Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution

The detection of conserved motifs in promoters of orthologous genes (phylogenetic footprints) has become a common strategy to predict cis-acting regulatory elements. Several software tools are routinely used to raise hypotheses about regulation. However, these tools are generally used as black boxes, with default parameters. A systematic evaluation of optimal parameters for a footprint discovery strategy can bring a sizeable improvement to the predictions.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Genome Biol.

With genome analysis expanding from the study of genes to the study of gene regulation, 'regulatory genomics' utilizes sequence information, evolution and functional genomics measurements to unravel how regulatory information is encoded in the genome

Unraveling networks of co-regulated genes on the sole basis of genome sequences

With the growing number of available microbial genome sequences, regulatory signals can now be revealed as conserved motifs in promoters of orthologous genes (phylogenetic footprints). A next challenge is to unravel genome-scale regulatory networks. Using as sole input genome sequences, we predicted cis-regulatory elements for each gene of the yeast Saccharomyces cerevisiae by discovering over-represented motifs in the promoters of their orthologs in 19 Saccharomycetes species. We then linked all genes displaying similar motifs in their promoter regions and inferred a co-regulation network including 56 919 links between 3171 genes. Comparison with annotated regulons highlights the high predictive value of the method: a majority of the top-scoring predictions correspond to already known co-regulations. We also show that this inferred network is as accurate as a co-expression network built from hundreds of transcriptome microarray experiments. Furthermore, we experimentally validated 14 among 16 new functional links between orphan genes and known regulons. This approach can be readily applied to unravel gene regulatory networks from hundreds of microbial genomes for which no other information is available except the sequence. Long-term benefits can easily be perceived when considering the exponential increase of new genome sequences

RSAT: regulatory sequence analysis tools

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published

Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements

<p>Abstract</p> <p>Background</p> <p>Studies on bacterial signal transduction systems have revealed complex networks of functional interactions, where the response regulators play a pivotal role. The AtoSC system of <it>E. coli </it>activates the expression of <it>atoDAEB </it>operon genes, and the subsequent catabolism of short-chain fatty acids, upon acetoacetate induction. Transcriptome and phenotypic analyses suggested that <it>atoSC </it>is also involved in several other cellular activities, although we have recently reported a palindromic repeat within the <it>atoDAEB </it>promoter as the single, <it>cis</it>-regulatory binding site of the AtoC response regulator. In this work, we used a computational approach to explore the presence of yet unidentified AtoC binding sites within other parts of the <it>E. coli </it>genome.</p> <p>Results</p> <p>Through the implementation of a computational <it>de novo </it>motif detection workflow, a set of candidate motifs was generated, representing putative AtoC binding targets within the <it>E. coli </it>genome. In order to assess the biological relevance of the motifs and to select for experimental validation of those sequences related robustly with distinct cellular functions, we implemented a novel approach that applies Gene Ontology Term Analysis to the motif hits and selected those that were qualified through this procedure. The computational results were validated using Chromatin Immunoprecipitation assays to assess the <it>in vivo </it>binding of AtoC to the predicted sites. This process verified twenty-two additional AtoC binding sites, located not only within intergenic regions, but also within gene-encoding sequences.</p> <p>Conclusions</p> <p>This study, by tracing a number of putative AtoC binding sites, has indicated an AtoC-related cross-regulatory function. This highlights the significance of computational genome-wide approaches in elucidating complex patterns of bacterial cell regulation.</p

Theoretical and empirical quality assessment of transcription factor-binding motifs

Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality such as E-value and information content rely on theoretical models, and may fail in the context of full genome sequences. We propose a method, implemented in the program ‘matrix-quality’, that combines theoretical and empirical score distributions to assess reliability of PSSMs for predicting TF-binding sites. We applied ‘matrix-quality’ to estimate the predictive capacity of matrices for bacterial, yeast and mouse TFs. The evaluation of matrices from RegulonDB revealed some poorly predictive motifs, and allowed us to quantify the improvements obtained by applying multi-genome motif discovery. Interestingly, the method reveals differences between global and specific regulators. It also highlights the enrichment of binding sites in sequence sets obtained from high-throughput ChIP-chip (bacterial and yeast TFs), and ChIP–seq and experiments (mouse TFs). The method presented here has many applications, including: selecting reliable motifs before scanning sequences; improving motif collections in TFs databases; evaluating motifs discovered using high-throughput data sets

Specificity Determination by paralogous winged helix-turn-helix transcription factors

Transcription factors (TFs) localize to regulatory regions throughout the genome, where they exert physical or enzymatic control over the transcriptional machinery and regulate expression of target genes. Despite the substantial diversity of TFs found across all kingdoms of life, most belong to a relatively small number of structural families characterized by homologous DNA-binding domains (DBDs). In homologous DBDs, highly-conserved DNA-contacting residues define a characteristic ‘recognition potential’, or the limited sequence space containing high-affinity binding sites. Specificity-determining residues (SDRs) alter DNA binding preferences to further delineate this sequence space between homologous TFs, enabling functional divergence through the recognition of distinct genomic binding sites. This thesis explores the divergent DNA-binding preferences among dimeric, winged helix-turn-helix (wHTH) TFs belonging to the OmpR sub-family. As the terminal effectors of orthogonal two-component signaling pathways in Escherichia coli, OmpR paralogs bind distinct genomic sequences and regulate the expression of largely non-overlapping gene networks. Using high-throughput SELEX, I discover multiple sources of variation in DNA-binding, including the spacing and orientation of monomer sites as well as a novel binding ‘mode’ with unique half-site preferences (but retaining dimeric architecture). Surprisingly, given the diversity of residues observed occupying positions in contact with DNA, there are only minor quantitative differences in sequence-specificity between OmpR paralogs. Combining phylogenetic, structural, and biological information, I then define a comprehensive set of putative SDRs, which, although distributed broadly across the protein:DNA interface, preferentially localize to the major groove of the DNA helix. Direct specificity profiling of SDR variants reveals that individual SDRs impact local base preferences as well as global structural properties of the protein:DNA complex. This study demonstrates clearly that OmpR family TFs possess multiple ‘axes of divergence’, including base recognition, dimeric architecture, and structural attributes of the protein:DNA complex. It also provides evidence for a common structural ‘code’ for DNA-binding by OmpR homologues, and demonstrates that surprisingly modest residue changes can enable recognition of highly divergent sequence motifs. Importantly, well-characterized genomic binding sites for many of the TFs in this study diverge substantially from the presented de novo models, and it is unclear how mutations may affect binding in more complex environments. Further analysis using native sequences is required to build combined models of cis- and trans-evolution of two-component regulatory networks

A survey of DNA motif finding algorithms

Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

Unfolding plant desiccation tolerance : evolution, structure, and function of LEA proteins

When plants colonized land they developed a wide range of adaptations to cope with life in a drier environment. One key adaptation was desiccation tolerance (DT) which is the ability to survive the removal of almost all cellular water without irreparable damage. DT is recurrent in orthodox seeds and in the vegetative body of species commonly known as ‘resurrection plants’. In this thesis a multilevel approach, combining genomics, transcriptomics, gene family evolution, protein structural and functional analysis, and seed physiology was employed in order to tackle curiosity-driven fundamental questions about the major mechanisms governing DT. Several mechanisms were found to be important for DT, including the coordinated activation of cell protection through Late Embryogenesis Abundant (LEA) proteins, which were shown to be common amongst resurrection plants and orthodox seeds. These findings aid to the comprehension of the complexity of DT in plants, and may provide transferrable knowledge to design more water-stress tolerant crops.</p