Biomedical subjects

Magali Lescot

Publications and source records attributed to Magali Lescot.

5 recordsLinked to original sources

PubMed2003

Computational approaches to identify promoters and cis-regulatory elements in plant genomes.

The identification of promoters and their regulatory elements is one of the major challenges in bioinformatics and integrates comparative, structural, and functional genomics. Many different approaches have been developed to detect conserved motifs in a set of genes that are either coregulated or orthologous. However, although recent approaches seem promising, in general, unambiguous identification of regulatory elements is not straightforward. The delineation of promoters is even harder, due to its complex nature, and in silico promoter prediction is still in its infancy. Here, we review the different approaches that have been developed for identifying promoters and their regulatory elements. We discuss the detection of cis-acting regulatory elements using word-counting or probabilistic methods (so-called "search by signal" methods) and the delineation of promoters by considering both sequence content and structural features ("search by content" methods). As an example of search by content, we explored in greater detail the association of promoters with CpG islands. However, due to differences in sequence content, the parameters used to detect CpG islands in humans and other vertebrates cannot be used for plants. Therefore, a preliminary attempt was made to define parameters that could possibly define CpG and CpNpG islands in Arabidopsis, by exploring the compositional landscape around the transcriptional start site. To this end, a data set of more than 5,000 gene sequences was built, including the promoter region, the 5'-untranslated region, and the first introns and coding exons. Preliminary analysis shows that promoter location based on the detection of potential CpG/CpNpG islands in the Arabidopsis genome is not straightforward. Nevertheless, because the landscape of CpG/CpNpG islands differs considerably between promoters and introns on the one side and exons (whether coding or not) on the other, more sophisticated approaches can probably be developed for the successful detection of "putative" CpG and CpNpG islands in plants.

Computational Biology↗

PubMed2002

PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences.

PlantCARE is a database of plant cis-acting regulatory elements, enhancers and repressors. Regulatory elements are represented by positional matrices, consensus sequences and individual sites on particular promoter sequences. Links to the EMBL, TRANSFAC and MEDLINE databases are provided when available. Data about the transcription sites are extracted mainly from the literature, supplemented with an increasing number of in silico predicted data. Apart from a general description for specific transcription factor sites, levels of confidence for the experimental evidence, functional information and the position on the promoter are given as well. New features have been implemented to search for plant cis-acting regulatory elements in a query sequence. Furthermore, links are now provided to a new clustering and motif search method to investigate clusters of co-expressed genes. New regulatory elements can be sent automatically and will be added to the database after curation. The PlantCARE relational database is available via the World Wide Web at http://sphinx.rug.ac.be:8080/PlantCARE/.

Consensus Sequence↗

PubMed2002

Inducibility by pathogen attack and developmental regulation of the rice Ltp1 gene.

Using a genomic clone encoding a rice lipid transfer protein, LTP1, we analysed the activity of the 5' region of the Ltp1 gene in transgenic rice (Oryza sativa L.) during plant development and under pathogen attack. The -1176/+13, -556/+13 and -284/+13 regions of the promoter were fused upstream from the uidA reporter gene and nos 3' polyadenylation signal, resulting in the pdelta1176Gus, pdelta556Gus and pdelta284Gus constructs which were transferred to rice by microprojectile bombardment. Histochemical and fluorometric GUS assays and in situ detection of uidA transcripts in transgenic homozygous lines harbouring the pdelta1176Gus construct demonstrated that the Ltp1 promoter is preferentially active in aerial vegetative and reproductive organs and that both specificity and level of expression are regulated during organ development. In leaf sheath, GUS activity which is initially strictly localized in the epidermis of growing tissue, becomes restricted to the vascular system in mature tissues. In expanded leaf blade, expression of the uidA gene was restricted to the cutting level suggesting inducibility by wounding. Strong activity was detected in lemma and palea, sterile glumes, and immature anther walls and microspores but not in female reproductive organs. No GUS activity was detected during seed embryo maturation whereas the uidA gene was strongly expressed at early stages of somatic embryogenesis in scutellum tissue. The Ltp1 transcripts were found to strongly accumulate in response to inoculation with the fungal agent of the blast disease, Magnaporthe grisea, in two rice cultivars exhibiting compatible or incompatible host-pathogen interactions. Analysis of pdelta1176Gus leaf samples inoculated with the blast fungus demonstrated that the Ltp1 promoter is induced in all cell types of tissues surrounding the lesion and notably in stomata guard cells. In plants harbouring the Ltp1 promoter deletion construct pdelta556Gus, activity was solely detected in the vascular system of mature leaves whereas no uidA gene expression was observed in pdelta284Gus plants. These observations are consistent with the proposed role of LTP1 in strenghtening of structural barriers and organ protection against mechanical disruption and pathogen attack.

Antigens, Plant↗

PubMed2002

A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes.

Microarray experiments can reveal important information about transcriptional regulation. In our case, we look for potential promoter regulatory elements in the upstream region of coexpressed genes. Here we present two modifications of the original Gibbs sampling algorithm for motif finding (Lawrence et al., 1993). First, we introduce the use of a probability distribution to estimate the number of copies of the motif in a sequence. Second, we describe the technical aspects of the incorporation of a higher-order background model whose application we discussed in Thijs et al. (2001). Our implementation is referred to as the Motif Sampler. We successfully validate our algorithm on several data sets. First, we show results for three sets of upstream sequences containing known motifs: 1) the G-box light-response element in plants, 2) elements involved in methionine response in Saccharomyces cerevisiae, and 3) the FNR O(2)-responsive element in bacteria. We use these data sets to explain the influence of the parameters on the performance of our algorithm. Second, we show results for upstream sequences from four clusters of coexpressed genes identified in a microarray experiment on wounding in Arabidopsis thaliana. Several motifs could be matched to regulatory elements from plant defence pathways in our database of plant cis-acting regulatory elements (PlantCARE). Some other strong motifs do not have corresponding motifs in PlantCARE but are promising candidates for further analysis.

Algorithms↗

PubMed2002

INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling.

INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes.

Algorithms↗