T-profiler: scoring the activity of predefined groups of genes using gene expression data

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. Here we describe T-profiler, a tool that uses the t-test to score changes in the average activity of predefined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif or location on the same chromosome. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can upload their microarray data for analysis on the web at

Inferring Condition-Specific Modulation of Transcription Factor Activity in Yeast through Regulon-Based Analysis of Genomewide Expression

Background: A key goal of systems biology is to understand how genomewide mRNA expression levels are controlled by transcription factors (TFs) in a condition-specific fashion. TF activity is frequently modulated at the post-translational level through ligand binding, covalent modification, or changes in sub-cellular localization. In this paper, we demonstrate how prior information about regulatory network connectivity can be exploited to infer condition-specific TF activity as a hidden variable from the genomewide mRNA expression pattern in the yeast Saccharomyces cerevisiae. Methodology/Principal Findings: We first validate experimentally that by scoring differential expression at the level of gene sets or "regulons" comprised of the putative targets of a TF, we can accurately predict modulation of TF activity at the post-translational level. Next, we create an interactive database of inferred activities for a large number of TFs across a large number of experimental conditions in S. cerevisiae. This allows us to perform TF-centric analysis of the yeast regulatory network. Conclusions/Significance: We analyze the degree to which the mRNA expression level of each TF is predictive of its regulatory activity. We also organize TFs into "co-modulation networks" based on their inferred activity profile across conditions, and find that this reveals functional and mechanistic relationships. Finally, we present evidence that the PAC and rRPE motifs antagonize TBP-dependent regulation, and function as core promoter elements governed by the transcription regulator NC2. Regulon-based monitoring of TF activity modulation is a powerful tool for analyzing regulatory network function that should be applicable in other organisms. Tools and results are available online at http://bussemakerlab.org/RegulonProfiler/

Dissecting complex transcriptional responses using pathway-level scores based on prior information-0

<p><b>Copyright information:</b></p><p>Taken from "Dissecting complex transcriptional responses using pathway-level scores based on prior information"</p><p>http://www.biomedcentral.com/1471-2105/8/S6/S6</p><p>BMC Bioinformatics 2007;8(Suppl 6):S6-S6.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC1995543.</p><p></p>in as measured by Hughes et al. [27]. The two-sample t-test reveals that the mean expression level of genes in the GO category "ergosterol biosynthesis" is significantly higher than expected (dotted line; = 7.4; = 1.1·10). Fisher's exact test can be used to score over-representation of the same GO category in the set of most induced genes. However, this requires one to first define a threshold for the expression fold-change of individual genes. The solid line shows how the P-value from Fisher's exact test depends on this threshold

Zinc Stabilizes the SecB Binding Site of SecA

The molecular chaperone SecB targets preproteins to SecA at the translocation sites in the cytoplasmic membrane of Escherichia coli. SecA recognizes SecB via its carboxyl-terminal 22 aminoacyl residues, a highly conserved domain that contains 3 cysteines and 1 histidine residue that could potentially be involved in the coordination of a metal ion. Treatment of SecA with a zinc chelator resulted in a loss of the stimulatory effect of SecB on the SecA translocation ATPase activity, while the activity could be restored by the addition of ZnCl2. Interaction of SecB with the SecB binding domain of SecA is disrupted by chelators of divalent cations, and could be restored by the addition of Cu2+ or Zn2+. Atomic absorption and electrospray mass spectrometry revealed the presence of one zinc atom per monomeric carboxyl terminus of SecA. It is concluded that the SecB binding domain of SecA is stabilized by a zinc ion that promotes the functional binding of SecB to SecA.