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Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-a-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.

Small non-coding RNAs represent RNA species that are not translated to proteins, but which have diverse and broad functional activities in physiological and pathophysiological states. The knowledge of these small RNAs is rapidly expanding in part through the use of massive parallel (deep) sequencing efforts. We present here the first deep sequencing of small RNomes in subcellular compartments with particular emphasis on small RNAs (sRNA) associated with the nucleolus. The vast majority of the cellular, cytoplasmic and nuclear sRNAs were identified as miRNAs. In contrast, the nucleolar sRNAs had a unique size distribution consisting of 19-20 and 25 nt RNAs, which were predominantly composed of small snoRNA-derived box C/D RNAs (termed as sdRNA). Sequences from 47 sdRNAs were identified, which mapped to both 5' and 3' ends of the snoRNAs, and retained conserved box C or D motifs. SdRNA reads mapping to SNORD44 comprised 74% of all nucleolar sdRNAs, and were confirmed by Northern blotting as comprising both 20 and 25 nt RNAs. A novel 120 nt SNORD44 form was also identified. The expression of the SNORD44 sdRNA and 120 nt form was independent of Dicer/Drosha-mediated processing pathways but was dependent on the box C/D snoRNP proteins/sno-ribonucleoproteins fibrillarin and NOP58. The 120 nt SNORD44-derived RNA bound to fibrillarin suggesting that C/D sno-ribonucleoproteins are involved in regulating the stability or processing of SNORD44. This study reveals sRNA cell-compartment specific expression and the distinctive unique composition of the nucleolar sRNAs.

RNA polymerase II synthesizes a diverse set of transcripts including both protein-coding and non-coding RNAs. One major difference between these two classes of transcripts is the mechanism of termination. Messenger RNA transcripts terminate downstream of the coding region in a process that is coupled to cleavage and polyadenylation reactions. Non-coding transcripts like Saccharomyces cerevisiae snoRNAs terminate in a process that requires the RNA–binding proteins Nrd1, Nab3, and Sen1. We report here the transcriptome-wide distribution of these termination factors. These data sets derived from in vivo protein–RNA cross-linking provide high-resolution definition of non-poly(A) terminators, identify novel genes regulated by attenuation of nascent transcripts close to the promoter, and demonstrate the widespread occurrence of Nrd1-bound 3′ antisense transcripts on genes that are poorly expressed. In addition, we show that Sen1 does not cross-link efficiently to many expected non-coding RNAs but does cross-link to the 3′ end of most pre–mRNA transcripts, suggesting an extensive role in mRNA 3′ end formation and/or termination., Author Summary Transcription in eukaryotes is widespread including both protein-coding transcripts and an increasing number of non-coding RNAs. Here we present the results of transcriptome-wide mapping of a set of yeast RNA–binding proteins that control expression of some protein-coding genes and a number of novel non-coding RNAs. The yeast Nrd1-Nab3-Sen1 pathway is required for termination and exosome-mediated processing of non-coding RNA polymerase II transcripts. Our data show that these components bind unexpected targets including a large number of antisense transcripts originating from the 3′ end of genes that are poorly expressed in the sense direction. We also show that Sen1 helicase, involved in termination of non-coding RNAs, is also present at the 3′ end of mRNAs, suggesting a more fundamental role in transcription termination. Mis-regulation of transcription is the underlying cause of many disease states. For example, mutation of the human Sen1 gene, senataxin, causes a range of neurodegenerative disorders. Understanding the roles of yeast RNA–binding proteins in controlling termination of coding and non-coding RNAs will be useful in deciphering the mechanism of these proteins in human cells.

Background Infection with multiple CMV strains is common in immunocompromised hosts, but its occurrence in normal hosts has not been well-studied. Methods We analyzed CMV strains longitudinally in women who acquired CMV while enrolled in a CMV glycoprotein B (gB) vaccine trial. Sequencing of four variable genes was performed in samples collected from seroconversion and up to 34 months thereafter. Results 199 cultured isolates from 53 women and 65 original fluids from a subset of 19 women were sequenced. 51 women were infected with one strain each without evidence for genetic drift; only two women shed multiple strains. Genetic variability among strains increased with the number of sequenced genetic loci. Nevertheless, 13 of 53 women proved to be infected with an identical CMV strain based on sequencing at all four variable genes. CMV vaccine did not alter the degree of genetic diversity amongst strains. Conclusions Primary CMV infection in healthy women nearly always involves shedding of one strain that remains stable over time. Immunization with CMVgB-1 vaccine strain is not selective against specific strains. Although 75% of women harbored their unique strain, or a strain shared with only one other woman, 25% shared a single common strain, suggesting that this predominant strain with a particular combination of genetic loci is advantageous in this large urban area.