A motif co-occurrence approach for genome-wide prediction of transcription-factor-binding sites in Escherichia coli

Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database ‘EcoPromDB’ has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

Importance of the 5' regulatory region to bacterial synthetic biology applications

The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of pre-characterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5' regulatory regions - promoters, untranslated regions and 5'-end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

Extreme Antibiotic Persistence via Heterogeneity-Generating Mutations Targeting Translation

Antibiotic persistence, the noninherited tolerance of a subpopulation of bacteria to high levels of antibiotics, is a bet-hedging phenomenon with broad clinical implications. Indeed, the isolation of bacteria with substantially increased persistence rates from chronic infections suggests that evolution of hyperpersistence is a significant factor in clinical therapy resistance. However, the pathways that lead to hyperpersistence and the underlying cellular states have yet to be systematically studied. Here, we show that laboratory evolution can lead to increase in persistence rates by orders of magnitude for multiple independently evolved populations of Escherichia coli and that the driving mutations are highly enriched in translation-related genes. Furthermore, two distinct adaptive mutations converge on concordant transcriptional changes, including increased population heterogeneity in the expression of several genes. Cells with extreme expression of these genes showed dramatic differences in persistence rates, enabling isolation of subpopulations in which a substantial fraction of cells are persisters. Expression analysis reveals coherent regulation of specific pathways that may be critical to establishing the hyperpersistence state. Hyperpersister mutants can thus enable the systematic molecular characterization of this unique physiological state, a critical prerequisite for developing antipersistence strategies.IMPORTANCE Bacterial persistence is a fascinating phenomenon in which a small subpopulation of bacteria becomes phenotypically tolerant to lethal antibiotic exposure. There is growing evidence that populations of bacteria in chronic clinical infections develop a hyperpersistent phenotype, enabling a substantially larger subpopulation to survive repeated antibiotic treatment. The mechanisms of persistence and modes of increasing persistence rates remain largely unknown. Here, we utilized experimental evolution to select for Escherichia coli mutants that have more than a thousandfold increase in persistence rates. We discovered that a variety of individual mutations to translation-related processes are causally involved. Furthermore, we found that these mutations lead to population heterogeneity in the expression of specific genes. We show that this can be used to isolate populations in which the majority of bacteria are persisters, thereby enabling systems-level characterization of this fascinating and clinically significant microbial phenomenon.

Evolutionary Footprints of Short Tandem Repeats in Avian Promoters

Short tandem repeats (STRs) or microsatellites are well-known sequence elements that may change the spacing between transcription factor binding sites (TFBSs) in promoter regions by expansion or contraction of repetitive units. Some of these mutations have the potential to contribute to phenotypic diversity by altering patterns of gene expression. To explore how repetitive sequence motifs within promoters have evolved in avian lineages under mutation-selection balance, more than 400 evolutionary conserved STRs (ecSTRs) were identified in this study by comparing the 2 kb upstream promoter sequences of chicken against those of other birds (turkey, duck, zebra finch, and flycatcher). The rate of conservation was significantly higher in AG dinucleotide repeats than in AC or AT repeats, with the expansion of AG motifs being noticeably constrained in passerines. Analysis of the relative distance between ecSTRs and TFBSs revealed a significantly higher rate of conserved TFBSs in the vicinity of ecSTRs in both chicken-duck and chicken-passerine comparisons. Our comparative study provides a novel insight into which intrinsic factors have influenced the degree of constraint on repeat expansion/contraction during avian promoter evolution.

Multifactorial Competition and Resistance in a Two-Species Bacterial System

Microorganisms exist almost exclusively in interactive multispecies communities, but genetic determinants of the fitness of interacting bacteria, and accessible adaptive pathways, remain uncharacterized. Here, using a two-species system, we studied the antagonism of Pseudomonas aeruginosa against Escherichia coli. Our unbiased genome-scale approach enabled us to identify multiple factors that explained the entire antagonism observed. We discovered both forms of ecological competition–sequestration of iron led to exploitative competition, while phenazine exposure engendered interference competition. We used laboratory evolution to discover adaptive evolutionary trajectories in our system. In the presence of P. aeruginosa toxins, E. coli populations showed parallel molecular evolution and adaptive convergence at the gene-level. The multiple resistance pathways discovered provide novel insights into mechanisms of toxin entry and activity. Our study reveals the molecular complexity of a simple two-species interaction, an important first-step in the application of systems biology to detailed molecular dissection of interactions within native microbiomes.

Bacteria commonly exist in nature as part of large multispecies communities, and their behavior is affected by the surrounding species via secreted molecules or physical contact. Such interactions are poorly understood, and the pathways that actually affect bacterial growth and behavior in any multispecies system have rarely been studied. In this study, we show that the opportunistic pathogen Pseudomonas aeruginosa inhibits the growth of the commensal Escherichia coli, and we use unbiased genome-scale methods to identify the mediators. We find that P. aeruginosa iron-chelating molecules and redox-active phenazines account for all of the E. coli growth inhibition seen in our system. We also evolve E. coli in the presence of the P. aeruginosa antimicrobials and identify multiple pathways that lead to resistance, gaining novel insights into the mechanism of action of these antimicrobial molecules. Thus, our study demonstrates the complexity of even simple two-species bacterial systems and lays down a framework for studying such interactions at the molecular level.

Discovery and information-theoretic characterization of transcription factor binding sites that act cooperatively

Transcription factor binding to the surface of DNA regulatory regions is one of the primary causes of regulating gene expression levels. A probabilistic approach to model protein-DNA interactions at the sequence level is through position weight matrices (PWMs) that estimate the joint probability of a DNA binding site sequence by assuming positional independence within the DNA sequence. Here we construct conditional PWMs that depend on the motif signatures in the flanking DNA sequence, by conditioning known binding site loci on the presence or absence of additional binding sites in the flanking sequence of each site's locus. Pooling known sites with similar flanking sequence patterns allows for the estimation of the conditional distribution function over the binding site sequences. We apply our model to the Dorsal transcription factor binding sites active in patterning the Dorsal-Ventral axis of Drosophila development. We find that those binding sites that cooperate with nearby Twist sites on average contain about 0.5 bits of information about the presence of Twist transcription factor binding sites in the flanking sequence. We also find that Dorsal binding site detectors conditioned on flanking sequence information make better predictions about what is a Dorsal site relative to background DNA than detection without information about flanking sequence features.

Role of DNA sequence based structural features of promoters in transcription initiation and gene expression

Regulatory information for transcription initiation is present in a stretch of genomic DNA, called the promoter region that is located upstream of the transcription start site (TSS) of the gene. The promoter region interacts with different transcription factors and RNA polymerase to initiate transcription and contains short stretches of transcription factor binding sites (TFBSs), as well as structurally unique elements. Recent experimental and computational analyses of promoter sequences show that they often have non-B-DNA structural motifs, as well as some conserved structural properties, such as stability, bendability, nucleosome positioning preference and curvature, across a class of organisms. Here, we briefly describe these structural features, the differences observed in various organisms and their possible role in regulation of gene expression.

Genome-scale analyses of Escherichia coli and Salmonella enterica AraC reveal noncanonical targets and an expanded core regulon

Escherichia coli AraC is a well-described transcription activator of genes involved in arabinose metabolism. Using complementary genomic approaches, chromatin immunoprecipitation (ChIP)-chip, and transcription profiling, we identify direct regulatory targets of AraC, including five novel target genes: ytfQ, ydeN, ydeM, ygeA, and polB. Strikingly, only ytfQ has an established connection to arabinose metabolism, suggesting that AraC has a broader function than previously described. We demonstrate arabinose-dependent repression of ydeNM by AraC, in contrast to the well-described arabinose-dependent activation of other target genes. We also demonstrate unexpected read-through of transcription at the Rho-independent terminators downstream of araD and araE, leading to significant increases in the expression of polB and ygeA, respectively. AraC is highly conserved in the related species Salmonella enterica. We use ChIP sequencing (ChIP-seq) and RNA sequencing (RNA-seq) to map the AraC regulon in S. enterica. A comparison of the E. coli and S. enterica AraC regulons, coupled with a bioinformatic analysis of other related species, reveals a conserved regulatory network across the family Enterobacteriaceae comprised of 10 genes associated with arabinose transport and metabolism.

Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements

Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis-regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF-TF-DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein-protein interactions, potentially indirect interactions and 'through-DNA' interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex.

dPeak: high resolution identification of transcription factor binding sites from PET and SET ChIP-Seq data

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as to resolution. Applications of dPeak to ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is widely used for studying in vivo protein-DNA interactions genome-wide. Current state-of-the-art ChIP-Seq protocols utilize single-end tag (SET) assay which only sequences ends of DNA fragments in the library. Although paired-end tag (PET) sequencing is routinely used in other applications of next generation sequencing, it has not been much adapted to ChIP-Seq. We illustrate both experimentally and computationally that PET sequencing significantly improves the resolution of ChIP-Seq experiments and enables ChIP-Seq applications in compact genomes like Escherichia coli (E. coli). To enable efficient identification using PET ChIP-Seq data, we develop dPeak as a high resolution binding site identification algorithm. dPeak implements probabilistic models for both SET and PET data and facilitates efficient analysis of both data types. Applications of dPeak to deeply sequenced E. coli PET and SET ChIP-Seq data establish significantly better resolution of PET compared to SET sequencing.

A fast weak motif-finding algorithm based on community detection in graphs

BACKGROUND

Identification of transcription factor binding sites (also called 'motif discovery') in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites. State-of-the-art algorithms have their own constraints (e.g., high time or space complexity for finding long motifs, low precision in identification of weak motifs, or the OOPS constraint: one occurrence of the motif instance per sequence) which limit their scope of application.

RESULTS

In this paper, we present a novel and fast algorithm we call TFBSGroup. It is based on community detection from a graph and is used to discover long and weak (l,d) motifs under the ZOMOPS constraint (zero, one or multiple occurrence(s) of the motif instance(s) per sequence), where l is the length of a motif and d is the maximum number of mutations between a motif instance and the motif itself. Firstly, TFBSGroup transforms the (l, d) motif search in sequences to focus on the discovery of dense subgraphs within a graph. It identifies these subgraphs using a fast community detection method for obtaining coarse-grained candidate motifs. Next, it greedily refines these candidate motifs towards the true motif within their own communities. Empirical studies on synthetic (l, d) samples have shown that TFBSGroup is very efficient (e.g., it can find true (18, 6), (24, 8) motifs within 30 seconds). More importantly, the algorithm has succeeded in rapidly identifying motifs in a large data set of prokaryotic promoters generated from the Escherichia coli database RegulonDB. The algorithm has also accurately identified motifs in ChIP-seq data sets for 12 mouse transcription factors involved in ES cell pluripotency and self-renewal.

CONCLUSIONS

Our novel heuristic algorithm, TFBSGroup, is able to quickly identify nearly exact matches for long and weak (l, d) motifs in DNA sequences under the ZOMOPS constraint. It is also capable of finding motifs in real applications. The source code for TFBSGroup can be obtained from http://bioinformatics.bioengr.uic.edu/TFBSGroup/.

Screening of potential pseudo att sites of Streptomyces phage ΦC31 integrase in the human genome

AIM

ΦC31 integrase mediates site-specific recombination between two short sequences, attP and attB, in phage and bacterial genomes, which is a promising tool in gene regulation-based therapy since the zinc finger structure is probably the DNA recognizing domain that can further be engineered. The aim of this study was to screen potential pseudo att sites of ΦC31 integrase in the human genome, and evaluate the risks of its application in human gene therapy.

METHODS

TFBS (transcription factor binding sites) were found on the basis of reported pseudo att sites using multiple motif-finding tools, including AlignACE, BioProspector, Consensus, MEME, and Weeder. The human genome with the proposed motif was scanned to find the potential pseudo att sites of ΦC31 integrase.

RESULTS

The possible recognition motif of ΦC31 integrase was identified, which was composed of two co-occurrence conserved elements that were reverse complement to each other flanking the core sequence TTG. In the human genome, a total of 27924 potential pseudo att sites of ΦC31 integrase were found, which were distributed in each human chromosome with high-risk specificity values in the chromosomes 16, 17, and 19. When the risks of the sites were evaluate more rigorously, 53 hits were discovered, and some of them were just the vital functional genes or regulatory regions, such as ACYP2, AKR1B1, DUSP4, etc.

CONCLUSION

The results provide clues for more comprehensive evaluation of the risks of using ΦC31 integrase in human gene therapy and for drug discovery.

TrFAST: a tool to predict signaling pathway-specific transcription factor binding sites

Recent advances in the development of high-throughput tools have significantly revolutionized our understanding of molecular mechanisms underlying normal and dysfunctional biological processes. Here we present a novel computational tool, transcription factor search and analysis tool (TrFAST), which was developed for the in silico analysis of transcription factor binding sites (TFBSs) of signaling pathway-specific TFs. TrFAST facilitates searching as well as comparative analysis of regulatory motifs through an exact pattern matching algorithm followed by the graphical representation of matched binding sites in multiple sequences up to 50 kb in length. TrFAST is proficient in reducing the number of comparisons by the exact pattern matching strategy. In contrast to the pre-existing tools that find TFBS in a single sequence, TrFAST seeks out the desired pattern in multiple sequences simultaneously. It counts the GC content within the given multiple sequence data set and assembles the combinational details of consensus sequence(s) located at these regions, thereby generating a visual display based on the abundance of unique pattern. Comparative regulatory region analysis of multiple orthologous sequences simultaneously enhances the features of TrFAST and provides a significant insight into study of conservation of non-coding cis-regulatory elements. TrFAST is freely available at http://www.fi-pk.com/trfast.html.

MycoRRdb: a database of computationally identified regulatory regions within intergenic sequences in mycobacterial genomes

The identification of regulatory regions for a gene is an important step towards deciphering the gene regulation. Regulatory regions tend to be conserved under evolution that facilitates the application of comparative genomics to identify such regions. The present study is an attempt to make use of this attribute to identify regulatory regions in the Mycobacterium species followed by the development of a database, MycoRRdb. It consist the regulatory regions identified within the intergenic distances of 25 mycobacterial species. MycoRRdb allows to retrieve the identified intergenic regulatory elements in the mycobacterial genomes. In addition to the predicted motifs, it also allows user to retrieve the Reciprocal Best BLAST Hits across the mycobacterial genomes. It is a useful resource to understand the transcriptional regulatory mechanism of mycobacterial species. This database is first of its kind which specifically addresses cis-regulatory regions and also comprehensive to the mycobacterial species. Database URL: http://mycorrdb.uohbif.in.

Construction of the control system of target molecule expression in Escherichia coli: application to a validation platform for bactericidal and bacteriostatic profiles due to suppression of a target molecule

Validation of bactericidal profiles owing to a deficiency of target bacterial molecule provides opportunities to discover antimicrobial drug candidates. In this study, we constructed genetic-engineered Escherichia coli strains, in which the target gene expression is conditionally regulated by a tryptophan promoter, while the target protein expression is regulated by N-end rule-based proteolysis. Among 10 genes, whose correspondent proteins are target candidates of antibiotics for community acquired respiratory tract infection, it was clearly demonstrated that the suppression of DnaB, GlmU, or DnaX results in a bactericidal profile, while the suppression of FabB, PyrG, DnaG, Der, PyrH, Era, or IspA leads to a bacteriostatic profile. This study is the first to predict the antibacterial inhibition profiles of Der, DnaG, DnaX, Era, GlmU, IspA, PyrG, and PyrH, and confirms previous findings for DnaB and FabB. The results suggested that the system constructed in this study is a novel and useful tool to validate whether the target bacterial molecule has appropriate properties as a target of antimicrobial agents.

The MetJ regulon in gammaproteobacteria determined by comparative genomics methods

Background

Whole-genome sequencing of bacteria has proceeded at an exponential pace but annotation validation has lagged behind. For instance, the MetJ regulon, which controls methionine biosynthesis and transport, has been studied almost exclusively in E. coli and Salmonella, but homologs of MetJ exist in a variety of other species. These include some that are pathogenic (e.g. Yersinia) and some that are important for environmental remediation (e.g. Shewanella) but many of which have not been extensively characterized in the literature.

Results

We have determined the likely composition of the MetJ regulon in all species which have MetJ homologs using bioinformatics techniques. We show that the core genes known from E. coli are consistently regulated in other species, and we identify previously unknown members of the regulon. These include the cobalamin transporter, btuB; all the genes involved in the methionine salvage pathway; as well as several enzymes and transporters of unknown specificity.

Conclusions

The MetJ regulon is present and functional in five orders of gammaproteobacteria: Enterobacteriales, Pasteurellales, Vibrionales, Aeromonadales and Alteromonadales. New regulatory activity for MetJ was identified in the genomic data and verified experimentally. This strategy should be applicable for the elucidation of regulatory pathways in other systems by using the extensive sequencing data currently being generated.

Use of structural DNA properties for the prediction of transcription-factor binding sites in Escherichia coli

Recognition of genomic binding sites by transcription factors can occur through base-specific recognition, or by recognition of variations within the structure of the DNA macromolecule. In this article, we investigate what information can be retrieved from local DNA structural properties that is relevant to transcription factor binding and that cannot be captured by the nucleotide sequence alone. More specifically, we explore the benefit of employing the structural characteristics of DNA to create binding-site models that encompass indirect recognition for the Escherichia coli model organism. We developed a novel methodology [Conditional Random fields of Smoothed Structural Data (CRoSSeD)], based on structural scales and conditional random fields to model and predict regulator binding sites. The value of relying on local structural-DNA properties is demonstrated by improved classifier performance on a large number of biological datasets, and by the detection of novel binding sites which could be validated by independent data sources, and which could not be identified using sequence data alone. We further show that the CRoSSeD-binding-site models can be related to the actual molecular mechanisms of the transcription factor DNA binding, and thus cannot only be used for prediction of novel sites, but might also give valuable insights into unknown binding mechanisms of transcription factors.

Quantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials

A new computational method uses gene expression databases and transcription factor binding specificities to describe regulatory elements in the Drosophila A/P patterning network in unprecedented detail.

Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development. We describe a new computational strategy to annotate genomic sequences based on their “pattern generating potential” and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions. We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction. Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome. These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns. This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene, obtained from available gene expression databases. Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factor's contribution to the module's pattern generating potential. We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network, identifying many examples of modules predicted to have overlapping expression activities. Surprisingly, conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions. Thus, unlike previous module prediction methods, this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern. As databases of transcription factor specificities and in vivo gene expression patterns grow, analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks.

The developmental program specifying segmentation along the anterior-posterior axis of the Drosophila embryo is one of the best studied examples of transcriptional regulatory networks. Previous work has identified the location and function of dozens of DNA segments called cis-regulatory “modules” that regulate several genes in precise spatial patterns in the early embryo. In many cases, transcription factors that interact with such modules have also been identified. We present a novel computational framework that turns a qualitative and fragmented understanding of modules and factor-module interactions into a quantitative, systems-level view. The formalism utilizes experimentally characterized binding specificities of transcription factors and gene expression patterns to describe how multiple transcription factors (working as activators or repressors) act together in a module to determine its regulatory activity. This formalism can explain the expression patterns of known modules, infer factor-module interactions and quantify the potential of an arbitrary DNA segment to drive a gene's expression. We have also employed databases of gene expression patterns to find novel modules of the regulatory network. As databases of binding motifs and gene expression patterns grow, this new approach provides a general method to decode transcriptional regulatory sequences and networks.

A blind deconvolution approach to high-resolution mapping of transcription factor binding sites from ChIP-seq data

CSdeconv is a novel method for determining the location of transcription factor binding from ChIP-seq data that discriminates closely-spaced sites.

We present CSDeconv, a computational method that determines locations of transcription factor binding from ChIP-seq data. CSDeconv differs from prior methods in that it uses a blind deconvolution approach that allows closely-spaced binding sites to be called accurately. We apply CSDeconv to novel ChIP-seq data for DosR binding in Mycobacterium tuberculosis and to existing data for GABP in humans and show that it can discriminate binding sites separated by as few as 40 bp.

Furanose-specific sugar transport: characterization of a bacterial galactofuranose-binding protein

The widespread utilization of sugars by microbes is reflected in the diversity and multiplicity of cellular transporters used to acquire these compounds from the environment. The model bacterium Escherichia coli has numerous transporters that allow it to take up hexoses and pentoses, which recognize the more abundant pyranose forms of these sugars. Here we report the biochemical and structural characterization of a transporter protein YtfQ from E. coli that forms part of an uncharacterized ABC transporter system. Remarkably the crystal structure of this protein, solved to 1.2 A using x-ray crystallography, revealed that YtfQ binds a single molecule of galactofuranose in its ligand binding pocket. Selective binding of galactofuranose over galactopyranose was also observed using NMR methods that determined the form of the sugar released from the protein. The pattern of expression of the ytfQRTyjfF operon encoding this transporter mirrors that of the high affinity galactopyranose transporter of E. coli, suggesting that this bacterium has evolved complementary transporters that enable it to use all the available galactose present during carbon limiting conditions.

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.

Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources

Background

Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes.

Results

We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website.

Conclusion

The Prosecutor software and supplementary datasets available at allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied.

GBNet: deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach

Background

Combinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.

Results

We present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.

Conclusion

We thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

Large-scale computational and statistical analyses of high transcription potentialities in 32 prokaryotic genomes

This article compares 32 bacterial genomes with respect to their high transcription potentialities. The sigma70 promoter has been widely studied for Escherichia coli model and a consensus is known. Since transcriptional regulations are known to compensate for promoter weakness (i.e. when the promoter similarity with regard to the consensus is rather low), predicting functional promoters is a hard task. Instead, the research work presented here comes within the scope of investigating potentially high ORF expression, in relation with three criteria: (i) high similarity to the sigma70 consensus (namely, the consensus variant appropriate for each genome), (ii) transcription strength reinforcement through a supplementary binding site--the upstream promoter (UP) element--and (iii) enhancement through an optimal Shine-Dalgarno (SD) sequence. We show that in the AT-rich Firmicutes' genomes, frequencies of potentially strong sigma70-like promoters are exceptionally high. Besides, though they contain a low number of strong promoters (SPs), some genomes may show a high proportion of promoters harbouring an UP element. Putative SPs of lesser quality are more frequently associated with an UP element than putative strong promoters of better quality. A meaningful difference is statistically ascertained when comparing bacterial genomes with similarly AT-rich genomes generated at random; the difference is the highest for Firmicutes. Comparing some Firmicutes genomes with similarly AT-rich Proteobacteria genomes, we confirm the Firmicutes specificity. We show that this specificity is neither explained by AT-bias nor genome size bias; neither does it originate in the abundance of optimal SD sequences, a typical and significant feature of Firmicutes more thoroughly analysed in our study.

Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation

Understanding transcriptional regulation is a major goal of molecular biology. Motif expression decomposition (MED) was recently introduced to describe the expression level of a gene as the sum of the products of the binding strengths of its cis-regulatory motifs and the activities of the corresponding trans-acting transcription factors (TFs). Here, we use computer simulation to examine the accuracy of MED. We found that although MED accurately rebuilds gene expression levels from decomposed motif binding strengths and TF activities, estimates of motif binding strengths and TF activities are unreliable. Nonetheless, MED provides accurate estimates of relative binding strengths of the same motif in different genes and relative activities of the same TF under different conditions. We found that reasonably accurate results are achievable with genome-wide expression data from only 30 conditions and that MED results are robust to the existence of unknown occurrences of known motifs, although they are less robust to the presence of unknown motifs. With these understandings, judicious use of MED will likely provide useful information about eukaryotic transcriptional regulation. As an example, MED results are used to demonstrate that motifs generally have higher binding strengths when appearing in multiple copies than appearing in one copy per promoter.

Limited functional conservation of a global regulator among related bacterial genera: Lrp in Escherichia, Proteus and Vibrio

Background

Bacterial genome sequences are being determined rapidly, but few species are physiologically well characterized. Predicting regulation from genome sequences usually involves extrapolation from better-studied bacteria, using the hypothesis that a conserved regulator, conserved target gene, and predicted regulator-binding site in the target promoter imply conserved regulation between the two species. However many compared organisms are ecologically and physiologically diverse, and the limits of extrapolation have not been well tested. In E. coli K-12 the leucine-responsive regulatory protein (Lrp) affects expression of ~400 genes. Proteus mirabilis and Vibrio cholerae have highly-conserved lrp orthologs (98% and 92% identity to E. coli lrp). The functional equivalence of Lrp from these related species was assessed.

Results

Heterologous Lrp regulated gltB, livK and lrp transcriptional fusions in an E. coli background in the same general way as the native Lrp, though with significant differences in extent. Microarray analysis of these strains revealed that the heterologous Lrp proteins significantly influence only about half of the genes affected by native Lrp. In P. mirabilis, heterologous Lrp restored swarming, though with some pattern differences. P. mirabilis produced substantially more Lrp than E. coli or V. cholerae under some conditions. Lrp regulation of target gene orthologs differed among the three native hosts. Strikingly, while Lrp negatively regulates its own gene in E. coli, and was shown to do so even more strongly in P. mirabilis, Lrp appears to activate its own gene in V. cholerae.

Conclusion

The overall similarity of regulatory effects of the Lrp orthologs supports the use of extrapolation between related strains for general purposes. However this study also revealed intrinsic differences even between orthologous regulators sharing >90% overall identity, and 100% identity for the DNA-binding helix-turn-helix motif, as well as differences in the amounts of those regulators. These results suggest that predicting regulation of specific target genes based on genome sequence comparisons alone should be done on a conservative basis.

Evolution and selection in yeast promoters: analyzing the combined effect of diverse transcription factor binding sites

In comparative genomics one analyzes jointly evolutionarily related species in order to identify conserved and diverged sequences and to infer their function. While such studies enabled the detection of conserved sequences in large genomes, the evolutionary dynamics of regulatory regions as a whole remain poorly understood. Here we present a probabilistic model for the evolution of promoter regions in yeast, combining the effects of regulatory interactions of many different transcription factors. The model expresses explicitly the selection forces acting on transcription factor binding sites in the context of a dynamic evolutionary process. We develop algorithms to compute likelihood and to learn de novo collections of transcription factor binding motifs and their selection parameters from alignments. Using the new techniques, we examine the evolutionary dynamics in Saccharomyces species promoters. Analyses of an evolutionary model constructed using all known transcription factor binding motifs and of a model learned from the data automatically reveal relatively weak selection on most binding sites. Moreover, according to our estimates, strong binding sites are constraining only a fraction of the yeast promoter sequence that is under selection. Our study demonstrates how complex evolutionary dynamics in noncoding regions emerges from formalization of the evolutionary consequences of known regulatory mechanisms.

Cells use sophisticated regulation to transform static genomic information into flexible function. We are still far from understanding how such regulation evolves. Short DNA sequences that physically bind transcription factors in promoter areas near target genes play an important role in gene regulation and are directly subject to mutation and selection. In this work, we develop a methodology for studying the evolution of promoter sequences under the effect of multiple regulatory interactions. We present a model that describes the evolutionary process at each genomic locus, taking into account a random flux of mutations that occur in it and the effects of transcription factor binding sites gain or loss. Our model accounts for dependencies (or epistasis) between adjacent loci that contribute to the same regulatory interactions: mutation in one such locus immediately changes the effect of mutations in the other. Using our model, we characterize the evolution of promoters in yeast, showing that many regulatory interactions that were discovered experimentally or computationally are evolutionarily unstable. The dynamic nature of transcriptional interactions may be explained if the regulatory phenotype is achieved through multiple interactions at different levels of specificity, and if only relatively few of these interactions are essential for themselves.

Structure and evolution of gene regulatory networks in microbial genomes

With the availability of genome sequences for hundreds of microbial genomes, it has become possible to address several questions from a comparative perspective to understand the structure and function of regulatory systems, at least in model organisms. Recent studies have focused on topological properties and the evolution of regulatory networks and their components. Our understanding of natural networks is paving the way to embedding synthetic regulatory systems into organisms, allowing us to expand the natural diversity of living systems to an extent we had never before anticipated.

Position and distance specificity are important determinants of cis-regulatory motifs in addition to evolutionary conservation

Computational discovery of cis-regulatory elements remains challenging. To cope with the high false positives, evolutionary conservation is routinely used. However, conservation is only one of the attributes of cis-regulatory elements and is neither necessary nor sufficient. Here, we assess two additional attributes—positional and inter-motif distance specificity—that are critical for interactions between transcription factors. We first show that for a greater than expected fraction of known motifs, the genes that contain the motifs in their promoters in a position-specific or distance-specific manner are related, both in function and/or in expression pattern. We then use the position and distance specificity to discover novel motifs. Our work highlights the importance of distance and position specificity, in addition to the evolutionary conservation, in discovering cis-regulatory motifs.

A comparative genomics approach to identifying the plasticity transcriptome

Background

Neuronal activity regulates gene expression to control learning and memory, homeostasis of neuronal function, and pathological disease states such as epilepsy. A great deal of experimental evidence supports the involvement of two particular transcription factors in shaping the genomic response to neuronal activity and mediating plasticity: CREB and zif268 (egr-1, krox24, NGFI-A). The gene targets of these two transcription factors are of considerable interest, since they may help develop hypotheses about how neural activity is coupled to changes in neural function.

Results

We have developed a computational approach for identifying binding sites for these transcription factors within the promoter regions of annotated genes in the mouse, rat, and human genomes. By combining a robust search algorithm to identify discrete binding sites, a comparison of targets across species, and an analysis of binding site locations within promoter regions, we have defined a group of candidate genes that are strong CREB- or zif268 targets and are thus regulated by neural activity. Our analysis revealed that CREB and zif268 share a disproportionate number of targets in common and that these common targets are dominated by transcription factors.

Conclusion

These observations may enable a more detailed understanding of the regulatory networks that are induced by neural activity and contribute to the plasticity transcriptome. The target genes identified in this study will be a valuable resource for investigators who hope to define the functions of specific genes that underlie activity-dependent changes in neuronal properties.

Computational framework for the prediction of transcription factor binding sites by multiple data integration

Control of gene expression is essential to the establishment and maintenance of all cell types, and its dysregulation is involved in pathogenesis of several diseases. Accurate computational predictions of transcription factor regulation may thus help in understanding complex diseases, including mental disorders in which dysregulation of neural gene expression is thought to play a key role. However, biological mechanisms underlying the regulation of gene expression are not completely understood, and predictions via bioinformatics tools are typically poorly specific.

We developed a bioinformatics workflow for the prediction of transcription factor binding sites from several independent datasets. We show the advantages of integrating information based on evolutionary conservation and gene expression, when tackling the problem of binding site prediction. Consistent results were obtained on a large simulated dataset consisting of 13050 in silico promoter sequences, on a set of 161 human gene promoters for which binding sites are known, and on a smaller set of promoters of Myc target genes.

Our computational framework for binding site prediction can integrate multiple sources of data, and its performance was tested on different datasets. Our results show that integrating information from multiple data sources, such as genomic sequence of genes' promoters, conservation over multiple species, and gene expression data, indeed improves the accuracy of computational predictions.

Genome-wide mapping of ORC and Mcm2p binding sites on tiling arrays and identification of essential ARS consensus sequences in S. cerevisiae

BACKGROUND

Eukaryotic replication origins exhibit different initiation efficiencies and activation times within S-phase. Although local chromatin structure and function influences origin activity, the exact mechanisms remain poorly understood. A key to understanding the exact features of chromatin that impinge on replication origin function is to define the precise locations of the DNA sequences that control origin function. In S. cerevisiae, Autonomously Replicating Sequences (ARSs) contain a consensus sequence (ACS) that binds the Origin Recognition Complex (ORC) and is essential for origin function. However, an ACS is not sufficient for origin function and the majority of ACS matches do not function as ORC binding sites, complicating the specific identification of these sites.

RESULTS

To identify essential origin sequences genome-wide, we utilized a tiled oligonucleotide array (NimbleGen) to map the ORC and Mcm2p binding sites at high resolution. These binding sites define a set of potential Autonomously Replicating Sequences (ARSs), which we term nimARSs. The nimARS set comprises 529 ORC and/or Mcm2p binding sites, which includes 95% of known ARSs, and experimental verification demonstrates that 94% are functional. The resolution of the analysis facilitated identification of potential ACSs (nimACSs) within 370 nimARSs. Cross-validation shows that the nimACS predictions include 58% of known ACSs, and experimental verification indicates that 82% are essential for ARS activity.

CONCLUSION

These findings provide the most comprehensive, accurate, and detailed mapping of ORC binding sites to date, adding to the emerging picture of the chromatin organization of the budding yeast genome.

Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues

Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues based on gene expression databases. We then evaluated the relationships between TFs using the relative position and co-occurrence of their binding sites in the promoters of tissue-specific genes. The predicted TF–TF interactions were validated by both known protein–protein interactions and co-expression of their target genes. We found that our predictions are enriched in known protein–protein interactions (>80 times that of random expectation). In addition, we found that the target genes show the highest co-expression in the tissue of interest. Our findings demonstrate that non-tissue specific TFs play a large role in regulation of tissue-specific genes. Furthermore, they show that individual TFs can contribute to tissue specificity in different tissues by interacting with distinct TF partners. Lastly, we identified several tissue-specific TF clusters that may play important roles in tissue-specific gene regulation.

Flexible promoter architecture requirements for coactivator recruitment

Background

The spatial organization of transcription factor binding sites in regulatory DNA, and the composition of intersite sequences, influences the assembly of the multiprotein complexes that regulate RNA polymerase recruitment and thereby affects transcription. We have developed a genetic approach to investigate how reporter gene transcription is affected by varying the spacing between transcription factor binding sites. We characterized the components of promoter architecture that govern the yeast transcription factors Cbf1 and Met31/32, which bind independently, but collaboratively recruit the coactivator Met4.

Results

A Cbf1 binding site was required upstream of a Met31/32 binding site for full reporter gene expression. Distance constraints on coactivator recruitment were more flexible than those for cooperatively binding transcription factors. Distances from 18 to 50 bp between binding sites support efficient recruitment of Met4, with only slight modulation by helical phasing. Intriguingly, we found that certain sequences located between the binding sites abolished gene expression.

Conclusion

These results yield insight to the influence of both binding site architecture and local DNA flexibility on gene expression, and can be used to refine computational predictions of gene expression from promoter sequences. In addition, our approach can be applied to survey promoter architecture requirements for arbitrary combinations of transcription factor binding sites.

Complex transcriptional control links NikABCDE-dependent nickel transport with hydrogenase expression in Escherichia coli

Escherichia coli requires nickel under anaerobic growth conditions for the synthesis of catalytically active NiFe hydrogenases. Transcription of the NikABCDE nickel transporter, which is required for NiFe hydrogenase synthesis, was previously shown to be upregulated by FNR (fumarate-nit rate regulator) in the absence of oxygen and repressed by the NikR repressor in the presence of high extracellular nickel levels. We present here a detailed analysis of nikABCDE transcriptional regulation and show that it closely correlates with hydrogenase expression levels. We identify a nitrate-dependent mechanism for nikABCDE repression that is linked to the NarLX two-component system. NikR is functional under all nickel conditions tested, but its activity is modulated by the total nickel concentration present as well as by one or more components of the hydrogenase assembly pathway. Unexpectedly, NikR function is independent of NikABCDE function, suggesting that NikABCDE is a hydrogenase-specific nickel transporter, consistent with its original identification as a hydrogenase (hyd) mutant. Further, the results suggest that the hydrogenase assembly pathway is sequestered within the cell. A second nickel import pathway in E. coli is implicated in NikR function.

Comparative studies of transcriptional regulation mechanisms in a group of eight gamma-proteobacterial genomes

Experimental data on the Escherichia coli transcriptional regulation has enabled the construction of statistical models to predict new regulatory elements within its genome. Far less is known about the transcriptional regulatory elements in other gamma-proteobacteria with sequenced genomes, so it is of great interest to conduct comparative genomic studies oriented to extracting biologically relevant information about transcriptional regulation in these less studied organisms using the knowledge from E. coli. In this work, we use the information stored in the TRACTOR_DB database to conduct a comparative study on the mechanisms of transcriptional regulation in eight gamma-proteobacteria and 38 regulons. We assess the conservation of transcription factors binding specificity across all the eight genomes and show a correlation between the conservation of a regulatory site and the structure of the transcription unit it regulates. We also find a marked conservation of site-promoter distances across the eight organisms and a correspondence of the statistical significance of co-occurrence of pairs of transcription factor binding sites in the regulatory regions, which is probably related to a conserved architecture of higher-order regulatory complexes in the organisms studied. The results obtained in this study using the information on transcriptional regulation in E. coli enable us to conclude that not only transcription factor-binding sites are conserved across related species but also several of the transcriptional regulatory mechanisms previously identified in E. coli.

A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs

By analyzing 3' UTR sequences and mRNA decay profiles in yeast, 53 sequence motifs have been identified that may be implicated in stabilization or destabilization of mRNA.

Background

In recent years, intensive computational efforts have been directed towards the discovery of promoter motifs that correlate with mRNA expression profiles. Nevertheless, it is still not always possible to predict steady-state mRNA expression levels based on promoter signals alone, suggesting that other factors may be involved. Other genic regions, in particular 3' UTRs, which are known to exert regulatory effects especially through controlling RNA stability and localization, were less comprehensively investigated, and deciphering regulatory motifs within them is thus crucial.

Results

By analyzing 3' UTR sequences and mRNA decay profiles of Saccharomyces cerevisiae genes, we derived a catalog of 53 sequence motifs that may be implicated in stabilization or destabilization of mRNAs. Some of the motifs correspond to known RNA-binding protein sites, and one of them may act in destabilization of ribosome biogenesis genes during stress response. In addition, we present for the first time a catalog of 23 motifs associated with subcellular localization. A significant proportion of the 3' UTR motifs is highly conserved in orthologous yeast genes, and some of the motifs are strikingly similar to recently published mammalian 3' UTR motifs. We classified all genes into those regulated only at transcription initiation level, only at degradation level, and those regulated by a combination of both. Interestingly, different biological functionalities and expression patterns correspond to such classification.

Conclusion

The present motif catalogs are a first step towards the understanding of the regulation of mRNA degradation and subcellular localization, two important processes which - together with transcription regulation - determine the cell transcriptome.

Reconstructing the metabolic network of a bacterium from its genome

The prospect of understanding the relationship between the genome and the physiology of an organism is an important incentive to reconstruct metabolic networks. The first steps in the process can be automated and it does not take much effort to obtain an initial metabolic reconstruction from a genome sequence. However, such a reconstruction is certainly not flawless and correction of the many imperfections is laborious. It requires the combined analysis of the available information on protein sequence, phylogeny, gene-context and co-occurrence but is also aided by high-throughput experimental data. Simultaneously, the reconstructed network provides the opportunity to visualize the "omics" data within a relevant biological functional context and thus aids the interpretation of those data.

Operon formation is driven by co-regulation and not by horizontal gene transfer

The organization of bacterial genes into operons was originally ascribed to the benefits of co-regulation. More recently, the "selfish operon" model, in which operons are formed by repeated gain and loss of genes, was proposed. Indeed, operons are often subject to horizontal gene transfer (HGT). On the other hand, non-HGT genes are particularly likely to be in operons. To clarify whether HGT is involved in operon formation, we identified recently formed operons in Escherichia coli K12. We show that genes that have homologs in distantly related bacteria but not in close relatives of E. coli--indicating HGT--form new operons at about the same rates as native genes. Furthermore, genes in new operons are no more likely than other genes to have phylogenetic trees that are inconsistent with the species tree. In contrast, essential genes and ubiquitous genes without paralogs--genes believed to undergo HGT rarely--often form new operons. We conclude that HGT is not a cause of operon formation but instead promotes the prevalence of pre-existing operons. To explain operon formation, we propose that new operons reduce the amount of regulatory information required to specify optimal expression patterns and infer that operons should be more likely to evolve than independent promoters when regulation is complex. Consistent with this hypothesis, operons have greater amounts of conserved regulatory sequences than do individually transcribed genes.

Decoding cis-regulatory systems in ascidians

Ascidians, or sea squirts, are lower chordates, and share basic gene repertoires and many characteristics, both developmental and physiological, with vertebrates. Therefore, decoding cis-regulatory systems in ascidians will contribute toward elucidating the genetic regulatory systems underlying the developmental and physiological processes of vertebrates. cis-Regulatory DNAs can also be used for tissue-specific genetic manipulation, a powerful tool for studying ascidian development and physiology. Because the ascidian genome is compact compared with vertebrate genomes, both intergenic regions and introns are relatively small in ascidians. Short upstream intergenic regions contain a complete set of cis-regulatory elements for spatially regulated expression of a majority of ascidian genes. These features of the ascidian genome are a great advantage in identifying cis-regulatory sequences and in analyzing their functions. Function of cis-regulatory DNAs has been analyzed for a number of tissue-specific and developmentally regulated genes of ascidians by introducing promoter-reporter fusion constructs into ascidian embryos. The availability of the whole genome sequences of the two Ciona species, Ciona intestinalis and Ciona savignyi, facilitates comparative genomics approaches to identify cis-regulatory DNAs. Recent studies demonstrate that computational methods can help identify cis-regulatory elements in the ascidian genome. This review presents a comprehensive list of ascidian genes whose cis-regulatory regions have been subjected to functional analysis, and highlights the recent advances in bioinformatics and comparative genomics approaches to cis-regulatory systems in ascidians.

Anomalies de la transcription et diagnostic en génétique constitutionnelle

There is a rapidly growing literature on transcription abnormalities, e.g. differential expression of alleles and the role of some single nucleotide polymorphisms in altering splicing patterns. An average 10% of splicing mutations is reported in the Human Gene Mutation Database but this figure could climb to 50% for some genes such as NF1 or ATM. This paper therefore aims at clarifying some important aspects of transcriptional abnormalities in genetic testing. The main types of alterations are presented, i.e. exonic, intronic and promoter modifications that could modify or create consensus motif and/or secondary structures. DNA, RNA based-diagnostic strategies and in silico tools are then presented and their performances and limitations outlined to build up a picture of the current state of the art.

Computational discovery of DNA motifs associated with cell type-specific gene expression in Ciona

Temporally and spatially co-expressed genes are expected to be regulated by common transcription factors and therefore to share cis-regulatory elements. In the ascidian Ciona intestinalis, the whole-genome sequences and genome-scale gene expression profiles allow the use of computational techniques to investigate cis-elements that control transcription. We collected 5' flanking sequences of 50 tissue-specific genes from genome databases of C. intestinalis and a closely related species Ciona savignyi. We searched for DNA motifs over-represented in upstream regions of a group of co-expressed genes. Several motifs were distributed predominantly in upstream regions of photoreceptor, pan-neuronal, or muscle-specific gene groups. One muscle-specific motif, M2, was distributed preferentially in regions from -200 to -100 bp relative to the translational start sites. Promoters of muscle-specific genes of C. intestinalis were isolated, connected with a green fluorescent protein gene (GFP), and introduced into C. intestinalis embryos. In muscle cells, these promoters specifically drove GFP expression, which mutations of the M2 sites greatly reduced. When M2 sites were located upstream of a basal promoter, the reporter GFP was specifically expressed in muscle cells. These results suggest the validity of our computational prediction of cis-regulatory elements. Thus, bioinformatics can help identify cis-regulatory elements involved in chordate development.

[Prediction of transcription and genomic sequences]

Technological developments have enhanced DNA sequencing at genomic scale. On the basis of the resulting sequences, computational biologists now attempt to localise the most important functional regions, starting with genes, but also importantly the regulatory motifs and conditions controlling their expression. In a recent paper published in Cell, M.A. Beer and S. Tavazoie report the results obtained by combining statistical classifications (clustering) of transcriptome data (DNA chips), software for the discovery of cis-regulatory patterns, together with a probabilistic learning method to infer regulatory rules tentatively accounting for the observed transcriptional profiles.

Extending the classification of bacterial transcription factors beyond the helix-turn-helix motif as an alternative approach to discover new cis/trans relationships

Transcription factors (TFs) of bacterial helix-turn-helix superfamilies exhibit different effector-binding domains (EBDs) fused to a DNA-binding domain with a common feature. In a previous study of the GntR superfamily, we demonstrated that classifying members into subfamilies according to the EBD heterogeneity highlighted unsuspected and accurate TF-binding site signatures. In this work, we present how such in silico analysis can provide prediction tools to discover new cis/trans relationships. The TF-binding site consensus of the HutC/GntR subfamily was used to (i) predict target sites within the Streptomyces coelicolor genome, (ii) discover a new HutC/GntR regulon and (iii) discover its specific TF. By scanning the S.coelicolor genome we identified a presumed new HutC regulon that comprises genes of the phosphotransferase system (PTS) specific for the uptake of N-acetylglucosamine (PTS(Nag)). A weight matrix was derived from the compilation of the predicted cis-acting elements upstream of each gene of the presumed regulon. Under the assumption that TFs are often subject to autoregulation, we used this matrix to scan the upstream region of the 24 HutC-like members of S.coelicolor. orf SCO5231 (dasR) was selected as the best candidate according to the high score of a 16 bp sequence identified in its upstream region. Our prediction that DasR regulates the PTS(Nag) regulon was confirmed by in vivo and in vitro experiments. In conclusion, our in silico approach permitted to highlight the specific TF of a regulon out of the 673 orfs annotated as 'regulatory proteins' within the genome of S.coelicolor.

Genome2D: a visualization tool for the rapid analysis of bacterial transcriptome data

Genome2D is a software tool that enables bacterial transcriptome data to be visualized on chromosome maps.

Genome2D is a Windows-based software tool for visualization of bacterial transcriptome and customized datasets on linear chromosome maps constructed from annotated genome sequences. Genome2D facilitates the analysis of transcriptome data by using different color ranges to depict differences in gene-expression levels on a genome map. Such output format enables visual inspection of the transcriptome data, and will quickly reveal transcriptional units, without prior knowledge of expression level cutoff values. The compiled version of Genome2D is freely available for academic or non-profit use from .

Computational prediction of transcription-factor binding site locations

Various experimental and computational approaches have been used to identify genomic locations of transcription-factor binding sites; methods involving computational comparisons of related genomes have been particularly successful.

Identifying genomic locations of transcription-factor binding sites, particularly in higher eukaryotic genomes, has been an enormous challenge. Various experimental and computational approaches have been used to detect these sites; methods involving computational comparisons of related genomes have been particularly successful.