Computational Modelling of Genome-Side Transcription Assembly Networks Using a Fluidics Analogy

Understanding how a myriad of transcription regulators work to modulate mRNA output at thousands of genes remains a fundamental challenge in molecular biology. Here we develop a computational tool to aid in assessing the plausibility of gene regulatory models derived from genome-wide expression profiling of cells mutant for transcription regulators. mRNA output is modelled as fluid flow in a pipe lattice, with assembly of the transcription machinery represented by the effect of valves. Transcriptional regulators are represented as external pressure heads that determine flow rate. Modelling mutations in regulatory proteins is achieved by adjusting valves' on/off settings. The topology of the lattice is designed by the experimentalist to resemble the expected interconnection between the modelled agents and their influence on mRNA expression. Users can compare multiple lattice configurations so as to find the one that minimizes the error with experimental data. This computational model provides a means to test the plausibility of transcription regulation models derived from large genomic data sets

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

The Yin and Yang of Yeast Transcription: Elements of a Global Feedback System between Metabolism and Chromatin

When grown in continuous culture, budding yeast cells tend to synchronize their respiratory activity to form a stable oscillation that percolates throughout cellular physiology and involves the majority of the protein-coding transcriptome. Oscillations in batch culture and at single cell level support the idea that these dynamics constitute a general growth principle. The precise molecular mechanisms and biological functions of the oscillation remain elusive. Fourier analysis of transcriptome time series datasets from two different oscillation periods (0.7 h and 5 h) reveals seven distinct co-expression clusters common to both systems (34% of all yeast ORF), which consolidate into two superclusters when correlated with a compilation of 1,327 unrelated transcriptome datasets. These superclusters encode for cell growth and anabolism during the phase of high, and mitochondrial growth, catabolism and stress response during the phase of low oxygen uptake. The promoters of each cluster are characterized by different nucleotide contents, promoter nucleosome configurations, and dependence on ATP-dependent nucleosome remodeling complexes. We show that the ATP:ADP ratio oscillates, compatible with alternating metabolic activity of the two superclusters and differential feedback on their transcription via activating (RSC) and repressive (Isw2) types of promoter structure remodeling. We propose a novel feedback mechanism, where the energetic state of the cell, reflected in the ATP:ADP ratio, gates the transcription of large, but functionally coherent groups of genes via differential effects of ATP-dependent nucleosome remodeling machineries. Besides providing a mechanistic hypothesis for the delayed negative feedback that results in the oscillatory phenotype, this mechanism may underpin the continuous adaptation of growth to environmental conditions

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/

Redundancy and the Evolution of Cis-Regulatory Element Multiplicity

The promoter regions of many genes contain multiple binding sites for the same transcription factor (TF). One possibility is that this multiplicity evolved through transitional forms showing redundant cis-regulation. To evaluate this hypothesis, we must disentangle the relative contributions of different evolutionary mechanisms to the evolution of binding site multiplicity. Here, we attempt to do this using a model of binding site evolution. Our model considers binding sequences and their interactions with TFs explicitly, and allows us to cast the evolution of gene networks into a neutral network framework. We then test some of the model's predictions using data from yeast. Analysis of the model suggested three candidate nonadaptive processes favoring the evolution of cis-regulatory element redundancy and multiplicity: neutral evolution in long promoters, recombination and TF promiscuity. We find that recombination rate is positively associated with binding site multiplicity in yeast. Our model also indicated that weak direct selection for multiplicity (partial redundancy) can play a major role in organisms with large populations. Our data suggest that selection for changes in gene expression level may have contributed to the evolution of multiple binding sites in yeast. We conclude that the evolution of cis-regulatory element redundancy and multiplicity is impacted by many aspects of the biology of an organism: both adaptive and nonadaptive processes, both changes in cis to binding sites and in trans to the TFs that interact with them, both the functional setting of the promoter and the population genetic context of the individuals carrying them

Variations in Stress Sensitivity and Genomic Expression in Diverse S. cerevisiae Isolates

Interactions between an organism and its environment can significantly influence phenotypic evolution. A first step toward understanding this process is to characterize phenotypic diversity within and between populations. We explored the phenotypic variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains collected from diverse environments. We measured the sensitivity of 52 strains to 14 environmental conditions, compared genomic expression in 18 strains, and identified gene copy-number variations in six of these isolates. Our results demonstrate a large degree of phenotypic variation in stress sensitivity and gene expression. Analysis of these datasets reveals relationships between strains from similar niches, suggests common and unique features of yeast habitats, and implicates genes whose variable expression is linked to stress resistance. Using a simple metric to suggest cases of selection, we found that strains collected from oak exudates are phenotypically more similar than expected based on their genetic diversity, while sake and vineyard isolates display more diverse phenotypes than expected under a neutral model. We also show that the laboratory strain S288c is phenotypically distinct from all of the other strains studied here, in terms of stress sensitivity, gene expression, Ty copy number, mitochondrial content, and gene-dosage control. These results highlight the value of understanding the genetic basis of phenotypic variation and raise caution about using laboratory strains for comparative genomics

Characterization of Transcription from TATA-Less Promoters: Identification of a New Core Promoter Element XCPE2 and Analysis of Factor Requirements

More than 80% of mammalian protein-coding genes are driven by TATA-less promoters which often show multiple transcriptional start sites (TSSs). However, little is known about the core promoter DNA sequences or mechanisms of transcriptional initiation for this class of promoters.Here we identify a new core promoter element XCPE2 (X core promoter element 2) (consensus sequence: A/C/G-C-C/T-C-G/A-T-T-G/A-C-C/A(+1)-C/T) that can direct specific transcription from the second TSS of hepatitis B virus X gene mRNA. XCPE2 sequences can also be found in human promoter regions and typically appear to drive one of the start sites within multiple TSS-containing TATA-less promoters. To gain insight into mechanisms of transcriptional initiation from this class of promoters, we examined requirements of several general transcription factors by in vitro transcription experiments using immunodepleted nuclear extracts and purified factors. Our results show that XCPE2-driven transcription uses at least TFIIB, either TFIID or free TBP, RNA polymerase II (RNA pol II) and the MED26-containing mediator complex but not Gcn5. Therefore, XCPE2-driven transcription can be carried out by a mechanism which differs from previously described TAF-dependent mechanisms for initiator (Inr)- or downstream promoter element (DPE)-containing promoters, the TBP- and SAGA (Spt-Ada-Gcn5-acetyltransferase)-dependent mechanism for yeast TATA-containing promoters, or the TFTC (TBP-free-TAF-containing complex)-dependent mechanism for certain Inr-containing TATA-less promoters. EMSA assays using XCPE2 promoter and purified factors further suggest that XCPE2 promoter recognition requires a set of factors different from those for TATA box, Inr, or DPE promoter recognition.We identified a new core promoter element XCPE2 that are found in multiple TSS-containing TATA-less promoters. Mechanisms of promoter recognition and transcriptional initiation for XCPE2-driven promoters appear different from previously shown mechanisms for classical promoters that show single "focused" TSSs. Our studies provide insight into novel mechanisms of RNA Pol II transcription from multiple TSS-containing TATA-less promoters

DNA origami-based single-molecule forcespectroscopy elucidates RNA Polymerase IIIpre-initiation complex stability

The TATA-binding protein (TBP) and a transcription factor (TF) IIB-like factor are important constituents of all eukaryotic initiation complexes. The reason for the emergence and strict requirement of the additional initiation factor Bdp1 in the RNA polymerase (RNAP) III system, however, remained elusive. A poorly studied aspect in this context is the effect of DNA strain arising from DNA compaction and transcriptional activity on initiation complex formation. We made use of a DNA origami-based force clamp to follow the assembly of human initiation complexes in the RNAP II and RNAP III systems at the single-molecule level under piconewton forces. We demonstrate that TBP-DNA complexes are force-sensitive and TFIIB is sufficient to stabilise TBP on a strained promoter. In contrast, Bdp1 is the pivotal component that ensures stable anchoring of initiation factors, and thus the polymerase itself, in the RNAP III system. Thereby, we offer an explanation for the crucial role of Bdp1 for the high transcriptional output of RNAP III

Expression variability of co-regulated genes differentiates Saccharomyces cerevisiae strains

Background: Saccharomyces cerevisiae (Baker’s yeast) is found in diverse ecological niches and is characterized by high adaptive potential under challenging environments. In spite of recent advances on the study of yeast genome diversity, little is known about the underlying gene expression plasticity. In order to shed new light onto this biological question, we have compared transcriptome profiles of five environmental isolates, clinical and laboratorial strains at different time points of fermentation in synthetic must medium, during exponential and stationary growth phases. Results: Our data unveiled diversity in both intensity and timing of gene expression. Genes involved in glucose metabolism and in the stress response elicited during fermentation were among the most variable. This gene expression diversity increased at the onset of stationary phase (diauxic shift). Environmental isolates showed lower average transcript abundance of genes involved in the stress response, assimilation of nitrogen and vitamins, and sulphur metabolism, than other strains. Nitrogen metabolism genes showed significant variation in expression among the environmental isolates. Conclusions: Wild type yeast strains respond differentially to the stress imposed by nutrient depletion, ethanol accumulation and cell density increase, during fermentation of glucose in synthetic must medium. Our results support previous data showing that gene expression variability is a source of phenotypic diversity among closely related organisms.Fundação para a Ciência e TecnologiaThe authors wish to thank Adega Cooperativa da Bairrada, Cantanhede, Portugal, for providing the commercial strains

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

Abstract Background A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. Results We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. Conclusions Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability