Transcriptome surveillance by selective termination of noncoding RNA synthesis.

Pervasive transcription of eukaryotic genomes stems to a large extent from bidirectional promoters that synthesize mRNA and divergent noncoding RNA (ncRNA). Here, we show that ncRNA transcription in the yeast S. cerevisiae is globally restricted by early termination that relies on the essential RNA-binding factor Nrd1. Depletion of Nrd1 from the nucleus results in 1,526 Nrd1-unterminated transcripts (NUTs) that originate from nucleosome-depleted regions (NDRs) and can deregulate mRNA synthesis by antisense repression and transcription interference. Transcriptome-wide Nrd1-binding maps reveal divergent NUTs at most promoters and antisense NUTs in most 3' regions of genes. Nrd1 and its partner Nab3 preferentially bind RNA motifs that are depleted in mRNAs and enriched in ncRNAs and some mRNAs whose synthesis is controlled by transcription attenuation. These results define a global mechanism for transcriptome surveillance that selectively terminates ncRNA synthesis to provide promoter directionality and to suppress antisense transcription.

Modulations of DNA Contacts by Linker Histones and Post-translational Modifications Determine the Mobility and Modifiability of Nucleosomal H3 Tails.

Post-translational histone modifications and linker histone incorporation regulate chromatin structure and genome activity. How these systems interface on a molecular level is unclear. Using biochemistry and NMR spectroscopy, we deduced mechanistic insights into the modification behavior of N-terminal histone H3 tails in different nucleosomal contexts. We find that linker histones generally inhibit modifications of different H3 sites and reduce H3 tail dynamics in nucleosomes. These effects are caused by modulations of electrostatic interactions of H3 tails with linker DNA and largely depend on the C-terminal domains of linker histones. In agreement, linker histone occupancy and H3 tail modifications segregate on a genome-wide level. Charge-modulating modifications such as phosphorylation and acetylation weaken transient H3 tail-linker DNA interactions, increase H3 tail dynamics, and, concomitantly, enhance general modifiability. We propose that alterations of H3 tail-linker DNA interactions by linker histones and charge-modulating modifications execute basal control mechanisms of chromatin function.

Large-scale analysis of Drosophila core promoter function using synthetic promoters.

The core promoter plays a central role in setting metazoan gene expression levels, but how exactly it "computes" expression remains poorly understood. To dissect its function, we carried out a comprehensive structure-function analysis in Drosophila. First, we performed a genome-wide bioinformatic analysis, providing an improved picture of the sequence motifs architecture. We then measured synthetic promoters' activities of ~3,000 mutational variants with and without an external stimulus (hormonal activation), at large scale and with high accuracy using robotics and a dual luciferase reporter assay. We observed a strong impact on activity of the different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, nucleosome positioning, and flanking sequences. A linear combination of the individual motif features largely accounts for the combinatorial effects on core promoter activity. These findings shed new light on the quantitative assessment of gene expression in metazoans.

The BaMM web server for de-novo motif discovery and regulatory sequence analysis.

The BaMM web server offers four tools: (i) de-novo discovery of enriched motifs in a set of nucleotide sequences, (ii) scanning a set of nucleotide sequences with motifs to find motif occurrences, (iii) searching with an input motif for similar motifs in our BaMM database with motifs for >1000 transcription factors, trained from the GTRD ChIP-seq database and (iv) browsing and keyword searching the motif database. In contrast to most other servers, we represent sequence motifs not by position weight matrices (PWMs) but by Bayesian Markov Models (BaMMs) of order 4, which we showed previously to perform substantially better in ROC analyses than PWMs or first order models. To address the inadequacy of P- and E-values as measures of motif quality, we introduce the AvRec score, the average recall over the TP-to-FP ratio between 1 and 100. The BaMM server is freely accessible without registration at https://bammmotif.mpibpc.mpg.de.

Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.

Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact.