Regulatory element detection using correlation with expression

We present here a new computational method for discovering cis-regulatory elements that circumvents the need to cluster genes based on their expression profiles. Based on a model in which upstream motifs contribute additively to the log-expression level of a gene, this method requires a single genome-wide set of expression ratios and the upstream sequence for each gene, and outputs statistically significant motifs. Analysis of publicly available expression data for Saccharomyces cerevisiae reveals several new putative regulatory elements, some of which plausibly control the early, transient induction of genes during sporulation. Known motifs generally have high statistical significance.     

Disruption of an imprinted gene cluster by a targeted chromosomal translocation in mice

Genomic imprinting is an epigenetic process in which the activity of a gene is determined by its parent of origin. Mechanisms governing genomic imprinting are just beginning to be understood. However, the tendency of imprinted genes to exist in chromosomal clusters suggests a sharing of regulatory elements. To better understand imprinted gene clustering, we disrupted a cluster of imprinted genes on mouse distal chromosome 7 using the Cre/loxP recombination system. In mice carrying a site-specific translocation separating Cdkn1c and Kcnq1, imprinting of the genes retained on chromosome 7, including Kcnq1, Kcnq1ot1, Ascl2, H19 and Igf2, is unaffected, demonstrating that these genes are not regulated by elements near or telomeric to Cdkn1c. In contrast, expression and imprinting of the translocated Cdkn1c, Slc22a1l and Tssc3 on chromosome 11 are affected, consistent with the hypothesis that elements regulating both expression and imprinting of these genes lie within or proximal to Kcnq1. These data support the proposal that chromosomal abnormalities, including translocations, within KCNQ1 that are associated with the human disease Beckwith-Wiedemann syndrome (BWS) may disrupt CDKN1C expression. These results underscore the importance of gene clustering for the proper regulation of imprinted genes.     

Chromatin profiling using targeted DNA adenine methyltransferase

Chromatin is the highly complex structure consisting of DNA and hundreds of associated proteins. Most chromatin proteins exert their regulatory and structural functions by binding to specific chromosomal loci. Knowledge of the identity of these in vivo target loci is essential for the understanding of the functions and mechanisms of action of chromatin proteins. We report here large-scale mapping of in vivo binding sites of chromatin proteins, using a novel approach based on a combination of targeted DNA methylation and microarray technology. We show that three distinct chromatin proteins in Drosophila melanogaster cells each associate with specific sets of genes. HP1 binds predominantly to pericentric genes and transposable elements. GAGA factor associates with euchromatic genes that are enriched in (GA)n motifs. A Drosophila homolog of Saccharomyces cerevisiae Sir2p is associated with several active genes and is excluded from heterochromatin. High-resolution, genome-wide maps of target loci of chromatin proteins ('chromatin profiles') provide new insights into chromatin structure and gene regulation.     

Clustering of housekeeping genes provides a unified model of gene order in the human genome

It is often supposed that, except for tandem duplicates, genes are randomly distributed throughout the human genome. However, recent analyses suggest that when all the genes expressed in a given tissue (notably placenta and skeletal muscle) are examined, these genes do not map to random locations but instead resolve to clusters. We have asked three questions: (i) is this clustering true for most tissues, or are these the exceptions; (ii) is any clustering simply the result of the expression of tandem duplicates and (iii) how, if at all, does this relate to the observed clustering of genes with high expression rates? We provide a unified model of gene clustering that explains the previous observations. We examined Serial Analysis of Gene Expression (SAGE) data for 14 tissues and found significant clustering, in each tissue, that persists even after the removal of tandem duplicates. We confirmed clustering by analysis of independent expressed-sequence tag (EST) data. We then tested the possibility that the human genome is organized into subregions, each specializing in genes needed in a given tissue. By comparing genes expressed in different tissues, we show that this is not the case: those genes that seem to be tissue-specific in their expression do not, as a rule, cluster. We report that genes that are expressed in most tissues (housekeeping genes) show strong clustering. In addition, we show that the apparent clustering of genes with high expression rates is a consequence of the clustering of housekeeping genes.