Biomedical subjects
Publications and source records attributed to Stephane Rombauts.
Sheep scab, caused by infestation with the ectoparasitic mite, Psoroptes ovis, represents a major welfare and economic challenge for the livestock industry. We present an updated 62.7 Mb genome assembly containing 10 chromosome-level scaffolds with 10,516 annotated protein-coding genes, which provides a useful resource for studies into resistance and control.
Previously we have shown that overexpression of the heterodimeric E2Fa-DPa transcription factor in Arabidopsis thaliana results in ectopic cell division, increased endoreduplication, and an early arrest in development. To gain a better insight into the phenotypic behavior of E2Fa-DPa transgenic plants and to identify E2Fa-DPa target genes, a transcriptomic microarray analysis was performed. Out of 4,390 unique genes, a total of 188 had a twofold or more up- (84) or down-regulated (104) expression level in E2Fa-DPa transgenic plants compared to wild-type lines. Detailed promoter analysis allowed the identification of novel E2Fa-DPa target genes, mainly involved in DNA replication. Secondarily induced genes encoded proteins involved in cell wall biosynthesis, transcription and signal transduction or had an unknown function. A large number of metabolic genes were modified as well, among which, surprisingly, many genes were involved in nitrate assimilation. Our data suggest that the growth arrest observed upon E2Fa-DPa overexpression results at least partly from a nitrogen drain to the nucleotide synthesis pathway, causing decreased synthesis of other nitrogen compounds, such as amino acids and storage proteins.
SUMMARY: A drawback of the Amplified Fragment Length Polymorphism (AFLP) fingerprinting method is the difficulty to correlate the different fragments with their DNA sequence. The AFLPinSilico application presented here simulates AFLP experiments run on either cDNA or genomic sequences, producing virtual fingerprints that allow high throughput identification of AFLP fragments. The program also enables biologists to manage experiments through simulations done beforehand, thereby reducing the number of experiments that have to be run. AFLPinSilico is available through the www or as a stand-alone version, through a command line executable (available upon request, for any platform running PERL).
The type I MADS-box genes constitute a largely unexplored subfamily of the extensively studied MADS-box gene family, well known for its role in flower development. Genes of the type I MADS-box subfamily possess the characteristic MADS box but are distinguished from type II MADS-box genes by the absence of the keratin-like box. In this in silico study, we have structurally annotated all 47 members of the type I MADS-box gene family in Arabidopsis thaliana and exerted a thorough analysis of the C-terminal regions of the translated proteins. On the basis of conserved motifs in the C-terminal region, we could classify the gene family into three main groups, two of which could be further subdivided. Phylogenetic trees were inferred to study the evolutionary relationships within this large MADS-box gene subfamily. These suggest for plant type I genes a dynamic of evolution that is significantly different from the mode of both animal type I (SRF) and plant type II (MIKC-type) gene phylogeny. The presence of conserved motifs in the majority of these genes, the identification of Oryza sativa MADS-box type I homologues, and the detection of expressed sequence tags for Arabidopsis thaliana and other plant type I genes suggest that these genes are indeed of functional importance to plants. It is therefore even more intriguing that, from an experimental point of view, almost nothing is known about the function of these MADS-box type I genes.
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.
Using synchronized tobacco Bright Yellow-2 cells and cDNA-amplified fragment length polymorphism-based genomewide expression analysis, we built a comprehensive collection of plant cell cycle-modulated genes. Approximately 1,340 periodically expressed genes were identified, including known cell cycle control genes as well as numerous unique candidate regulatory genes. A number of plant-specific genes were found to be cell cycle modulated. Other transcript tags were derived from unknown plant genes showing homology to cell cycle-regulatory genes of other organisms. Many of the genes encode novel or uncharacterized proteins, indicating that several processes underlying cell division are still largely unknown.
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/.
The ancient land plant Physcomitrella patens is a model system that is becoming increasingly important for plant functional genomics because gene knockouts can be produced with relative ease. Recently, several EST-sequencing projects have been launched as a first step towards a thorough functional characterization of the moss. However, for careful comparison with other plant model systems, the complete genomic sequence is needed as well as the transcriptome.
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.
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.
Cyclin-dependent kinases and cyclins regulate with the help of different interacting proteins the progression through the eukaryotic cell cycle. A high-quality, homology-based annotation protocol was applied to determine the core cell cycle genes in the recently completed Arabidopsis genome sequence. In total, 61 genes were identified belonging to seven selected families of cell cycle regulators, for which 30 are new or corrections of the existing annotation. A new class of putative cell cycle regulators was found that probably are competitors of E2F/DP transcription factors, which mediate the G1-to-S progression. In addition, the existing nomenclature for cell cycle genes of Arabidopsis was updated, and the physical positions of all genes were compared with segmentally duplicated blocks in the genome, showing that 22 core cell cycle genes emerged through block duplications. This genome-wide analysis illustrates the complexity of the plant cell cycle machinery and provides a tool for elucidating the function of new family members in the future.