Modeling DNA-binding of Escherichia coli sigma70 exhibits a characteristic energy landscape around strong promoters

Energetic funnel facilitates facilitated diffusion

Transcription factors (TFs) are able to associate to their binding sites on DNA faster than the physical limit posed by diffusion. Such high association rates can be achieved by alternating between three-dimensional diffusion and one-dimensional sliding along the DNA chain, a mechanism-dubbed facilitated diffusion. By studying a collection of TF binding sites of Escherichia coli from the RegulonDB database and of Bacillus subtilis from DBTBS, we reveal a funnel in the binding energy landscape around the target sequences. We show that such a funnel is linked to the presence of gradients of AT in the base composition of the DNA region around the binding sites. An extensive computational study of the stochastic sliding process along the energetic landscapes obtained from the database shows that the funnel can significantly enhance the probability of TFs to find their target sequences when sliding in their proximity. We demonstrate that this enhancement leads to a speed-up of the association process.

Computational models for large-scale simulations of facilitated diffusion

The binding of site-specific transcription factors to their genomic target sites is a key step in gene regulation. While the genome is huge, transcription factors belong to the least abundant protein classes in the cell. It is therefore fascinating how short the time frame is that they require to home in on their target sites. The underlying search mechanism is called facilitated diffusion and assumes a combination of three-dimensional diffusion in the space around the DNA combined with one-dimensional random walk on it. In this review, we present the current understanding of the facilitated diffusion mechanism and identify questions that lack a clear or detailed answer. One way to investigate these questions is through stochastic simulation and, in this manuscript, we support the idea that such simulations are able to address them. Finally, we review which biological parameters need to be included in such computational models in order to obtain a detailed representation of the actual process.

System size reduction in stochastic simulations of the facilitated diffusion mechanism

Background

Site-specific Transcription Factors (TFs) are proteins that bind to specific sites on the DNA and control the activity of a target gene by enhancing or decreasing the rate at which the gene is transcribed by RNA polymerase. The process by which TF molecules locate their target sites is a key component of transcriptional regulation. Therefore it is essential to gain insight into the mechanisms by which TFs search for the target sites.

Research in this area uses experimental and analytical approaches, but also stochastic simulations of the search process. Previous work based on stochastic simulations focussed only on short sequences, primarily for reasons of technical feasibility. Many of these studies had to disregard possible biases introduced by reducing a genome-wide system to a smaller subsystem. In particular, we identified crucial parameters that require adjustment, which were not adequately changed in these previous studies.

Results

We investigated several methods that adequately adapt the parameters of stochastic simulations of the facilitated diffusion, when the full sequence space is reduced to smaller regions of interest. We found two methods that scale the system accordingly: the copy number model and the association rate model. We systematically compared the results produced by simulations of the subsystem with respect to the original system. Our results confirmed that the copy number model is adequate only for high abundance TFs, while for low abundance TFs the association rate model is the only one that reproduces with high accuracy the results of the full system.

Conclusions

We propose a strategy to reduce the size of the system that adequately adapts important parameters to capture the behaviour of the full system. This enables correct simulations of a smaller sequence space (which can be as small as 100 Kbp) and, thus, provides independence from computationally intensive genome-wide simulations of the facilitated diffusion mechanism.

A comprehensive computational model of facilitated diffusion in prokaryotes

MOTIVATION

Gene activity is mediated by site-specific transcription factors (TFs). Their binding to defined regions in the genome determines the rate at which their target genes are transcribed.

RESULTS

We present a comprehensive computational model of the search process of TF for their genomic target site(s). The computational model considers: the DNA sequence, various TF species and the interaction of the individual molecules with the DNA or between themselves. We also demonstrate a systematic approach how to parametrize the system using available experimental data.

Evidence that purifying selection acts on promoter sequences

We tested whether functionally important sites in bacterial, yeast, and animal promoters are more conserved than their neighbors. We found that substitutions are predominantly seen in less important sites and that those that occurred tended to have less impact on gene expression than possible alternatives. These results suggest that purifying selection operates on promoter sequences.

Upstream curved sequences in E. coli are related to the regulation of transcription initiation

The advancement in Escherichia coli genome research has made the information regarding transcription start sites of many genes available. A study relying on the availability of transcription start locations was performed. The first question addressed was what an average DNA curvature profile upstream of genes would look like when these genes are aligned by transcription start sites in comparison to alignment by translation start sites. Since it was hypothesized that curvature plays a role in transcription regulation, the expectation was that curvature measurements relative to transcription starts, rather than translation, should strengthen the signal. Our study justified this expectation. The second question aimed to clarify the relation between DNA curvature and promoter strength. Through clustering based on DNA curvature profiles along promoter regions, a strong positive correlation between the promoter strength and the curved DNA was found. The third question dealt with dinucleotide periodicity in E. coli to see whether a periodicity pattern specific to promoter regions exists. Such unknown pattern might shed new light on transcription regulation mechanisms in E. coli. A sequence periodicity of about 11 bp is characteristic to the whole E. coli genome, and is especially well-expressed in intergenic regions. Here it was shown that regions of the size of about 100-150 bp centered 70-100 bp upstream to transcription starts carry hidden periodicity with a period of about 10.3 bp.

Modeling promoter search by E. coli RNA polymerase: one-dimensional diffusion in a sequence-dependent energy landscape

We present a biophysical model of promoter search by Escherichia coli RNA polymerase. We use an unconventional weight matrix derived from promoter strength data to extract the energy landscape common to a large set of known promoters. This exhibits a continuous strengthening of the binding energy when approaching the transcription start site from either side. During promoter search, the RNA polymerase slides along the DNA double helix (one-dimensional diffusion) after randomly binding to it. We discuss the possibility that the sliding has a sequence-dependent component, which implies that the energy landscape influences the movement with respect to speed, direction and efficiency. Based on this assumption, we relate the obtained energy landscape around the promoters to the one-dimensional diffusion of the RNA polymerase. Our analytical results suggest that the sequence-dependent random walk slows down and gets directed upon entering a region of 500 bp around the transcription start site, which significantly increases the efficiency of promoter search. These results may explain how the RNA polymerase is able to find the promoter in biologically relevant times out of a vast excess of non-target sites. Moreover, they provide evidence for a sequence-dependent component of one-dimensional diffusion.

Organization and evolution of the biological response to singlet oxygen stress

The appearance of atmospheric oxygen from photosynthetic activity led to the evolution of aerobic respiration and responses to the resulting reactive oxygen species. In Rhodobacter sphaeroides, a photosynthetic alpha-proteobacterium, a transcriptional response to the reactive oxygen species singlet oxygen ((1)O(2)) is controlled by the group IV sigma factor sigma(E) and the anti-sigma factor ChrR. In this study, we integrated various large datasets to identify genes within the (1)O(2) stress response that contain sigma(E)-dependent promoters both within R. sphaeroides and across the bacterial phylogeny. Transcript pattern clustering and a sigma(E)-binding sequence model were used to predict candidate promoters that respond to (1)O(2) stress in R. sphaeroides. These candidate promoters were experimentally validated to nine R. sphaeroides sigma(E)-dependent promoters that control the transcription of 15 (1)O(2)-activated genes. Knowledge of the R. sphaeroides response to (1)O(2) and its regulator sigma(E)-ChrR was combined with large-scale phylogenetic and sequence analyses to predict the existence of a core set of approximately eight conserved sigma(E)-dependent genes in alpha-proteobacteria and gamma-proteobacteria. The bacteria predicted to contain this conserved response to (1)O(2) include photosynthetic species, as well as free-living and symbiotic/pathogenic nonphotosynthetic species. Our analysis also predicts that the response to (1)O(2) evolved within the time frame of the accumulation of atmospheric molecular oxygen on this planet.