MC EMiNEM Maps the Interaction Landscape of the Mediator

The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors

Regionale Standards: Ausgabe 2013

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The RNA-bound proteome of MRSA reveals post-transcriptional roles for helix-turn-helix DNA-binding and Rossmann-fold proteins

RNA-binding proteins play key roles in controlling gene expression in many organisms, but relatively few have been identified and characterised in detail in Gram-positive bacteria. Here, we globally analyse RNA-binding proteins in methicillin-resistant Staphylococcus aureus (MRSA) using two complementary biochemical approaches. We identify hundreds of putative RNA-binding proteins, many containing unconventional RNA-binding domains such as Rossmann-fold domains. Remarkably, more than half of the proteins containing helix-turn-helix (HTH) domains, which are frequently found in prokaryotic transcription factors, bind RNA in vivo. In particular, the CcpA transcription factor, a master regulator of carbon metabolism, uses its HTH domain to bind hundreds of RNAs near intrinsic transcription terminators in vivo. We propose that CcpA, besides acting as a transcription factor, post-transcriptionally regulates the stability of many RNAs

Gene set enrichment analysis.

<p>A) Expression changes of the target genes of SKO1 across all experiments. Experiments correspond to rows; the respective Mediator subunit perturbations are indicated by the colored boxes to the left of the heat map (coloring is in accordance with the Mediator module structure in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi-1002568-g003" target="_blank">Fig. 3</a>). Target genes correspond to columns. If a target gene is attached to a Mediator subunit in the MC EMiNEM effects graph, this is indicated by a colored box on top of the respective column, using the same color code as for the experiments. Expression changes relative to wild type are color coded by the panel on the right. In the gene set enrichment analysis, SKO1 target genes were found enriched for upregulated genes attached to the Med10Med21 node in the MC EMiNEM effects graph. These genes lie to the left of the bold vertical line in the heat map. Briefly, our Mediator NEM model predicts that they should also change their expression in the Med19 and Med7C perturbations, which lie above the bold horizontal line. Ideally, the expression changes in the upper left corner defined by the two bold lines should be strong and consistent, while those in the remaining part should be weaker and less consistent. B) Same plot as A), for the target genes of SWI5. Since SWI5 targets are enriched for downregulated genes attached to Med7N, and Med7N is downstream of all other nodes in the signals graph, we expect consistent expression changes of the Med7N attached genes across all perturbations.</p

Effects graph inferred from the Mediator data.

<p>Shown are the log-odds ratios which serve as MC EMiNEM's input. Genes that are likely to change in a given condition are depicted in red,and they are blue otherwise. Color saturation indicates the absolute value of the log-odds ratio (cf. Fig. S4.3 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi.1002568.s004" target="_blank">Text S1</a>). Rows correspond to Mediator perturbation experiments, columns correspond to genes, sorted according to their attachment to Mediator subunits. Mediator subunits are colored as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi-1002568-g003" target="_blank">Fig. 3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi-1002568-g005" target="_blank">Fig. 5</a>.</p

Prediction quality and influence of the Empirical Bayes procedure.

<p>(A) Prediction quality. Comparison of the sensitivity of MC EMiNEM and four alternative methods for four different noise levels (top) and four different signals graph sizes (bottom). The sensitivity is depicted on the y-axis, each frame corresponds to one parameter setting. Top: For a signals graph of 11 nodes, noisy data was generated such that for an optimal test with a type-I error (-level) of 5%, a type II error (-level) of , and would be achieved, respectively. Bottom: For a noise level corresponding to an error level of (, ), signals graph sizes of are investigated. We expect our application to range within the four central scenarios. The comparisons of sensitivities is a fair comparison of the prediction qualities since the specificities for all methods and parameter settings are located (see also Fig. S3.7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi.1002568.s004" target="_blank">Text S1</a>). (B) Influence of the Empirical Bayes procedure. Here, for the standard setting and (, ). The x-axis shows the calculated marginal posterior values centered at (indicated by the dashed vertical line), on the y-axis the frequency is displayed. In the table, the percentages of signals graphs scoring higher than are provided, as well as the -distances (relative to the maximum).</p

Example NEM.

<p>, . Shaded matrix fields correspond to an expression change of effect gene upon perturbation of signal , white fields indicate no change in expression. The edges and cause an effect in genes directly attached to signal and respectively, when is perturbed.</p