotype in wild-type cells are largely unknown, and the first steps of this phenomenon are poorly understood. We investigated the transcriptional mechanisms underlying the establishment of an efficient PDR response in budding yeast. We show that within a few minutes of drug sensing yeast elicits an effective PDR response, involving tens of PDR genes. This early PDR response (ePDR) is highly dependent on the Pdr1p transcription factor, which is also one of the major genetic determinants of long term PDR acquisition. The activity of Pdr1p in early drug response is not drug-specific, as two chemically unrelated drugs, benomyl and fluphenazine, elicit identical, Pdr1p-dependent, ePDR patterns. Our data also demonstrate that Pdr1p is an original stress response factor, the DNA binding properties of which do not depend on the presence of drugs. Thus, Pdr1p is a promoter-resident regulator involved in both basal expression and rapid drug-dependent induction of PDR genes.

Antipsychotic Agents↗

PubMed2006

Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms.

MOTIVATION: Molecular evolution, which is classically assessed by comparison of individual proteins or genes between species, can now be studied by comparing co-expressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by genome-wide expression analyses. However, it requires new methodologies to represent the data in a more accessible way for cross-species comparisons. RESULTS: In this work, we present an approach based on Multi-dimensional Scaling techniques, to compare the conformation of two gene expression networks, represented in a multi-dimensional space. The expression networks are optimally superimposed, taking into account two criteria: (1) inter-organism orthologous gene pairs have to be nearby points in the final multi-dimensional space and (2) the distortion of the gene expression networks, the organization of which reflects the similarities between the gene expression measurements, has to be circumscribed. Using this approach, we compared the transcriptional programs that drive sporulation in budding and fission yeasts, extracting some common properties and differences between the two species.

Computational Biology↗

PubMed2004

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states.

BACKGROUND: Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions. RESULTS: MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at http://www.transcriptome.ens.fr/micovito/. CONCLUSIONS: MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables.

Cluster Analysis↗

PubMed2004

yMGV: a cross-species expression data mining tool.

The yeast Microarray Global Viewer (yMGV @ http://transcriptome.ens.fr/ymgv) was created 3 years ago as a database that houses a collection of Saccharomyces cerevisiae and Schizosaccharo myces pombe microarray data sets published in 82 different articles. yMGV couples data mining tools with a user-friendly web interface so that, with a few mouse clicks, one can identify the conditions that affect the expression of a gene or list of genes regulated in a set of experiments. One of the major new features we present here is a set of tools that allows for inter-organism comparisons. This should enable the fission yeast community to take advantage of the large amount of available information on budding yeast transcriptome. New tools and ongoing developments are also presented here.

Computational Biology↗

PubMed2003

Competitive promoter occupancy by two yeast paralogous transcription factors controlling the multidrug resistance phenomenon.

Highly flexible gene expression programs are required to allow cell growth in the presence of a wide variety of chemicals. We used genome-wide expression analyses coupled with chromatin immunoprecipitation experiments to study the regulatory relationships between two very similar yeast transcription factors involved in the control of the multidrug resistance phenomenon. Yrm1 (Yor172w) is a new zinc finger transcription factor, the overproduction of which decreases the level of transcription of the target genes of Yrr1, a zinc finger transcription factor controlling the expression of several membrane transporter-encoding genes. Surprisingly, the absence of YRR1 releases the transcriptional activity of Yrm1, which then up-regulates 23 genes, 14 of which are also direct target genes of Yrr1. Chromatin immunoprecipitation experiments confirmed that Yrm1 binds to the promoters of the up-regulated genes only in yeast strains from which YRR1 has been deleted. This sophisticated regulatory program can be associated with drug resistance phenotypes of the cell. The program-specific distribution of paired transcription factors throughout the genome may be a general mechanism by which similar transcription factors regulate overlapping gene expression programs in response to chemical stress.

Alleles↗