Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli

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.

Deciphering the transcriptional regulatory logic of amino acid metabolism

Although metabolic networks have been reconstructed on a genome scale, the corresponding reconstruction and integration of governing transcriptional regulatory networks has not been fully achieved. Here we reconstruct such an integrated network for amino acid metabolism in Escherichia coli. Analysis of ChIP-chip and gene expression data for the transcription factors ArgR, Lrp and TrpR showed that 19 out of 20 amino acid biosynthetic pathways are either directly or indirectly controlled by these regulators. Classifying the regulated genes into three functional categories of transport, biosynthesis and metabolism leads to the elucidation of regulatory motifs that constitute the integrated network's basic building blocks. The regulatory logic of these motifs was determined on the basis of relationships between transcription factor binding and changes in the amount of transcript in response to exogenous amino acids. Remarkably, the resulting logic shows how amino acids are differentiated as signaling and nutrient molecules, revealing the overarching regulatory principles of the amino acid stimulon.

Newly identified genetic variations in common Escherichia coli MG1655 stock cultures

We have recently identified seven mutations in commonly used stocks of the sequenced Escherichia coli strain MG1655 which do not appear in the reference sequence. The mutations are likely to cause loss of function of the glpR and crl genes, which may have serious implications for physiological experiments using the affected strains.

Role of ArgP (IciA) in lysine-mediated repression in Escherichia coli

Initially identified as an inhibitor of oriC-initiated DNA replication in vitro, the ArgP or IciA protein of Escherichia coli has subsequently been described as a nucleoid-associated protein and also as a transcriptional regulator of genes involved in DNA replication (dnaA and nrdA) and amino acid metabolism (argO, dapB, and gdhA [the last in Klebsiella pneumoniae]). ArgP mediates lysine (Lys) repression of argO, dapB, and gdhA in vivo, for which two alternative mechanisms have been identified: at the dapB and gdhA regulatory regions, ArgP binding is reduced upon the addition of Lys, whereas at argO, RNA polymerase is trapped at the step of promoter clearance by Lys-bound ArgP. In this study, we have examined promoter-lac fusions in strains that were argP(+) or ΔargP or that were carrying dominant argP mutations in order to identify several new genes that are ArgP-regulated in vivo, including lysP, lysC, lysA, dapD, and asd (in addition to argO, dapB, and gdhA). All were repressed upon Lys supplementation, and in vitro studies demonstrated that ArgP binds to the corresponding regulatory regions in a Lys-sensitive manner (with the exception of argO, whose binding to ArgP was Lys insensitive). Neither dnaA nor nrdA was ArgP regulated in vivo, although their regulatory regions exhibited low-affinity binding to ArgP. Our results suggest that ArgP is a transcriptional regulator for Lys repression of genes in E. coli but that it is noncanonical in that it also exhibits low-affinity binding, without apparent direct regulatory effect, to a number of additional sites in the genome.

Deciphering the regulatory codes in bacterial genomes

Interactions between cis-regulatory elements and trans-acting factors are fundamental for cellular functions such as transcription. With the revolution in microarrays and sequencing technologies, genome-wide binding locations of trans-acting factors are being determined in large numbers. The richness of the genome-scale information has revealed that the nature of the bacterial transcriptome and regulome are considerably more complex than previously expected. In addition, the emerging view of the bacterial transcriptome is revising the concept of the operon organization of the genome. This review describes current advances in the genome-scale analysis of the interaction between cis-regulatory elements and trans-acting factors in microorganisms.

Microbial laboratory evolution in the era of genome-scale science

Advances in DNA sequencing, high-throughput technologies, and genetic manipulation systems have enabled empirical studies of the molecular and genomic bases of adaptive evolution. This review discusses key insights learned from direct observation of the evolution process.

Laboratory evolution studies provide fundamental biological insight through direct observation of the evolution process. They not only enable testing of evolutionary theory and principles, but also have applications to metabolic engineering and human health. Genome-scale tools are revolutionizing studies of laboratory evolution by providing complete determination of the genetic basis of adaptation and the changes in the organism's gene expression state. Here, we review studies centered on four central themes of laboratory evolution studies: (1) the genetic basis of adaptation; (2) the importance of mutations to genes that encode regulatory hubs; (3) the view of adaptive evolution as an optimization process; and (4) the dynamics with which laboratory populations evolve.

Genome-wide identification of in vivo binding sites of GlxR, a cyclic AMP receptor protein-type regulator in Corynebacterium glutamicum

Corynebacterium glutamicum GlxR is a cyclic AMP (cAMP) receptor protein-type regulator. Although over 200 GlxR-binding sites in the C. glutamicum genome are predicted in silico, studies on the physiological function of GlxR have been hindered by the severe growth defects of a glxR mutant. This study identified the GlxR regulon by chromatin immunoprecipitation in conjunction with microarray (ChIP-chip) analyses. In total, 209 regions were detected as in vivo GlxR-binding sites. In vitro binding assays and promoter-reporter assays demonstrated that GlxR directly activates expression of genes for aerobic respiration, ATP synthesis, and glycolysis and that it is required for expression of genes for cell separation and mechanosensitive channels. GlxR also directly represses a citrate uptake gene in the presence of citrate. Moreover, ChIP-chip analyses showed that GlxR was still able to interact with its target sites in a mutant with a deletion of cyaB, the sole adenylate cyclase gene in the genome, even though binding affinity was markedly decreased. Thus, GlxR is physiologically functional at the relatively low cAMP levels in the cyaB mutant, allowing the cyaB mutant to grow much better than the glxR mutant.

Recognition of DNA by the helix-turn-helix global regulatory protein Lrp is modulated by the amino terminus

The AsnC/Lrp family of regulatory proteins links bacterial and archaeal transcription patterns to metabolism. In Escherichia coli, Lrp regulates approximately 400 genes, over 200 of them directly. In earlier studies, lrp genes from Vibrio cholerae, Proteus mirabilis, and E. coli were introduced into the same E. coli background and yielded overlapping but significantly different regulons. These differences were seen despite amino acid sequence identities of 92% (Vibrio) and 98% (Proteus) to E. coli Lrp, including complete conservation of the helix-turn-helix motifs. The N-terminal region contains many of the sequence differences among these Lrp orthologs, which led us to investigate its role in Lrp function. Through the generation of hybrid proteins, we found that the N-terminal diversity is responsible for some of the differences between orthologs in terms of DNA binding (as revealed by mobility shift assays) and multimerization (as revealed by gel filtration, dynamic light scattering, and analytical ultracentrifugation). These observations indicate that the N-terminal tail plays a significant role in modulating Lrp function, similar to what is seen for a number of other regulatory proteins.

Regulation and control of metabolic fluxes in microbes

After about ten years of research renaissance in metabolism, the present challenge is to understand how metabolic fluxes are controlled by a complex interplay of overlapping regulatory mechanisms. Reconstruction of various regulatory network topologies is steaming, illustrating that we underestimated the broad importance of post-translational modifications such as enzyme phosphorylation or acetylation for microbial metabolism. With the growing topological knowledge, the functional relevance of these regulatory events becomes an even more pressing need. A major knowledge gap resides in the regulatory network of protein-metabolite interactions, simply because we lacked pertinent methods for systematic analyses - but a start has now been made. Perhaps most dramatic was the conceptual shift in our perception of metabolism from an engine of cellular operation to a generator of input and feedback signals for regulatory circuits that govern many important decisions on cell proliferation, differentiation, death, and naturally metabolism.

The PurR regulon in Escherichia coli K-12 MG1655

The PurR transcription factor plays a critical role in transcriptional regulation of purine metabolism in enterobacteria. Here, we elucidate the role of PurR under exogenous adenine stimulation at the genome-scale using high-resolution chromatin immunoprecipitation (ChIP)–chip and gene expression data obtained under in vivo conditions. Analysis of microarray data revealed that adenine stimulation led to changes in transcript level of about 10% of Escherichia coli genes, including the purine biosynthesis pathway. The E. coli strain lacking the purR gene showed that a total of 56 genes are affected by the deletion. From the ChIP–chip analysis, we determined that over 73% of genes directly regulated by PurR were enriched in the biosynthesis, utilization and transport of purine and pyrimidine nucleotides, and 20% of them were functionally unknown. Compared to the functional diversity of the regulon of the other general transcription factors in E. coli, the functions and size of the PurR regulon are limited.

Identification of ArgP and Lrp as transcriptional regulators of lysP, the gene encoding the specific lysine permease of Escherichia coli

Expression of lysP, which encodes the lysine-specific transporter LysP in Escherichia coli, is regulated by the concentration of exogenous available lysine. In this study, the LysR-type transcriptional regulator ArgP was identified as the activator of lysP expression. At lysine concentrations higher than 25 μM, lysP expression was shut off and phenocopied an argP deletion mutant. Purified ArgP-His(6) bound to the lysP promoter/control region at a sequence containing a conserved T-N(11)-A motif. Its affinity increased in the presence of lysine but not in the presence of the other known coeffector, arginine. In vivo data suggest that lysine-loaded ArgP and arginine-loaded ArgP compete at the lysP promoter. We propose that lysine-loaded ArgP prevents lysP transcription at the promoter clearance step, as described for the lysine-dependent regulation of argO (R. S. Laishram and J. Gowrishankar, Genes Dev. 21:1258-1272, 2007). The global regulator Lrp also bound to the lysP promoter/control region. An lrp mutant exhibited reduced lysP expression in the absence of external lysine. These results indicate that ArgP is a major regulator of lysP expression but that Lrp modulates lysP transcription under lysine-limiting conditions.

Distribution of genes encoding nucleoid-associated protein homologs in plasmids

Bacterial nucleoid-associated proteins (NAPs) form nucleoprotein complexes and influence the expression of genes. Recent studies have shown that some plasmids carry genes encoding NAP homologs, which play important roles in transcriptional regulation networks between plasmids and host chromosomes. In this study, we determined the distributions of the well-known NAPs Fis, H-NS, HU, IHF, and Lrp and the newly found NAPs MvaT and NdpA among the whole-sequenced 1382 plasmids found in Gram-negative bacteria. Comparisons between NAP distributions and plasmid features (size, G+C content, and putative transferability) were also performed. We found that larger plasmids frequently have NAP gene homologs. Plasmids with H-NS gene homologs had less G+C content. It should be noted that plasmids with the NAP gene homolog also carried the relaxase gene involved in the conjugative transfer of plasmids more frequently than did those without the NAP gene homolog, implying that plasmid-encoded NAP homologs positively contribute to transmissible plasmids.

Deep sequencing-based analysis of the anaerobic stimulon in Neisseria gonorrhoeae

BACKGROUND

Maintenance of an anaerobic denitrification system in the obligate human pathogen, Neisseria gonorrhoeae, suggests that an anaerobic lifestyle may be important during the course of infection. Furthermore, mounting evidence suggests that reduction of host-produced nitric oxide has several immunomodulary effects on the host. However, at this point there have been no studies analyzing the complete gonococcal transcriptome response to anaerobiosis. Here we performed deep sequencing to compare the gonococcal transcriptomes of aerobically and anaerobically grown cells. Using the information derived from this sequencing, we discuss the implications of the robust transcriptional response to anaerobic growth.

RESULTS

We determined that 198 chromosomal genes were differentially expressed (~10% of the genome) in response to anaerobic conditions. We also observed a large induction of genes encoded within the cryptic plasmid, pJD1. Validation of RNA-seq data using translational-lacZ fusions or RT-PCR demonstrated the RNA-seq results to be very reproducible. Surprisingly, many genes of prophage origin were induced anaerobically, as well as several transcriptional regulators previously unknown to be involved in anaerobic growth. We also confirmed expression and regulation of a small RNA, likely a functional equivalent of fnrS in the Enterobacteriaceae family. We also determined that many genes found to be responsive to anaerobiosis have also been shown to be responsive to iron and/or oxidative stress.

CONCLUSIONS

Gonococci will be subject to many forms of environmental stress, including oxygen-limitation, during the course of infection. Here we determined that the anaerobic stimulon in gonococci was larger than previous studies would suggest. Many new targets for future research have been uncovered, and the results derived from this study may have helped to elucidate factors or mechanisms of virulence that may have otherwise been overlooked.

An overview of prokaryotic transcription factors : a summary of function and occurrence in bacterial genomes

Transcriptional initiation is arguably the most important control point for gene expression. It is regulated by a combination of factors, including DNA sequence and its three-dimensional topology, proteins and small molecules. In this chapter, we focus on the trans-acting factors of bacterial regulation. Initiation begins with the recruitment of the RNA polymerase holoenzyme to a specific locus upstream of the gene known as its promoter. The sigma factor, which is a component of the holoenzyme, provides the most fundamental mechanisms for orchestrating broad changes in gene expression state. It is responsible for promoter recognition as well as recruiting the holoenzyme to the promoter. Distinct sigma factors compete with for binding to a common pool of RNA polymerases, thus achieving condition-dependent differential expression. Another important class of bacterial regulators is transcription factors, which activate or repress transcription of target genes typically in response to an environmental or cellular trigger. These factors may be global or local depending on the number of genes and range of cellular functions that they target. The activities of both global and local transcription factors may be regulated either at a post-transcriptional level via signal-sensing protein domains or at the level of their own expression. In addition to modulating polymerase recruitment to promoters, several global factors are considered as "nucleoid-associated proteins" that impose structural constraints on the chromosome by altering the conformation of the bound DNA, thus influencing other processes involving DNA such as replication and recombination. This chapter concludes with a discussion of how regulatory interactions between transcription factors and their target genes can be represented as a network.

Discretely calibrated regulatory loops controlled by ppGpp partition gene induction across the 'feast to famine' gradient in Escherichia coli

Bacteria comprehensively reorganize their global gene expression when faced with starvation. The alarmone ppGpp facilitates this massive response by co-ordinating the downregulation of genes of the translation apparatus, and the induction of biosynthetic genes and the general stress response. Such a large reorientation requires the activities of multiple regulators, yet the regulatory network downstream of ppGpp remains poorly defined. Transcription profiling during isoleucine depletion, which leads to gradual starvation (over > 100 min), allowed us to identify genes that required ppGpp, Lrp and RpoS for their induction and to deduce the regulon response times. Although the Lrp and RpoS regulons required ppGpp for their activation, they were not induced simultaneously. The data suggest that metabolic genes, i.e. those of the Lrp regulon, require only a low level of ppGpp for their induction. In contrast, the RpoS regulon was induced only when high levels of ppGpp accumulated. We tested several predictions of a model that explains how bacteria allocate transcriptional resources between metabolism and stress response by discretely tuning two regulatory circuits to different levels of ppGpp. The emergent regulatory structure insures that stress survival circuits are only triggered if homeostatic metabolic networks fail to compensate for environmental deficiencies.

Unexpected coregulator range for the global regulator Lrp of Escherichia coli and Proteus mirabilis

The Lrp/AsnC family of transcription factors links gene regulation to metabolism in bacteria and archaea. Members of this family, collectively, respond to a wide range of amino acids as coregulators. In Escherichia coli, Lrp regulates over 200 genes directly and is well known to respond to leucine and, to a somewhat lesser extent, alanine. We focused on Lrp from Proteus mirabilis and E. coli, orthologs with 98% identity overall and identical helix-turn-helix motifs, for which a previous study nevertheless found functional differences. Sequence differences between these orthologs, within and adjacent to the amino acid-responsive RAM domain, led us to test for differential sensitivity to coregulatory amino acids. In the course of this investigation, we found, via in vivo reporter fusion assays and in vitro electrophoretic mobility shift experiments, that E. coli Lrp itself responded to a broader range of amino acids than was previously appreciated. In particular, for both the E. coli and P. mirabilis orthologs, Lrp responsiveness to methionine was similar in magnitude to that to leucine. Both Lrp orthologs are also fairly sensitive to Ile, His, and Thr. These observations suggest that Lrp ties gene expression in the Enterobacteriaceae rather extensively to physiological status, as reflected in amino acid pools. These findings also have substantial implications for attempts to model regulatory architecture from transcriptome measurements or to infer such architecture from genome sequences, and they suggest that even well-studied regulators deserve ongoing exploration.

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.

The Lrp family of transcription regulators in archaea

Archaea possess a eukaryotic-type basal transcription apparatus that is regulated by bacteria-like transcription regulators. A universal and abundant family of transcription regulators are the bacterial/archaeal Lrp-like regulators. The Lrp family is one of the best studied regulator families in archaea, illustrated by investigations of proteins from the archaeal model organisms: Sulfolobus, Pyrococcus, Methanocaldococcus, and Halobacterium. These regulators are extremely versatile in their DNA-binding properties, response to effector molecules, and molecular regulatory mechanisms. Besides being involved in the regulation of the amino acid metabolism, they also regulate central metabolic processes. It appears that these regulatory proteins are also involved in large regulatory networks, because of hierarchical regulations and the possible combinatorial use of different Lrp-like proteins. Here, we discuss the recent developments in our understanding of this important class of regulators.

The dawning era of comprehensive transcriptome analysis in cellular microbiology

Bacteria rapidly change their transcriptional patterns during infection in order to adapt to the host environment. To investigate host–bacteria interactions, various strategies including the use of animal infection models, in vitro assay systems and microscopic observations have been used. However, these studies primarily focused on a few specific genes and molecules in bacteria. High-density tiling arrays and massively parallel sequencing analyses are rapidly improving our understanding of the complex host–bacterial interactions through identification and characterization of bacterial transcriptomes. Information resulting from these high-throughput techniques will continue to provide novel information on the complexity, plasticity, and regulation of bacterial transcriptomes as well as their adaptive responses relative to pathogenecity. Here we summarize recent studies using these new technologies and discuss the utility of transcriptome analysis.

Sensitive and accurate identification of protein-DNA binding events in ChIP-chip assays using higher order derivative analysis

Immuno-precipitation of protein-DNA complexes followed by microarray hybridization is a powerful and cost-effective technology for discovering protein-DNA binding events at the genome scale. It is still an unresolved challenge to comprehensively, accurately and sensitively extract binding event information from the produced data. We have developed a novel strategy composed of an information-preserving signal-smoothing procedure, higher order derivative analysis and application of the principle of maximum entropy to address this challenge. Importantly, our method does not require any input parameters to be specified by the user. Using genome-scale binding data of two Escherichia coli global transcription regulators for which a relatively large number of experimentally supported sites are known, we show that ∼90% of known sites were resolved to within four probes, or ∼88 bp. Over half of the sites were resolved to within two probes, or ∼38 bp. Furthermore, we demonstrate that our strategy delivers significant quantitative and qualitative performance gains over available methods. Such accurate and sensitive binding site resolution has important consequences for accurately reconstructing transcriptional regulatory networks, for motif discovery, for furthering our understanding of local and non-local factors in protein-DNA interactions and for extending the usefulness horizon of the ChIP-chip platform.

An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.

Computational models of biological networks are useful for explaining experimental observations and predicting phenotypic behaviors. The construction of genome-scale metabolic and regulatory models is still a labor-intensive process, even with the availability of genome sequences and high-throughput datasets. Since our knowledge about biological systems is incomplete, these models are iteratively refined and validated as we discover new connections in biological networks, and eliminate inconsistencies between model predictions and experimental observations. To enable researchers to quickly determine what causes discrepancies between observed phenotypes and model predictions, we developed a new approach (GeneForce) that automatically corrects integrated metabolic and transcriptional regulatory network models. To illustrate the utility of the approach, we applied the developed method to well-curated models of E. coli metabolism and regulation. We found that the approach significantly improved the accuracy of phenotype predictions and suggested changes needed to the metabolic and/or regulatory models. We also used the approach to identify rescue non-growth phenotypes and to evaluate the conservation of transcriptional regulatory interactions between E. coli and S. typhimurium. The developed approach helps reconcile discrepancies between model predictions and experimental data by hypothesizing required network changes, and helps facilitate the development of new genome-scale models.

Inferring the conservative causal core of gene regulatory networks

Background

Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results

In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions

For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Systems biology of microbial metabolism

One current challenge in metabolic systems biology is to map out the regulation networks that control metabolism. From progress in this area, we conclude that non-transcriptional mechanisms (e.g. metabolite-protein interactions and protein phosphorylation) are highly relevant in actually controlling metabolic function. Furthermore, recent results highlight more functions of enzymes and metabolites than currently appreciated in genome-scale metabolic reconstructions, thereby adding another level of complexity. Combining experimental analyses and modeling efforts we are also beginning to understand how metabolic behavior emerges. Particularly, we recognize that metabolism is not simply a dull workhorse process but rather takes very active control of itself and other cellular processes, rendering true system-level understanding of metabolism possibly more difficult than for other cellular systems.

Bacterial nucleoid-associated proteins, nucleoid structure and gene expression

Emerging models of the bacterial nucleoid show that nucleoid-associated proteins (NAPs) and transcription contribute in combination to the dynamic nature of nucleoid structure. NAPs and other DNA-binding proteins that display gene-silencing and anti-silencing activities are emerging as key antagonistic regulators of nucleoid structure. Furthermore, it is becoming clear that the boundary between NAPs and conventional transcriptional regulators is quite blurred and that NAPs facilitate the evolution of novel gene regulatory circuits. Here, NAP biology is considered from the standpoints of both gene regulation and nucleoid structure.

Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests

Background

Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskal's algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems.

Results

We extend Chow and Liu's approach in two ways: first, to find the forest optimizing a penalized likelihood criterion, for example AIC or BIC, and second, to handle data with both discrete and Gaussian variables. We apply the approach to three datasets: two from gene expression studies and the third from a genetics of gene expression study. The minimal BIC forest supplements a conventional analysis of differential expression by providing a tentative network for the differentially expressed genes. In the genetics of gene expression context the method identifies a network approximating the joint distribution of the DNA markers and the gene expression levels.

Conclusions

The approach is generally useful as a preliminary step towards understanding the overall dependence structure of high-dimensional discrete and/or continuous data. Trees and forests are unrealistically simple models for biological systems, but can provide useful insights. Uses include the following: identification of distinct connected components, which can be analysed separately (dimension reduction); identification of neighbourhoods for more detailed analyses; as initial models for search algorithms with a larger search space, for example decomposable models or Bayesian networks; and identification of interesting features, such as hub nodes.

Structural and functional map of a bacterial nucleoid

Mapping global protein binding in the E. coli genome reveals extended domains of high protein occupancy.

Genome-wide mapping of transcription factor-DNA interactions in bacterial chromosomes in vivo has begun to reveal global zones occupied by these factors that serve two purposes: compacting the bacterial DNA and influencing global programs of gene transcription.

LrhA positively controls the expression of the locus of enterocyte effacement genes in enterohemorrhagic Escherichia coli by differential regulation of their master regulators PchA and PchB

Summary Genes essential for eliciting pathogenicity of enterohemorrhagic Escherichia coli are located within the locus of enterocyte effacement (LEE). Expression of LEE genes is positively regulated by paralogues PchA, PchB and PchC, which are encoded by separate loci of the chromosome. To elucidate the underlying regulatory mechanism, we screened transposon mutants exhibiting reduced expression of pchA, transcription level of which is highest among the pch genes. Here, we report that the LysR-homologue A (LrhA) positively regulated the transcription of pchA and pchB. A deletion in lrhA reduced the transcription levels of pchA and pchB to different degrees, and also reduced the expression of LEE-coded type 3-secreted protein, EspB. Expression of LrhA from a plasmid restored and markedly increased the transcription levels of pchA and pchB respectively, and highly induced EspB expression. Deletion analysis of the regulatory region showed that both promoter-proximal (-195 to +88) and promoter-distal (-418 to -392 for pchA and -391 to -375 for pchB) sequences were required for the LrhA-mediated upregulation of pchA and pchB genes. Purified His(6)-LrhA protein differentially bound to the regulatory regions of pchA/B, suggesting that direct regulation of pchA and pchB genes by LrhA in turn controls the expression of LEE genes.

The transcription unit architecture of the Escherichia coli genome

Bacterial genomes are organized by structural and functional elements, including promoters, transcription start and termination sites, open reading frames, regulatory noncoding regions, untranslated regions and transcription units. Here, we iteratively integrate high-throughput, genome-wide measurements of RNA polymerase binding locations and mRNA transcript abundance, 5' sequences and translation into proteins to determine the organizational structure of the Escherichia coli K-12 MG1655 genome. Integration of the organizational elements provides an experimentally annotated transcription unit architecture, including alternative transcription start sites, 5' untranslated region, boundaries and open reading frames of each transcription unit. A total of 4,661 transcription units were identified, representing an increase of >530% over current knowledge. This comprehensive transcription unit architecture allows for the elucidation of condition-specific uses of alternative sigma factors at the genome scale. Furthermore, the transcription unit architecture provides a foundation on which to construct genome-scale transcriptional and translational regulatory networks.

Branched-chain amino acids are required for the survival and virulence of Actinobacillus pleuropneumoniae in swine

In Actinobacillus pleuropneumoniae, which causes porcine pleuropneumonia, ilvI was identified as an in vivo-induced (ivi) gene and encodes the enzyme acetohydroxyacid synthase (AHAS) required for branched-chain amino acid (BCAA) biosynthesis. ilvI and 7 of 32 additional ivi promoters were upregulated in vitro when grown in chemically defined medium (CDM) lacking BCAA. Based on these observations, we hypothesized that BCAA would be found at limiting concentrations in pulmonary secretions and that A. pleuropneumoniae mutants unable to synthesize BCAA would be attenuated in a porcine infection model. Quantitation of free amino acids in porcine pulmonary epithelial lining fluid showed concentrations of BCAA ranging from 8 to 30 micromol/liter, which is 10 to 17% of the concentration in plasma. The expression of both ilvI and lrp, a global regulator that is required for ilvI expression, was strongly upregulated in CDM containing concentrations of BCAA similar to those found in pulmonary secretions. Deletion-disruption mutants of ilvI and lrp were both auxotrophic for BCAA in CDM and attenuated compared to wild-type A. pleuropneumoniae in competitive index experiments in a pig infection model. Wild-type A. pleuropneumoniae grew in CDM+BCAA but not in CDM-BCAA in the presence of sulfonylurea AHAS inhibitors. These results clearly demonstrate that BCAA availability is limited in the lungs and support the hypothesis that A. pleuropneumoniae, and potentially other pulmonary pathogens, uses limitation of BCAA as a cue to regulate the expression of genes required for survival and virulence. These results further suggest a potential role for AHAS inhibitors as antimicrobial agents against pulmonary pathogens.

Direct methods for studying transcription regulatory proteins and RNA polymerase in bacteria

Transcription factors and sigma factors play a major role in bacterial gene regulation by guiding the distribution of RNA polymerase between the promoters of different transcription units in response to changes in the environment. For 40 years Escherichia coli K-12 has been the paradigm for investigating this regulation and most studies have focused on small numbers of promoters studied by a combination of genetics and biochemistry. Since the first complete sequence for a bacterial genome was reported, the emphasis has switched to studying transcription on a global scale, with transcriptomics and bioinformatics becoming the methods of choice. Here we discuss two complementary direct experimental methods for studying transcription factors and sigma factors and we outline their potential use in rapidly establishing the regulatory networks in newly sequenced bacteria.

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation

A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

Functional states of the genome-scale Escherichia coli transcriptional regulatory system

A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this “pseudo-stoichiometric” matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

Cells are comprised of genomic information that encodes for proteins, the basic building blocks underlying all biological processes. A transcriptional regulatory system (TRS) connects a cell's environmental cues to its genome and in turn determines which genes are turned “on” in response to these cues. Consequently, TRSs control which proteins of an intracellular biochemical reaction network are present. These systems have been mathematically described, often through Boolean expressions that represent the activation or inhibition of gene transcription in response to various inputs. We recently developed a matrix formalism that extends these approaches and facilitates a quantitative representation of the Boolean logic underlying a TRS. We demonstrated on small-scale TRSs that this matrix representation is advantageous in that it facilitates the calculation of unique properties of a given TRS. Here we apply this matrix formalism to the genome-scale Escherichia coli TRS, demonstrating for the first time the predictive power of the approach at a large scale. We use the matrix-based model of E. coli transcriptional regulation to generate novel findings about the system, including new functional motifs; sets of genes whose regulation is poorly understood; and features of the TRS structure.

The DNA-recognition mode shared by archaeal feast/famine-regulatory proteins revealed by the DNA-binding specificities of TvFL3, FL10, FL11 and Ss-LrpB

The DNA-binding mode of archaeal feast/famine-regulatory proteins (FFRPs), i.e. paralogs of the Esherichia coli leucine-responsive regulatory protein (Lrp), was studied. Using the method of systematic evolution of ligands by exponential enrichment (SELEX), optimal DNA duplexes for interacting with TvFL3, FL10, FL11 and Ss-LrpB were identified as TACGA[AAT/ATT]TCGTA, GTTCGA[AAT/ATT]TCGAAC, CCGAAA[AAT/ATT]TTTCGG and TTGCAA[AAT/ATT]TTGCAA, respectively, all fitting into the form abcdeWWWedcba. Here W is A or T, and e.g. a and a are bases complementary to each other. Apparent equilibrium binding constants of the FFRPs and various DNA duplexes were determined, thereby confirming the DNA-binding specificities of the FFRPs. It is likely that these FFRPs recognize DNA in essentially the same way, since their DNA-binding specificities were all explained by the same pattern of relationship between amino-acid positions and base positions to form chemical interactions. As predicted from this relationship, when Gly36 of TvFL3 was replaced by Thr, the b base in the optimal DNA duplex changed from A to T, and, when Thr36 of FL10 was replaced by Ser, the b base changed from T to G/A. DNA-binding characteristics of other archaeal FFRPs, Ptr1, Ptr2, Ss-Lrp and LysM, are also consistent with the relationship.