The Patrocles database (http://www.patrocles.org/) compiles DNA sequence polymorphisms (DSPs) that are predicted to perturb miRNA-mediated gene regulation. Distinctive features include: (i) the coverage of seven vertebrate species in its present release, aiming for more when information becomes available, (ii) the coverage of the three compartments involved in the silencing process (i.e. targets, miRNA precursors and silencing machinery), (iii) contextual information that enables users to prioritize candidate ‘Patrocles DSPs’, including graphical information on miRNA-target coexpression and eQTL effect of genotype on target expression levels, (iv) the inclusion of Copy Number Variants and eQTL information that affect miRNA precursors as well as genes encoding components of the silencing machinery and (v) a tool (Patrocles finder) that allows the user to determine whether her favorite DSP may perturb miRNA-mediated gene regulation of custom target sequences. To support the biological relevance of Patrocles' content, we searched for signatures of selection acting on ‘Patrocles single nucleotide polymorphisms (pSNPs)’ in human and mice. As expected, we found a strong signature of purifying selection against not only SNPs that destroy conserved target sites but also against SNPs that create novel, illegitimate target sites, which is reminiscent of the Texel mutation in sheep.

In the post-genomic area, the prediction of transcription factor regulons by position weight matrix-based programmes is a powerful approach to decipher biological pathways and to modelize regulatory networks in bacteria. The main difficulty once a regulon prediction is available is to estimate its reliability prior to start expensive experimental validations and therefore trying to find a way how to identify true positive hits from an endless list of potential target genes of a regulatory protein. Here we introduce PREDetector (Prokaryotic Regulatory Elements Detector), a tool developed for predicting regulons of DNA-binding proteins in bacterial genomes that, beside the automatic prediction, scoring and positioning of potential binding sites and their respective target genes in annotated bacterial genomes, it also provides an easy way to estimate the thresholds where to find reliable possible new target genes. PREDetector can be downloaded freely at http://www.montefiore.ulg.ac.be/~hiard/PreDetector/PreDetector.php. Ó 2007 Published by Elsevier Inc.Keywords: Regulon prediction; Transcriptional regulation; Regulatory networks; Position weight matrix; DNA-binding motif Genome sequences are a mine of information to estimate the natural predisposition of a microorganism to face and respond to particular ecological niches or can be regarded as valuable resources for interrogation to specific biotechnological ends. However, beyond these basic genetic data, the assessment of the real metabolic and physiological potentialities requires intensive investigations on how the living cell senses environmental signals and transmits messages to regulatory authorities that control genes expression. The characterisation of a regulon, i.e. the transcription factor(s) (TF), cis-acting element(s), ligand affecting the DNA-binding ability, the set of target genes and the controlled biological processes, is crucial to understand all living organisms. Deciphering the cis-trans relationships that weave a regulatory network is considered as the first step towards this aim. Once regulatory DNA sequences have been demonstrated as targets for a specific transcription factor, their conserved signature is generally described by position weight matrices (PWMs) which specify the frequency distribution of nucleotides at each position of the TF cis-acting elements. Several PWMs based web tools (such as Target Explorer

No abstract

The callipyge mutation (CLPG) is an A to G transition that affects a muscle-specific long-range control element located in the middle of the 90-kb DLK1-GTL2 intergenic (IG) region. It causes ectopic expression of a 327-kb cluster of imprinted genes in skeletal muscle, resulting in the callipyge muscular hypertrophy and its non-Mendelian inheritance pattern known as polar overdominance. We herein demonstrate that the CLPG mutation alters the muscular epigenotype of the DLK1-GTL2 IG region in cis, including hypomethylation, acquisition of novel DNase-I hypersentivite sites, and, most strikingly, strongly enhanced bidirectional, longrange IG transcription. The callipyge phenotype thus emerges as a unique model to study the functional significance of IG transcription, which recently has proven to be a widespread, yet elusive, feature of the mammalian genome.DNA methylation ͉ DNase-I hypersensitivity ͉ intergenic region ͉ noncoding RNA T he callipyge phenotype is an inherited muscular hypertrophy of sheep. It is characterized by polar overdominance, an unusual mode of inheritance in which only heterozygotes having received the CLPG mutation from their sire express the phenotype (1). The CLPG mutation is an A-to-G transition in a conserved dodecamer motif located in the 90-kb intergenic (IG) region separating the imprinted DLK1 and GTL2 genes on sheep chromosome 18 (refs. 2 and 3; Fig. 1). This motif was assumed to be part of a muscle-specific locus control region (LCR), because the CLPG mutation causes ectopic expression of a core cluster of neighboring genes in postnatal skeletal muscle, a tissue in which these genes are normally silenced (6, 7). Genes whose expression is affected by the CLPG mutation include (i) the paternally expressed protein encoding DLK1 and PEG11 genes, located, respectively, 64 kb proximally and 88 kb distally from the CLPG mutation, and (ii) the maternally expressed noncoding RNA genes GTL2, antiPEG11, MEG8, and MIRG, located between 33 and 262 kb distally from the CLPG mutation, as well as their multiple C͞D small nucleolar RNA and microRNA (miRNA) guests (8, 9). With the exception of PEG11, all these genes are transcribed toward the telomere. The effect of the CLPG mutation is cis-restricted and subordinate to imprinting control because it does not perturb the monoallelic expression of the target genes (6).It was recently shown that the callipyge phenotype can be caused by ectopic expression of DLK1 protein in skeletal muscle as observed in ϩ͞C Pat individuals (10). The lack of phenotypic expression in C͞C animals is postulated to be due to translational inhibition of padumnal DLK1 transcripts by noncoding madumnal transcripts (11). A direct role for miRNAs in this trans effect is suggested by the demonstration of RNA interference-mediated degradation of padumnal PEG11 transcripts by miRNAs processed from madumnal antiPEG11 transcripts (12).How the CLPG mutation operates such profound, tissuespecific inf luence on the expression of genes, which can be as far as 262 kb away, remains unknown...

In the era that huge numbers of microbial genomes are being released in the databases, it becomes increasingly important to rapidly mine genes as well as predict the regulatory networks that control their expression. To this end, we have developed an improved and online version of the PREDetector software aimed at identifying putative transcription factor-binding sites (TFBS) in bacterial genomes.The original philosophy of PREDetector 1.0 is maintained, i.e. to allow users to freely fix the DNA-motif screening parameters, and to provide a statistical means to estimate the reliability of the prediction output. This new version offers an interactive table as well as graphics to dynamically alter the main screening parameters with automatic update of the list of identified putative TFBS. PREDetector 2.0 also has the following additional options: (i) access to genome sequences from different databases, (ii) access to weight matrices from public repositories, (iii) visualization of the predicted hits in their genomic context, (iv) grouping of hits identified in the same upstream region, (v) possibility to store the performed jobs, and (vi) automated export of the results in various formats. PREDetector 2.0 is available at