Birth,Death, and Diversification of Mobile Promoters in Prokaryotes

A previous study of prokaryotic genomes identified large reservoirs of putative mobile promoters (PMPs), that is, homologous promoter sequences associated with nonhomologous coding sequences. Here we extend this data set to identify the full complement of mobile promoters in sequenced prokaryotic genomes. The expanded search identifies nearly 40,000 PMP sequences, 90% of which occur in noncoding regions of the genome. To gain further insight from this data set, we develop a birth–death–diversification model for mobile genetic elements subject to sequence diversification; applying the model to PMPs we are able to quantify the relative importance of duplication, loss, horizontal gene transfer (HGT), and diversification to the maintenance of the PMP reservoir. The model predicts low rates of HGT relative to the duplication and loss of PMP copies, rapid dynamics of PMP families, and a pool of PMPs that exist as a single copy in a genome at any given time, despite their mobility. We report evidence of these “singletons” at high frequencies in prokaryotic genomes. We also demonstrate that including selection, either for or against PMPs, was not necessary to describe the observed data.     

Investigating extended regulatory regions of genomic DNA sequences.

MOTIVATION: Despite the growing volume of data on primary nucleotide sequences, the regulatory regions remain a major puzzle with regard to their function. Numerous recognising programs considering a diversity of properties of regulatory regions have been developed. The system proposed here allows the specific contextual, conformational and physico-chemical properties to be revealed based on analysis of extended DNA regions. RESULTS: The Internet-accessible computer system RegScan, designed to analyse the extended regulatory regions of eukaryotic genes, has been developed. The computer system comprises the following software: (i) programs for classification dividing a set of promoters into TATA-containing and TATA-less promoters and promoters with and without CpG islands; (ii) programs for constructing (a) nucleotide frequency profiles, (b) sequence complexity profiles and (c) profiles of conformational and physico-chemical properties; (iii) the program for constructing the sets of degenerate oligonucleotide motifs of a specified length; and (iv) the program searching for and visualising repeats in nucleotide sequences. The system has allowed us to demonstrate the following characteristic patterns of vertebrate promoter regions: the TATA box region is flanked by regions with an increased G+C content and increased bending stiffness, the TATA box content is asymmetric and promoter regions are saturated with both direct and inverted repeats. AVAILABILITY: The computer system RegScan is available via the Internet at http://www.mgs.bionet.nsc. ru/Systems/RegScan, http://www.cbil.upenn.edu/mgs/systems/r egscan/.     

Differences among brain tumor stem cell types and fetal neural stem cells in focal regions of histone modifications and DNA methylation,broad regions of modifications,and bivalent promoters

Background

Aberrational epigenetic marks are believed to play a major role in establishing the abnormal features of cancer cells. Rational use and development of drugs aimed at epigenetic processes requires an understanding of the range, extent, and roles of epigenetic reprogramming in cancer cells. Using ChIP-chip and MeDIP-chip approaches, we localized well-established and prevalent epigenetic marks (H3K27me3, H3K4me3, H3K9me3, DNA methylation) on a genome scale in several lines of putative glioma stem cells (brain tumor stem cells, BTSCs) and, for comparison, normal human fetal neural stem cells (fNSCs).

Results

We determined a substantial “core” set of promoters possessing each mark in every surveyed BTSC cell type, which largely overlapped the corresponding fNSC sets. However, there was substantial diversity among cell types in mark localization. We observed large differences among cell types in total number of H3K9me3+ positive promoters and peaks and in broad modifications (defined as >50 kb peak length) for H3K27me3 and, to a lesser extent, H3K9me3. We verified that a change in a broad modification affected gene expression of CACNG7. We detected large numbers of bivalent promoters, but most bivalent promoters did not display direct overlap of contrasting epigenetic marks, but rather occupied nearby regions of the proximal promoter. There were significant differences in the sets of promoters bearing bivalent marks in the different cell types and few consistent differences between fNSCs and BTSCs.

Conclusions

Overall, our “core set” data establishes sets of potential therapeutic targets, but the diversity in sets of sites and broad modifications among cell types underscores the need to carefully consider BTSC subtype variation in epigenetic therapy. Our results point toward substantial differences among cell types in the activity of the production/maintenance systems for H3K9me3 and for broad regions of modification (H3K27me3 or H3K9me3). Finally, the unexpected diversity in bivalent promoter sets among these multipotent cells indicates that bivalent promoters may play complex roles in the overall biology of these cells. These results provide key information for forming the basis for future rational drug therapy aimed at epigenetic processes in these cells.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-724) contains supplementary material, which is available to authorized users.     

Subtle motifs: defining the limits of motif finding algorithms

MOTIVATION: What constitutes a subtle motif? Intuitively, it is a motif that is almost indistinguishable, in the statistical sense, from random motifs. This question has important practical consequences: consider, for example, a biologist that is generating a sample of upstream regulatory sequences with the goal of finding a regulatory pattern that is shared by these sequences. If the sequences are too short then one risks losing some of the regulatory patterns that are located further upstream. Conversely, if the sequences are too long, the motif becomes too subtle and one is then likely to encounter random motifs which are at least as significant statistically as the regulatory pattern itself. In practical terms one would like to recognize the sequence length threshold, or the twilight zone, beyond which the motifs are in some sense too subtle. RESULTS: The paper defines the motif twilight zone where every motif finding algorithm would be exposed to random motifs which are as significant as the one which is sought. We also propose an objective tool for evaluating the performance of subtle motif finding algorithms. Finally we apply these tools to evaluate the success of our MULTIPROFILER algorithm to detect subtle motifs.     

Systematic identification of mammalian regulatory motifs' target genes and functions

We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.     

WildSpan: mining structured motifs from protein sequences

Background  

Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.     

Selection for unequal densities of sigma70 promoter-like signals in different regions of large bacterial genomes

The evolutionary processes operating in the DNA regions that participate in the regulation of gene expression are poorly understood. In Escherichia coli, we have established a sequence pattern that distinguishes regulatory from nonregulatory regions. The density of promoter-like sequences, that could be recognizable by RNA polymerase and may function as potential promoters, is high within regulatory regions, in contrast to coding regions and regions located between convergently transcribed genes. Moreover, functional promoter sites identified experimentally are often found in the subregions of highest density of promoter-like signals, even when individual sites with higher binding affinity for RNA polymerase exist elsewhere within the regulatory region. In order to see the generality of this pattern, we have analyzed 43 additional genomes belonging to most established bacterial phyla. Differential densities between regulatory and nonregulatory regions are detectable in most of the analyzed genomes, with the exception of those that have evolved toward extreme genome reduction. Thus, presence of this pattern follows that of genes and other genomic features that require weak selection to be effective in order to persist. On this basis, we suggest that the loss of differential densities in the reduced genomes of host-restricted pathogens and symbionts is an outcome of the process of genome degradation resulting from the decreased efficiency of purifying selection in highly structured small populations. This implies that the differential distribution of promoter-like signals between regulatory and nonregulatory regions detected in large bacterial genomes confers a significant, although small, fitness advantage. This study paves the way for further identification of the specific types of selective constraints that affect the organization of regulatory regions and the overall distribution of promoter-like signals through more detailed comparative analyses among closely related bacterial genomes.