Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes

Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined.Comment: Published version available from http://www.biomedcentral.com/1471-2105/3/

Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities

Abstract Motivation: The computational evaluation of candidate genes for hereditary disorders is a non-trivial task. Several excellent methods for disease-gene prediction have been developed in the past 2 decades, exploiting widely differing data sources to infer disease-relevant functional relationships between candidate genes and disorders. We have shown recently that spatially mapped, i.e. 3D, gene expression data from the mouse brain can be successfully used to prioritize candidate genes for human Mendelian disorders of the central nervous system. Results: We improved our previous work 2-fold: (i) we demonstrate that condition-independent transcription factor binding affinities of the candidate genes' promoters are relevant for disease-gene prediction and can be integrated with our previous approach to significantly enhance its predictive power; and (ii) we define a novel similarity measure—termed Relative Intensity Overlap—for both 3D gene expression patterns and binding affinity profiles that better exploits their disease-relevant information content. Finally, we present novel disease-gene predictions for eight loci associated with different syndromes of unknown molecular basis that are characterized by mental retardation. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Citron Kinase Is a Cell Cycle-dependent, Nuclear Protein Required for G 2 /M Transition of Hepatocytes

Citron Kinase (Citron-K) is a cell cycle-dependent protein regulating the G(2)/M transition in hepatocytes. Synchronization studies demonstrated that expression of the Citron-K protein starts at the late S and/or the early G(2) phase after that of cyclin B1. Expression of Citron-K is developmentally regulated. Levels of Citron-K mRNA and protein are highest in embryonic liver and gradually decrease after birth. Citron-K exists in interphase nuclei and begins to disperse into the cytoplasm at prophase. It concentrates at the cleavage furrow and midbody during anaphase, telophase, and cytokinesis, implicating a role in the control of cytokinesis. However, studies with knockouts show that Citron-K is not essential for cytokinesis in hepatocytes. Instead, loss of Citron-K causes a significant increase of G(2) tetraploid nuclei in one-week-old rat and mouse liver. In addition, Citron-K deficiency triggers apoptosis in a small subset of embryonic liver cells. In summary, our data demonstrate that Citron-K has a distinct cell cycle-dependent expression pattern and cellular localization as a downstream target of Rho-GTPase and functions in the control of G(2)/M transition in the hepatocyte cell cycle