Biomedical subjects

Michel Bellis

Publications and source records attributed to Michel Bellis.

6 recordsLinked to original sources

PubMed2006

Chromosomal distribution of PcG proteins during Drosophila development.

Polycomb group (PcG) proteins are able to maintain the memory of silent transcriptional states of homeotic genes throughout development. In Drosophila, they form multimeric complexes that bind to specific DNA regulatory elements named PcG response elements (PREs). To date, few PREs have been identified and the chromosomal distribution of PcG proteins during development is unknown. We used chromatin immunoprecipitation (ChIP) with genomic tiling path microarrays to analyze the binding profile of the PcG proteins Polycomb (PC) and Polyhomeotic (PH) across 10 Mb of euchromatin. We also analyzed the distribution of GAGA factor (GAF), a sequence-specific DNA binding protein that is found at most previously identified PREs. Our data show that PC and PH often bind to clustered regions within large loci that encode transcription factors which play multiple roles in developmental patterning and in the regulation of cell proliferation. GAF co-localizes with PC and PH to a limited extent, suggesting that GAF is not a necessary component of chromatin at PREs. Finally, the chromosome-association profile of PC and PH changes during development, suggesting that the function of these proteins in the regulation of some of their target genes might be more dynamic than previously anticipated.

Animals↗

PubMed2006

Mapping the distribution of chromatin proteins by ChIP on chip.

The ChIP on chip method combines chromatin immunoprecipitation (ChIP) with hybridization on DNA microarrays (chip). The ChIP technique allows one to obtain a DNA sample enriched in sequences bound by transcription factors or chromatin-associated proteins. Usually, ChIP is used to test whether specific candidate sequences are bound by a transcription factor, but microarrays are a powerful tool that allows testing large pools of sequences at once. This chapter presents the pipeline of a ChIP on chip method that can be applied to map the binding sites of chromatin-associated proteins along Drosophila chromosomes at different developmental stages. This chapter provides protocols for ChIP, for quality control tests of ChIP samples, for microarray design, for hybridization of the ChIP samples onto microarrays, and for initial analysis of the data. In addition, this chapter discusses the most important steps in each of the protocols as well as the importance of bioinformatic analysis in order to extract valuable biological information from the data sets.

Chromatin↗

PubMed2006

Clustering methods for analyzing large data sets: gonad development, a study case.

With the development of data set repositories, it is now possible to collate high numbers of related results by gathering data from experiments carried out in different laboratories and addressing similar questions or using a single type of biological material under different conditions. To address the challenge posed by the heterogeneous nature of multiple data sources, this chapter presents several methods used routinely for assessing the quality of data (i.e., reproducibility of replicates and similarity between experimental points obtained under identical or similar biological conditions). As gene clustering on large data sets is not straightforward, this chapter also presents a rapid gene clustering method that involves translating variation profiles from an ordered set of comparisons into chains of symbols. In addition, it shows that lists of genes assembled based on the presence of a common term in their functional description can be used to find the most informative comparisons and to construct from them exemplar chains of symbols that are useful for clustering similar genes. Finally, this symbolic approach is extended to the overall set of biological conditions under study and shows how the resultant collection of variation profiles can be used to construct transcriptional networks, which in turn can be used as powerful tools for gene clustering.

Animals↗

PubMed2005

Gene expression profiling of the effects of intravenous immunoglobulin in human whole blood.

Intravenous immunoglobulin (IVIG) is involved in many complex mechanisms that act in synergy including expression and function of Fc receptors, complement activation, the cytokine network, interaction with the anti-idiotypic network and modulation of B and T cell activation. To gain insight into the early effects of IVIG on this broad range of activities at the gene level we performed DNA microarray analysis. Human whole blood was incubated in vitro for 4 h followed by extraction of RNA which was hybridized to a chip containing 8793 genes. About 75 upregulated genes and 21 downregulated genes were identified using a cut off for the false discovery rate of 5%. These genes are associated with a wide range of cellular immune functions in line with the broad mechanism of action of IVIG. A striking upregulation of a series of genes coding for chemokines was measured. This finding was confirmed at the protein level as pharmacologically relevant concentrations of CXCL9 and CXCL10 were measured in serum. Interestingly, IVIG shows a partial overlap of its gene expression program with lipopolysaccharide. Our data suggests multiple hypotheses regarding the pharmacology of IVIG that must be validated by complementary studies.

Blood↗

PubMed2004

Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data.

BACKGROUND: A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions. RESULTS: We describe a new method, Rank Difference Analysis of Microarrays (RDAM), which estimates the total number of truly varying genes and assigns a p-value to each signal variation. Information on a group of differentially expressed genes includes the sensitivity and the false discovery rate. We demonstrate the feasibility and efficiency of our approach by applying it to a large synthetic expression data set and to a biological data set obtained by comparing vegetatively-growing wild type and tor2-mutant yeast strains. In both cases we observed a significant improvement of the power of analysis when our method is compared to another popular nonparametric method. CONCLUSIONS: This study provided a valuable new statistical method to analyze microarray data. We conclude that the good quality of the results obtained by RDAM is mainly due to the quasi-perfect equalization of variation distribution, which is related to the standardization procedure used and to the measurement of variation by rank difference.

Gene Expression Profiling↗

PubMed2002

The Ume6 regulon coordinates metabolic and meiotic gene expression in yeast.

The Ume6 transcription factor in yeast is known to both repress and activate expression of diverse genes during growth and meiotic development. To obtain a more complete profile of the functions regulated by this protein, microarray analysis was used to examine transcription in wild-type and ume6Delta diploids during vegetative growth in glucose and acetate. Two different genetic backgrounds (W303 and SK1) were examined to identify a core set of strain-independent Ume6-regulated genes. Among genes whose expression is controlled by Ume6 in both backgrounds, 82 contain homologies to the Ume6-binding site (URS1) and are expected to be directly regulated by Ume6. The vast majority of those whose functions are known participate in carbon/nitrogen metabolism and/or meiosis. Approximately half of the Ume6 direct targets are induced during meiosis, with most falling into the early meiotic expression class (cluster 4), and a smaller subset in the middle and later classes (clusters 5-7). Based on these data, we propose that Ume6 serves a unique role in diploid cells, coupling metabolic responses to nutritional cues with the initiation and progression of meiosis. Finally, expression patterns in the two genetic backgrounds suggest that SK1 is better adapted to respiration and W303 to fermentation, which may in part account for the more efficient and synchronous sporulation of SK1.

DNA-Binding Proteins↗