RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12

Genomic SELEX Reveals Pervasive Role of the Flagella Master Regulator FlhDC in Carbon Metabolism

Flagella are vital bacterial organs that allow microorganisms to move to favorable environments. However, their construction and operation consume a large amount of energy. The master regulator FlhDC mediates all flagellum-forming genes in E. coli through a transcriptional regulatory cascade, the details of which remain elusive. In this study, we attempted to uncover a direct set of target genes in vitro using gSELEX-chip screening to re-examine the role of FlhDC in the entire E. coli genome regulatory network. We identified novel target genes involved in the sugar utilization phosphotransferase system, sugar catabolic pathway of glycolysis, and other carbon source metabolic pathways in addition to the known flagella formation target genes. Examining FlhDC transcriptional regulation in vitro and in vivo and its effects on sugar consumption and cell growth suggested that FlhDC activates these new targets. Based on these results, we proposed that the flagella master transcriptional regulator FlhDC acts in the activation of a set of flagella-forming genes, sugar utilization, and carbon source catabolic pathways to provide coordinated regulation between flagella formation, operation and energy production.

Systematic analysis of the underlying genomic architecture for transcriptional-translational coupling in prokaryotes

Transcriptional-translational coupling is accepted to be a fundamental mechanism of gene expression in prokaryotes and therefore has been analyzed in detail. However, the underlying genomic architecture of the expression machinery has not been well investigated so far. In this study, we established a bioinformatics pipeline to systematically investigated >1800 bacterial genomes for the abundance of transcriptional and translational associated genes clustered in distinct gene cassettes. We identified three highly frequent cassettes containing transcriptional and translational genes, i.e. rplk-nusG (gene cassette 1; in 553 genomes), rpoA-rplQ-rpsD-rpsK-rpsM (gene cassette 2; in 656 genomes) and nusA-infB (gene cassette 3; in 877 genomes). Interestingly, each of the three cassettes harbors a gene (nusG, rpsD and nusA) encoding a protein which links transcription and translation in bacteria. The analyses suggest an enrichment of these cassettes in pathogenic bacterial phyla with >70% for cassette 3 (i.e. Neisseria, Salmonella and Escherichia) and >50% for cassette 1 (i.e. Treponema, Prevotella, Leptospira and Fusobacterium) and cassette 2 (i.e. Helicobacter, Campylobacter, Treponema and Prevotella). These insights form the basis to analyze the transcriptional regulatory mechanisms orchestrating transcriptional-translational coupling and might open novel avenues for future biotechnological approaches.

cAMP Receptor Protein Positively Regulates the Expression of Genes Involved in the Biosynthesis of Klebsiella oxytoca Tilivalline Cytotoxin

Klebsiella oxytoca is a resident of the human gut. However, certain K. oxytoca toxigenic strains exist that secrete the nonribosomal peptide tilivalline (TV) cytotoxin. TV is a pyrrolobenzodiazepine that causes antibiotic-associated hemorrhagic colitis (AAHC). The biosynthesis of TV is driven by enzymes encoded by the aroX and NRPS operons. In this study, we determined the effect of environmental signals such as carbon sources, osmolarity, and divalent cations on the transcription of both TV biosynthetic operons. Gene expression was enhanced when bacteria were cultivated in tryptone lactose broth. Glucose, high osmolarity, and depletion of calcium and magnesium diminished gene expression, whereas glycerol increased transcription of both TV biosynthetic operons. The cAMP receptor protein (CRP) is a major transcriptional regulator in bacteria that plays a key role in metabolic regulation. To investigate the role of CRP on the cytotoxicity of K. oxytoca, we compared levels of expression of TV biosynthetic operons and synthesis of TV in wild-type strain MIT 09-7231 and a Δcrp isogenic mutant. In summary, we found that CRP directly activates the transcription of the aroX and NRPS operons and that the absence of CRP reduced cytotoxicity of K. oxytoca on HeLa cells, due to a significant reduction in TV production. This study highlights the importance of the CRP protein in the regulation of virulence genes in enteric bacteria and broadens our knowledge on the regulatory mechanisms of the TV cytotoxin.

Single-Target Regulators Constitute the Minority Group of Transcription Factors in Escherichia coli K-12

The identification of regulatory targets of all transcription factors (TFs) is critical for understanding the entire network of genome regulation. A total of approximately 300 TFs exist in the model prokaryote Escherichia coli K-12, but the identification of whole sets of their direct targets is impossible with use of in vivo approaches. For this end, the most direct and quick approach is to identify the TF-binding sites in vitro on the genome. We then developed and utilized the gSELEX screening system in vitro for identification of more than 150 E. coli TF-binding sites along the E. coli genome. Based on the number of predicted regulatory targets, we classified E. coli K-12 TFs into four groups, altogether forming a hierarchy ranging from a single-target TF (ST-TF) to local TFs, global TFs, and nucleoid-associated TFs controlling as many as 1,000 targets. Using the collection of purified TFs and a library of genome DNA segments from a single and the same E. coli K-12, we identified here a total of 11 novel ST-TFs, CsqR, CusR, HprR, NorR, PepA, PutA, QseA, RspR, UvrY, ZraR, and YqhC. The regulation of single-target promoters was analyzed in details for the hitherto uncharacterized QseA and RspR. In most cases, the ST-TF gene and its regulatory target genes are adjacently located on the E. coli K-12 genome, implying their simultaneous transfer in the course of genome evolution. The newly identified 11 ST-TFs and the total of 13 hitherto identified altogether constitute the minority group of TFs in E. coli K-12.

Expanded roles of pyruvate-sensing PdhR in transcription regulation of the Escherichia coli K-12 genome: fatty acid catabolism and cell motility

The transcription factor PdhR has been recognized as the master regulator of the pyruvate catabolism pathway in Escherichia coli, including both NAD-linked oxidative decarboxylation of pyruvate to acetyl-CoA by PDHc (pyruvate dehydrogenase complex) and respiratory electron transport of NADH to oxygen by Ndh-CyoABCD enzymes. To identify the whole set of regulatory targets under the control of pyruvate-sensing PdhR, we performed genomic SELEX (gSELEX) screening in vitro. A total of 35 PdhR-binding sites were identified along the E. coli K-12 genome, including previously identified targets. Possible involvement of PdhR in regulation of the newly identified target genes was analysed in detail by gel shift assay, RT-qPCR and Northern blot analysis. The results indicated the participation of PdhR in positive regulation of fatty acid degradation genes and negative regulation of cell mobility genes. In fact, GC analysis indicated an increase in free fatty acids in the mutant lacking PdhR. We propose that PdhR is a bifunctional global regulator for control of a total of 16–23 targets, including not only the genes involved in central carbon metabolism but also some genes for the surrounding pyruvate-sensing cellular pathways such as fatty acid degradation and flagella formation. The activity of PdhR is controlled by pyruvate, the key node between a wide variety of metabolic pathways, including generation of metabolic energy and cell building blocks.

Single-target regulators form a minor group of transcription factors in Escherichia coli K-12

The identification of regulatory targets of all TFs is critical for understanding the entire network of the genome regulation. The lac regulon of Escherichia coli K-12 W3110 is composed of the lacZYA operon and its repressor lacI gene, and has long been recognized as the seminal model of transcription regulation in bacteria with only one highly preferred target. After the Genomic SELEX screening in vitro of more than 200 transcription factors (TFs) from E. coli K-12, however, we found that most TFs regulate multiple target genes. With respect to the number of regulatory targets, a total of these 200 E. coli TFs form a hierarchy ranging from a single target to as many as 1000 targets. Here we focus a total of 13 single-target TFs, 9 known TFs (BetI, KdpE, LacI, MarR, NanR, RpiR, TorR, UlaR and UxuR) and 4 uncharacterized TFs (YagI, YbaO, YbiH and YeaM), altogether forming only a minor group of TFs in E. coli. These single-target TFs were classified into three groups based on their functional regulation.

Prediction and large-scale analysis of primary operons in plastids reveals unique genetic features in the evolution of chloroplasts

While bacterial operons have been thoroughly studied, few analyses of chloroplast operons exist, limiting the ability to study fundamental elements of these structures and utilize them for synthetic biology. Here, we describe the creation of a plastome-specific operon database (link provided below) achieved by combining experimental tools and predictive modeling. Using a Reverse-Transcription-PCR based method and published data, we determined the transcription-state of 213 gene pairs from four plastomes of evolutionary distinct organisms. By analyzing sequence-based features computed for our dataset, we were able to highlight fundamental characteristics differentiating between operon pairs and non-operon pairs. These include an interesting tendency toward maintaining similar messenger RNA-folding profiles in operon gene pairs, a feature that failed to yield any informative separation in cyanobacteria, suggesting that it catches unique traits of operon gene expression, which have evolved post-endosymbiosis. Subsequently, we used this feature set to train a random-forest classifier for operon prediction. As our results demonstrate the ability of our predictor to obtain accurate (84%) and robust predictions on unlabeled datasets, we proceeded to building operon maps for 2018 sequenced plastids. Our database may now present new opportunities for promoting metabolic engineering and synthetic biology in chloroplasts.

The Fructose-Specific Phosphotransferase System of Klebsiella pneumoniae Is Regulated by Global Regulator CRP and Linked to Virulence and Growth

Klebsiella pneumoniae is an opportunistic pathogen, and its hypervirulent variants cause serious invasive community-acquired infections. A genomic view of K. pneumoniae NTUH-2044 for the carbohydrate phosphotransferase system (PTS) found a putative fructose PTS, namely, the Frw PTS gene cluster. The deletion mutant and the complemented mutant of frwC (KP1_1992), which encodes the putative fructose-specific enzyme IIC, were constructed, and the phenotypes were characterized. This transmembrane PTS protein is responsible for fructose utilization. frwC deletion can enhance biofilm formation and capsular polysaccharide (CPS) biosynthesis but decreases the growth rate and lethality in mice. frwC expression was repressed in the cyclic AMP receptor protein (CRP) mutant. Electrophoretic mobility shift assay showed that CRP can directly bind to the promoter of frwC These results indicated that frwC expression is controlled by CRP directly and that such regulation contributes to bacterial growth, CPS synthesis, and the virulence of the Δcrp strain. The findings help elucidate fructose metabolism and the CRP regulatory mechanism in K. pneumoniae.

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.

The whole set of the constitutive promoters recognized by four minor sigma subunits of Escherichia coli RNA polymerase

The promoter selectivity of Escherichia coli RNA polymerase (RNAP) is determined by the sigma subunit. The model prokaryote Escherichia coli K-12 contains seven species of the sigma subunit, each recognizing a specific set of promoters. For identification of the "constitutive promoters" that are recognized by each RNAP holoenzyme alone in the absence of other supporting factors, we have performed the genomic SELEX screening in vitro for their binding sites along the E. coli K-12 W3110 genome using each of the reconstituted RNAP holoenzymes and a collection of genome DNA segments of E. coli K-12. The whole set of constitutive promoters for each RNAP holoenzyme was then estimated based on the location of RNAP-binding sites. The first successful screening of the constitutive promoters was achieved for RpoD (σ70), the principal sigma for transcription of growth-related genes. As an extension, we performed in this study the screening of constitutive promoters for four minor sigma subunits, stationary-phase specific RpoS (σ38), heat-shock specific RpoH (σ32), flagellar-chemotaxis specific RpoF (σ28) and extra-cytoplasmic stress-response RpoE (σ24). The total number of constitutive promoters were: 129~179 for RpoS; 101~142 for RpoH; 34~41 for RpoF; and 77~106 for RpoE. The list of constitutive promoters were compared with that of known promoters identified in vivo under various conditions and using varieties of E. coli strains, altogether allowing the estimation of "inducible promoters" in the presence of additional supporting factors.

Killing two birds with one stone: Model plant systems as a tool to teach the fundamental concepts of gene expression while analyzing biological data

Plants are ideal systems to teach core biology concepts due to their unique physiological and developmental features. Advances in DNA sequencing technology and genomics have allowed scientists to generate genome sequences and transcriptomics data for numerous model plant species. This information is publicly available and presents a valuable tool to introduce undergraduate students to the fundamental concepts of gene expression in the context of modern quantitative biology and bioinformatics. Modern biology classrooms must provide authentic research experiences to allow developing core competencies such as scientific inquiry, critical interpretation of experimental results, and quantitative analyses of large dataset using computational approaches. Recent educational research has shown that undergraduate students struggle when connecting gene expression concepts to classic genetics, phenotypic analyses, and overall flow of biological information in living organisms, suggesting that novel approaches are necessary to enhance learning of gene expression and regulation. This review describes different strategies and resources available to instructors willing to incorporate authentic research experiences, genomic tools, and bioinformatics analyses when teaching transcriptional regulation and gene expression in undergraduate courses. A variety of laboratory exercises and pedagogy materials developed to teach gene expression using plants are discussed. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Effect of database drift on network topology and enrichment analyses: a case study for RegulonDB

RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information. Database URL: www.regulondb.ccg.unam.mx.

Transcription profile of Escherichia coli: genomic SELEX search for regulatory targets of transcription factors

Bacterial genomes are transcribed by DNA-dependent RNA polymerase (RNAP), which achieves gene selectivity through interaction with sigma factors that recognize promoters, and transcription factors (TFs) that control the activity and specificity of RNAP holoenzyme. To understand the molecular mechanisms of transcriptional regulation, the identification of regulatory targets is needed for all these factors. We then performed genomic SELEX screenings of targets under the control of each sigma factor and each TF. Here we describe the assembly of 156 SELEX patterns of a total of 116 TFs performed in the presence and absence of effector ligands. The results reveal several novel concepts: (i) each TF regulates more targets than hitherto recognized; (ii) each promoter is regulated by more TFs than hitherto recognized; and (iii) the binding sites of some TFs are located within operons and even inside open reading frames. The binding sites of a set of global regulators, including cAMP receptor protein, LeuO and Lrp, overlap with those of the silencer H-NS, suggesting that certain global regulators play an anti-silencing role. To facilitate sharing of these accumulated SELEX datasets with the research community, we compiled a database, ‘Transcription Profile of Escherichia coli’ (www.shigen.nig.ac.jp/ecoli/tec/).

Inferring gene correlation networks from transcription factor binding sites

Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining

Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user.

Transcriptional occlusion caused by overlapping promoters

RpoS (σ(38)) is required for cell survival under stress conditions, but it can inhibit growth if produced inappropriately and, consequently, its production and activity are elaborately regulated. Crl, a transcriptional activator that does not bind DNA, enhances RpoS activity by stimulating the interaction between RpoS and the core polymerase. The crl gene has two overlapping promoters, a housekeeping, RpoD- (σ(70)) dependent promoter, and an RpoN (σ(54)) promoter that is strongly up-regulated under nitrogen limitation. However, transcription from the RpoN promoter prevents transcription from the RpoD promoter, and the RpoN-dependent transcript lacks a ribosome-binding site. Thus, activation of the RpoN promoter produces a long noncoding RNA that silences crl gene expression simply by being made. This elegant and economical mechanism, which allows a near-instantaneous reduction in Crl synthesis without the need for transacting regulatory factors, restrains the activity of RpoS to allow faster growth under nitrogen-limiting conditions.

Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data

BACKGROUND

Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately.

RESULTS

In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs.

CONCLUSIONS

Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set.

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/.

Gene regulatory networks in plants: learning causality from time and perturbation

The goal of systems biology is to generate models for predicting how a system will react under untested conditions or in response to genetic perturbations. This paper discusses experimental and analytical approaches to deriving causal relationships in gene regulatory networks.

MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences

Background

Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.

Results

This paper presents a new scoring function, termed as MISCORE, for functional motif characterization and evaluation. Our MISCORE is free from: (i) any assumption on model dependency; and (ii) the use of Markov chain model for background modeling. It integrates the compositional complexity of motif instances into the function. Performance evaluations with comparison to the well-known Maximum a Posteriori (MAP) score and Information Content (IC) have shown that MISCORE has promising capabilities to separate and recognize functional DNA motifs and its instances from non-functional ones.

Conclusions

MISCORE is a fast computational tool for candidate motif characterization, evaluation and selection. It enables to embed priori known motif models for computing motif-to-motif similarity, which is more advantageous than IC and MAP score. In addition to these merits mentioned above, MISCORE can automatically filter out some repetitive k-mers from a motif model due to the introduction of the compositional complexity in the function. Consequently, the merits of our proposed MISCORE in terms of both motif signal modeling power and computational efficiency will make it more applicable in the development of computational motif discovery tools.

Transcription factor binding sites detection by using alignment-based approach

Gene expression is the main cause for the existence of various phenotypes. Through this procedure, the information stored in DNA rises to the phenotype. Essentially, gene expression is dependent upon the successful binding of transcription factors (TFs) - a specific type of proteins - to explicit positions in its upstream, TF binding sites (TFBSs). Unfortunately, finding these TFBSs is costly and laborious; therefore, discovering TFBSs computationally is a significant problem that many researches endeavor to solve. In this paper, a new TFBS discovery method is presented by considering known biological facts about TFBSs. The input to this method includes sequences with arbitrary lengths and the output comprises positions that tend to be TFBS. Through the application of previous methods along with a method that focuses on biological and simulated datasets, it is shown that this method achieves higher accuracy in discovering TFBSs.

Prokaryotic genome regulation: a revolutionary paradigm

After determination of the whole genome sequence, the research frontier of bacterial molecular genetics has shifted to reveal the genome regulation under stressful conditions in nature. The gene selectivity of RNA polymerase is modulated after interaction with two groups of regulatory proteins, 7 sigma factors and 300 transcription factors. For identification of regulation targets of transcription factors in Escherichia coli, we have developed Genomic SELEX system and subjected to screening the binding sites of these factors on the genome. The number of regulation targets by a single transcription factor was more than those hitherto recognized, ranging up to hundreds of promoters. The number of transcription factors involved in regulation of a single promoter also increased to as many as 30 regulators. The multi-target transcription factors and the multi-factor promoters were assembled into complex networks of transcription regulation. The most complex network was identified in the regulation cascades of transcription of two master regulators for planktonic growth and biofilm formation.

Extracting regulatory networks of Escherichia coli from RegulonDB

RegulonDB contains the largest and currently best-known data set on transcriptional regulation in a single free-living organism, that of Escherichia coli K-12 (Gama-Castro et al. Nucleic Acids Res 36:D120-D124, 2008). This organized knowledge has been the gold standard for the implementation of bioinformatic predictive methods on gene regulation in bacteria (Collado-Vides et al. J Bacteriol 191:23-31, 2009). Given the complexity of different types of interactions, the difficulty of visualizing in a single figure of the whole network, and the different uses of this knowledge, we are making available different views of the genetic network. This chapter describes case studies about how to access these views, via precomputed files, web services and SQL, including sigma-gene relationships corresponding to transcription of alternative RNA polymerase holoenzyme promoters; as well as, transcription factor (TF)-genes, TF-operons, TF-TF, and TF-regulon interactions. 17.

CoryneRegNet 6.0--Updated database content, new analysis methods and novel features focusing on community demands

Post-genomic analysis techniques such as next-generation sequencing have produced vast amounts of data about micro organisms including genetic sequences, their functional annotations and gene regulatory interactions. The latter are genetic mechanisms that control a cell's characteristics, for instance, pathogenicity as well as survival and reproduction strategies. CoryneRegNet is the reference database and analysis platform for corynebacterial gene regulatory networks. In this article we introduce the updated version 6.0 of CoryneRegNet and describe the updated database content which includes, 6352 corynebacterial regulatory interactions compared with 4928 interactions in release 5.0 and 3235 regulations in release 4.0, respectively. We also demonstrate how we support the community by integrating analysis and visualization features for transiently imported custom data, such as gene regulatory interactions. Furthermore, with release 6.0, we provide easy-to-use functions that allow the user to submit data for persistent storage with the CoryneRegNet database. Thus, it offers important options to its users in terms of community demands. CoryneRegNet is publicly available at http://www.coryneregnet.de.

Genome wide identification of Acidithiobacillus ferrooxidans (ATCC 23270) transcription factors and comparative analysis of ArsR and MerR metal regulators

Acidithiobacillus ferrooxidans is a chemolithoautotrophic acidophilic bacterium that obtains its energy from the oxidation of ferrous iron, elemental sulfur, or reduced sulfur minerals. This capability makes it of great industrial importance due to its applications in biomining. During the industrial processes, A. ferrooxidans survives to stressing circumstances in its environment, such as an extremely acidic pH and high concentration of transition metals. In order to gain insight into the organization of A. ferrooxidans regulatory networks and to provide a framework for further studies in bacterial growth under extreme conditions, we applied a genome-wide annotation procedure to identify 87 A. ferrooxidans transcription factors. We classified them into 19 families that were conserved among diverse prokaryotic phyla. Our annotation procedure revealed that A. ferrooxidans genome contains several members of the ArsR and MerR families, which are involved in metal resistance and detoxification. Analysis of their sequences revealed known and potentially new mechanism to coordinate gene-expression in response to metal availability. A. ferrooxidans inhabit some of the most metal-rich environments known, thus transcription factors identified here seem to be good candidates for functional studies in order to determine their physiological roles and to place them into A. ferrooxidans transcriptional regulatory networks.

PromBase: a web resource for various genomic features and predicted promoters in prokaryotic genomes

Background

As more and more genomes are being sequenced, an overview of their genomic features and annotation of their functional elements, which control the expression of each gene or transcription unit of the genome, is a fundamental challenge in genomics and bioinformatics.

Findings

Relative stability of DNA sequence has been used to predict promoter regions in 913 microbial genomic sequences with GC-content ranging from 16.6% to 74.9%. Irrespective of the genome GC-content the relative stability based promoter prediction method has already been proven to be robust in terms of recall and precision. The predicted promoter regions for the 913 microbial genomes have been accumulated in a database called PromBase. Promoter search can be carried out in PromBase either by specifying the gene name or the genomic position. Each predicted promoter region has been assigned to a reliability class (low, medium, high, very high and highest) based on the difference between its average free energy and the downstream region. The recall and precision values for each class are shown graphically in PromBase. In addition, PromBase provides detailed information about base composition, CDS and CG/TA skews for each genome and various DNA sequence dependent structural properties (average free energy, curvature and bendability) in the vicinity of all annotated translation start sites (TLS).

Conclusion

PromBase is a database, which contains predicted promoter regions and detailed analysis of various genomic features for 913 microbial genomes. PromBase can serve as a valuable resource for comparative genomics study and help the experimentalist to rapidly access detailed information on various genomic features and putative promoter regions in any given genome. This database is freely accessible for academic and non- academic users via the worldwide web http://nucleix.mbu.iisc.ernet.in/prombase/.

Inferring protein-DNA interaction parameters from SELEX experiments

Systematic Evolution of Ligands by EXponential enrichment (SELEX) is an experimental procedure that allows extraction, from an initially random pool of oligonucleotides, of the oligomers with a high binding affinity for a given molecular target. The highest affinity binding sequences isolated through SELEX can have numerous research, diagnostic, and therapeutic applications. Recently, important new modifications of the SELEX protocol have been proposed. In particular, a suitably modified SELEX experiment, together with an appropriate computational procedure, allows inference of protein-DNA interaction parameters with up to now unprecedented accuracy. Such inference is possible even when there is no a priori information on transcription factor binding specificity, which allows accurate predictions of binding sites for any transcription factor of interest. In this chapter we discuss how to accurately determine protein-DNA interaction parameters from SELEX experiments. The chapter addresses experimental and computational procedure needed to generate and analyze appropriate data.

Trimming of mammalian transcriptional networks using network component analysis

Background

Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.

Results

We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.

Conclusions

The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis

Prediction of metabolic changes that result from genetic or environmental perturbations has several important applications, including diagnosing metabolic disorders and discovering novel drug targets. A cardinal challenge in obtaining accurate predictions is the integration of transcriptional regulatory networks with the corresponding metabolic network. We propose a method called probabilistic regulation of metabolism (PROM) that achieves this synthesis and enables straightforward, automated, and quantitative integration of high-throughput data into constraint-based modeling, making it an ideal tool for constructing genome-scale regulatory-metabolic network models for less-studied organisms. PROM introduces probabilities to represent gene states and gene-transcription factor interactions. By using PROM, we constructed an integrated regulatory-metabolic network for the model organism, Escherichia coli, and demonstrated that our method based on automated inference is more accurate and comprehensive than the current state of the art, which is based on manual curation of literature. After validating the approach, we used PROM to build a genome-scale integrated metabolic-regulatory model for Mycobacterium tuberculosis, a critically important human pathogen. This study incorporated data from more than 1,300 microarrays, 2,000 transcription factor-target interactions regulating 3,300 metabolic reactions, and 1,905 KO phenotypes for E. coli and M. tuberculosis. PROM identified KO phenotypes with accuracies as high as 95%, and predicted growth rates quantitatively with correlation of 0.95. Importantly, PROM represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology models that are rarely combined quantitatively.

Defining the plasticity of transcription factor binding sites by Deconstructing DNA consensus sequences: the PhoP-binding sites among gamma/enterobacteria

Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg(2+) homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the "Divide & Conquer" strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase.

PtrA is required for coordinate regulation of gene expression during phosphate stress in a marine Synechococcus

Previous microarray analyses have shown a key role for the two-component system PhoBR (SYNW0947, SYNW0948) in the regulation of P transport and metabolism in the marine cyanobacterium Synechococcus sp. WH8102. However, there is some evidence that another regulator, SYNW1019 (PtrA), probably under the control of PhoBR, is involved in the response to P depletion. PtrA is a member of the cAMP receptor protein transcriptional regulator family that shows homology to NtcA, the global nitrogen regulator in cyanobacteria. To define the role of this regulator, we constructed a mutant by insertional inactivation and compared the physiology of wild-type Synechcococcus sp. WH8102 with the ptrA mutant under P-replete and P-stress conditions. In response to P stress the ptrA mutant failed to upregulate phosphatase activity. Microarrays and quantitative RT-PCR indicate that a subset of the Pho regulon is controlled by PtrA, including two phosphatases, a predicted phytase and a gene of unknown function psip1 (SYNW0165), all of which are highly upregulated during P limitation. Electrophoretic mobility shift assays indicate binding of overexpressed PtrA to promoter sequences upstream of the induced genes. This work suggests a two-tiered response to P depletion in this strain, the first being PhoB-dependent induction of high-affinity PO(4) transporters, and the second the PtrA-dependent induction of phosphatases for scavenging organic P. The levels of numerous other transcripts are also directly or indirectly influenced by PtrA, including those involved in cell-surface modification, metal uptake, photosynthesis, stress responses and other metabolic processes, which may indicate a wider role for PtrA in cellular regulation in marine picocyanobacteria.

The Motif Tool Assessment Platform (MTAP) for sequence-based transcription factor binding site prediction tools

Predicting transcription factor binding sites (TFBS) from sequence is one of the most challenging problems in computational biology. The development of (semi-)automated computer-assisted prediction methods is needed to find TFBS over an entire genome, which is a first step in reconstructing mechanisms that control gene activity. Bioinformatics journals continue to publish diverse methods for predicting TFBS on a monthly basis. To help practitioners in deciding which method to use to predict for a particular TFBS, we provide a platform to assess the quality and applicability of the available methods. Assessment tools allow researchers to determine how methods can be expected to perform on specific organisms or on specific transcription factor families. This chapter introduces the TFBS detection problem and reviews current strategies for evaluating algorithm effectiveness. In this chapter, a novel and robust assessment tool, the Motif Tool Assessment Platform (MTAP), is introduced and discussed.

Promoter and regulon analysis of nitrogen assimilation factor, sigma54, reveal alternative strategy for E. coli MG1655 flagellar biosynthesis

Bacteria core RNA polymerase (RNAP) must associate with a σ factor to recognize promoter sequences. Promoters recognized by the σ⁵⁴ (or σ^N) associated RNA polymerase are unique in having conserved positions around −24 and −12 nucleotides upstream from the transcriptional start site. Using DNA microarrays representing the entire Escherichia coli genome and promoter validation approaches, we identify 40 in vivo targets of σ⁵⁴, the nitrogen assimilation σ factor, and estimate that there are 70 σ⁵⁴ promoters in total. Immunoprecipitation assays have been performed to further evaluate the efficiency of our approaches. In addition, promoter consensus binding search and primer extension assay helped us to identify a new σ⁵⁴ promoter carried by insB-5 in the upstream of flhDC operon. The involvement of σ⁵⁴ in flagellar biosynthesis in sequenced E. coli strain MG1655 indicates a fluid gene regulation phenomenon carried by some mobile elements in bacteria genome.

iTriplet, a rule-based nucleic acid sequence motif finder

Background

With the advent of high throughput sequencing techniques, large amounts of sequencing data are readily available for analysis. Natural biological signals are intrinsically highly variable making their complete identification a computationally challenging problem. Many attempts in using statistical or combinatorial approaches have been made with great success in the past. However, identifying highly degenerate and long (>20 nucleotides) motifs still remains an unmet challenge as high degeneracy will diminish statistical significance of biological signals and increasing motif size will cause combinatorial explosion. In this report, we present a novel rule-based method that is focused on finding degenerate and long motifs. Our proposed method, named iTriplet, avoids costly enumeration present in existing combinatorial methods and is amenable to parallel processing.

Results

We have conducted a comprehensive assessment on the performance and sensitivity-specificity of iTriplet in analyzing artificial and real biological sequences in various genomic regions. The results show that iTriplet is able to solve challenging cases. Furthermore we have confirmed the utility of iTriplet by showing it accurately predicts polyA-site-related motifs using a dual Luciferase reporter assay.

Conclusion

iTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods. In addition, iTriplet is distinguished from other methods of the same family by its parallelizability, which allows it to leverage the power of today's readily available high-performance computing systems.

Network-based multiple locus linkage analysis of expression traits

MOTIVATION

We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as specified a priori by a gene pathway or network.

RESULTS

An analysis of a mouse dataset and simulation studies clearly demonstrate the advantage of the proposed method over a standard approach that ignores biological knowledge of gene networks.

Toolbox model of evolution of prokaryotic metabolic networks and their regulation

It has been reported that the number of transcription factors encoded in prokaryotic genomes scales approximately quadratically with their total number of genes. We propose a conceptual explanation of this finding and illustrate it using a simple model in which metabolic and regulatory networks of prokaryotes are shaped by horizontal gene transfer of coregulated metabolic pathways. Adapting to a new environmental condition monitored by a new transcription factor (e.g., learning to use another nutrient) involves both acquiring new enzymes and reusing some of the enzymes already encoded in the genome. As the repertoire of enzymes of an organism (its toolbox) grows larger, it can reuse its enzyme tools more often and thus needs to get fewer new ones to master each new task. From this observation, it logically follows that the number of functional tasks and their regulators increases faster than linearly with the total number of genes encoding enzymes. Genomes can also shrink, e.g., because of a loss of a nutrient from the environment, followed by deletion of its regulator and all enzymes that become redundant. We propose several simple models of network evolution elaborating on this toolbox argument and reproducing the empirically observed quadratic scaling. The distribution of lengths of pathway branches in our model agrees with that of the real-life metabolic network of Escherichia coli. Thus, our model provides a qualitative explanation for broad distributions of regulon sizes in prokaryotes.

The genome sequence of Geobacter metallireducens: features of metabolism, physiology and regulation common and dissimilar to Geobacter sulfurreducens

Background

The genome sequence of Geobacter metallireducens is the second to be completed from the metal-respiring genus Geobacter, and is compared in this report to that of Geobacter sulfurreducens in order to understand their metabolic, physiological and regulatory similarities and differences.

Results

The experimentally observed greater metabolic versatility of G. metallireducens versus G. sulfurreducens is borne out by the presence of more numerous genes for metabolism of organic acids including acetate, propionate, and pyruvate. Although G. metallireducens lacks a dicarboxylic acid transporter, it has acquired a second putative succinate dehydrogenase/fumarate reductase complex, suggesting that respiration of fumarate was important until recently in its evolutionary history. Vestiges of the molybdate (ModE) regulon of G. sulfurreducens can be detected in G. metallireducens, which has lost the global regulatory protein ModE but retained some putative ModE-binding sites and multiplied certain genes of molybdenum cofactor biosynthesis. Several enzymes of amino acid metabolism are of different origin in the two species, but significant patterns of gene organization are conserved. Whereas most Geobacteraceae are predicted to obtain biosynthetic reducing equivalents from electron transfer pathways via a ferredoxin oxidoreductase, G. metallireducens can derive them from the oxidative pentose phosphate pathway. In addition to the evidence of greater metabolic versatility, the G. metallireducens genome is also remarkable for the abundance of multicopy nucleotide sequences found in intergenic regions and even within genes.

Conclusion

The genomic evidence suggests that metabolism, physiology and regulation of gene expression in G. metallireducens may be dramatically different from other Geobacteraceae.

A universal operon predictor for prokaryotic genomes

Identification of operons at the genome scale of prokaryotic organisms represents a key step in deciphering of their transcriptional regulation machinery, biological pathways, and networks. While numerous computational methods have been shown to be effective in predicting operons for well-studied organisms such as Escherichia coli K12 and Bacillus subtilis 168, these methods generally do not generalize well to genomes other than the ones used to train the methods, or closely related genomes because they rely on organism-specific information. Several methods have been explored to address this problem through utilizing only genomic structural information conserved across multiple organisms, but they all suffer from the issue of low prediction sensitivity. In this paper, we report a novel operon prediction method that is applicable to any prokaryotic genome with high prediction accuracy. The key idea of the method is to predict operons through identification of conserved gene clusters across multiple genomes and through deriving a key parameter relevant to the distribution of intergenic distances in genomes. We have implemented this method using a graph-theoretic approach, to calculate a set of maximum gene clusters in the target genome that are conserved across multiple reference genomes. Our computational results have shown that this method has higher prediction sensitivity as well as specificity than most of the published methods. We have carried out a preliminary study on operons unique to archaea and bacteria, respectively, and derived a number of interesting new insights about operons between these two kingdoms. The software and predicted operons of 365 prokaryotic genomes are available at http://csbl.bmb.uga.edu/~dongsheng/UNIPOP.

Fis negatively affects binding of Tn4652 transposase by out-competing IHF from the left end of Tn4652

Transposition activity in bacteria is generally maintained at a low level. The activity of mobile DNA elements can be controlled by bacterially encoded global regulators. Regulation of transposition of Tn4652 in Pseudomonas putida is one such example. Activation of transposition of Tn4652 in starving bacteria requires the stationary-phase sigma factor RpoS and integration host factor (IHF). IHF plays a dual role in Tn4652 translocation by activating transcription of the transposase gene tnpA of the transposon and facilitating TnpA binding to the inverted repeats of the transposon. Our previous results have indicated that besides IHF some other P. putida-encoded global regulator(s) might bind to the ends of Tn4652 and regulate transposition activity. In this study, employing a DNase I footprint assay we have identified a binding site of P. putida Fis (factor for inversion stimulation) centred 135 bp inside the left end of Tn4652. Our results of gel mobility shift and DNase I footprint studies revealed that Fis out-competes IHF from the left end of Tn4652, thereby abolishing the binding of TnpA. Thus, the results obtained in this study indicate that the transposition of Tn4652 is regulated by the cellular amount of P. putida global regulators Fis and IHF.

Identifying promoter features of co-regulated genes with similar network motifs

Background

A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.

Results

We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif.

Conclusion

Global transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or network motif, but also the cis-acting elements participating in each of these connexions.

Computational prediction of cAMP receptor protein (CRP) binding sites in cyanobacterial genomes

Background

Cyclic AMP receptor protein (CRP), also known as catabolite gene activator protein (CAP), is an important transcriptional regulator widely distributed in many bacteria. The biological processes under the regulation of CRP are highly diverse among different groups of bacterial species. Elucidation of CRP regulons in cyanobacteria will further our understanding of the physiology and ecology of this important group of microorganisms. Previously, CRP has been experimentally studied in only two cyanobacterial strains: Synechocystis sp. PCC 6803 and Anabaena sp. PCC 7120; therefore, a systematic genome-scale study of the potential CRP target genes and binding sites in cyanobacterial genomes is urgently needed.

Results

We have predicted and analyzed the CRP binding sites and regulons in 12 sequenced cyanobacterial genomes using a highly effective cis-regulatory binding site scanning algorithm. Our results show that cyanobacterial CRP binding sites are very similar to those in E. coli; however, the regulons are very different from that of E. coli. Furthermore, CRP regulons in different cyanobacterial species/ecotypes are also highly diversified, ranging from photosynthesis, carbon fixation and nitrogen assimilation, to chemotaxis and signal transduction. In addition, our prediction indicates that crp genes in modern cyanobacteria are likely inherited from a common ancestral gene in their last common ancestor, and have adapted various cellular functions in different environments, while some cyanobacteria lost their crp genes as well as CRP binding sites during the course of evolution.

Conclusion

The CRP regulons in cyanobacteria are highly diversified, probably as a result of divergent evolution to adapt to various ecological niches. Cyanobacterial CRPs may function as lineage-specific regulators participating in various cellular processes, and are important in some lineages. However, they are dispensable in some other lineages. The loss of CRPs in these species leads to the rapid loss of their binding sites in the genomes.

Modular gene expression in Poplar: a multilayer network approach

By applying a multilayer network approach to an extensive set of Poplar microarray data, a genome-wide coexpression network has been detected and explored. Multilayer networks were generated from minimum spanning trees (MSTs) using Kruskal's algorithm from random jack-knife resamplings of half of the full data set. The final network is obtained from the union of all the generated MSTs. The gene expression correlations display a highly clustered topology, which is more pronounced when introducing links appearing in relatively few of the generated MSTs. The network also reveals a modular architecture, reflecting functional groups with relatively frequent gene-to-gene communication. Furthermore, the observed modular structure overlaps with different gene activities in different tissues, and closely related tissues show similar over- and/or under-expression patterns at the modular scale. It is shown that including links that appear in a few of the generated MSTs increases the information quality of the network. In other words, a link may be 'weak' because it reflects rare signaling events rather than merely a signal weakened by noise. The method allows, from comparisons of random 'null networks', tuning to maximize the information obtainable.

Performance evaluation of DNA motif discovery programs

Methods for the identification of transcription factor binding sites have proved to be useful for deciphering genetic regulatory networks. The strengths and weaknesses for a number of available web tools are not fully understood. Here, we designed a comprehensive set of performance measures and benchmarked sequence-based motif discovery tools using large scale datasets (derived from Escherichia coli genome and RegulonDB database). The benchmark study showed that nucleotide based and binding site based prediction accuracy is often low and activator binding site based prediction accuracy is high.