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103. Participation of IHF and a distant UP element in the stimulation of the phage Lambda P[sub L]...

Author

Giladi, Hilla, Koby, Simi, Prag, Gali, Engelhorn, Manuel, Geiselmann, Johannes, and Oppenheim, Amos B.

Subjects
CARRIER proteins, PROMOTERS (Genetics), TRANSCRIPTION factors, DNA
Abstract

Focuses on participation of a heterodimeric DNA-binding and -bending protein in the regulation of a promoter. Stimulation of transcription by integration host factor (IHF); Correct phasing of the promoter with the IHF binding site; Importance of a flexible DNA joint between the sites bound by IHF and by RNA polymerase.

Published
1998
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105. A quantitative UV laser footprinting analysis of the interaction of IHF with specific binding sites: re-evaluation of the effective concentration of IHF in the cell11Edited by M. Yaniv

Author

Murtin, Christine, Engelhorn, Manuel, Geiselmann, Johannes, and Boccard, Fre´de´ric

Abstract

The integration host factor (IHF) of Escherichia coli is a major nucleoid-associated protein that binds to specific sites on DNA. Using gel retardation and competition experiments we have estimated that in vitro IHF binds specific sites 1000–10,000 times more tightly than non-specific, chromosomal DNA. We have analyzed the in vitro and in vivo interaction of IHF with three specific binding sites using UV laser footprinting. Because there is a strict correspondence between the intensity of the footprinting signal and the occupancy of a site, we can correlate in vitro association constants with in vivo site occupancy. From the fractional occupancy of various ihf sites in vivo, we then estimate the amount of free IHF in the cell. Exponentially growing cells contain only about 0.7 nM of free IHF, a value 20-fold smaller than the one previously deduced from DMS footprinting. As a consequence low affinity sites are only partially occupied and strong binding sites reach semi-saturation. In stationary phase the concentration of free IHF in the cell increases about sevenfold. These results show that only a very small fraction of total IHF is free in solution. Given the affinity of IHF for non-specific DNA our data imply that a large part of chromosomal DNA is accessible to IHF, and that IHF is a major contributor to chromosomal DNA condensation. The in vivo UV-laser footprinting method is of general interest, because it allows the measurement and the comparison of DNA-protein interactions in vitro and in vivo.

Published
1998
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106. Characterization of the Escherichia coli σS core regulon by Chromatin Immunoprecipitation-sequencing (ChIP-seq) analysis.

Author

Peano, Clelia, Petiti, Luca, De Bellis, Gianluca, Wolf, Johannes, Egli, Thomas, Demol, Julien, Geiselmann, Johannes, Lacour, Stephan, Rossi, Elio, and Landini, Paolo

Subjects
ESCHERICHIA coli, IMMUNOPRECIPITATION, RNA polymerases, GENETIC transcription, BIOSYNTHESIS
Abstract

In bacteria, selective promoter recognition by RNA polymerase is achieved by its association with σ factors, accessory subunits able to direct RNA polymerase 'core enzyme' (E) to different promoter sequences. Using Chromatin Immunoprecipitation-sequencing (ChIP-seq), we searched for promoters bound by the σS-associated RNA polymerase form (EσS) during transition from exponential to stationary phase. We identified 63 binding sites for EσS overlapping known or putative promoters, often located upstream of genes (encoding either ORFs or non-coding RNAs) showing at least some degree of dependence on the σS-encoding rpoS gene. EσS binding did not always correlate with an increase in transcription level, suggesting that, at some σS-dependent promoters, EσS might remain poised in a pre-initiation state upon binding. A large fraction of EσS-binding sites corresponded to promoters recognized by RNA polymerase associated with σ70 or other σ factors, suggesting a considerable overlap in promoter recognition between different forms of RNA polymerase. In particular, EσS appears to contribute significantly to transcription of genes encoding proteins involved in LPS biosynthesis and in cell surface composition. Finally, our results highlight a direct role of EσS in the regulation of non coding RNAs, such as OmrA/B, RyeA/B and SibC. [ABSTRACT FROM AUTHOR]

Published
2015
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107. Bioinformatic study of the evolution of the transcriptional regulation in bacteria

Author

Janky, Rekin's, van Helden, Jacques, Droogmans, Louis, Raussens, Vincent, Colet, Marc, André, Bruno, Geiselmann, Johannes, and Thieffry, Denis

Subjects
coregulation network, Bacteria, Bio-informatique structurale, RSAT, Microbial genomics, lexA, Structural bioinformatics, Bacterial genetics, Bactéries -- Métabolisme -- Régulation, operon prediction, Génomique microbienne, pattern-discovery, Génétique bactérienne, dyad-analysis, Microbial metabolism -- Regulation, Evaluation, Biologie, Phylogenetic footprinting, Sciences exactes et naturelles
Abstract

L'objet de cette thèse de bioinformatique est de mieux comprendre l’ensemble des systèmes de régulation génique chez les bactéries. La disponibilité de centaines de génomes complets chez les bactéries ouvre la voie aux approches de génomique comparative et donc à l’étude de l’évolution des réseaux transcriptionnels bactériens. Dans un premier temps, nous avons implémenté et validé plusieurs méthodes de prédiction d’opérons sur base des génomes bactériens séquencés. Suite à cette étude, nous avons décidé d’utiliser un algorithme qui se base simplement sur un seuil sur la distance intergénique, à savoir la distance en paires de bases entre deux gènes adjacents. Notre évaluation sur base d’opérons annotés chez Escherichia coli et Bacillus subtilis nous permet de définir un seuil optimal de 55pb pour lequel nous obtenons respectivement 78 et 79% de précision. Deuxièmement, l’identification des motifs de régulation transcriptionnelle, tels les sites de liaison des facteurs de transcription, donne des indications de l’organisation de la régulation. Nous avons développé une méthode de recherche d’empreintes phylogénétiques qui consiste à découvrir des paires de mots espacés (dyades) statistiquement sur-représentées en amont de gènes orthologues bactériens. Notre méthode est particulièrement adaptée à la recherche de motifs chez les bactéries puisqu’elle profite d’une part des centaines de génomes bactériens séquencés et d’autre part les facteurs de transcription bactériens présentent des domaines Hélice-Tour-Hélice qui reconnaissent spécifiquement des dyades. Une évaluation systématique sur 368 gènes de E.coli a permis d’évaluer les performances de notre méthode et de tester l’influence de plus de 40 combinaisons de paramètres concernant le niveau taxonomique, l’inférence d’opérons, le filtrage des dyades spécifiques de E.coli, le choix des modèles de fond pour le calcul du score de significativité, et enfin un seuil sur ce score. L’analyse détaillée pour un cas d’étude, l’autorégulation du facteur de transcription LexA, a montré que notre approche permet d’étudier l’évolution des sites d’auto-régulation dans plusieurs branches taxonomiques des bactéries. Nous avons ensuite appliqué la détection d’empreintes phylogénétiques à chaque gène de E.coli, et utilisé les motifs détectés comme significatifs afin de prédire les gènes co-régulés. Au centre de cette dernière stratégie, est définie une matrice de scores de significativité pour chaque mot détecté par gène chez l’organisme de référence. Plusieurs métriques ont été définies pour la comparaison de paires de profils de scores de sorte que des paires de gènes ayant des motifs détectés significativement en commun peuvent être regroupées. Ainsi, l’ensemble des nos méthodes nous permet de reconstruire des réseaux de co-régulation uniquement à partir de séquences génomiques, et nous ouvre la voie à l’étude de l’organisation et de l’évolution de la régulation transcriptionnelle pour des génomes dont on ne connaît rien.The purpose of my thesis is to study the evolution of regulation within bacterial genomes by using a cross-genomic comparative approach. Nowadays, numerous genomes have been sequenced facilitating in silico analysis in order to detect groups of functionally related genes and to predict the mechanism of their relative regulation. In this project, we combined prediction of operons and regulons in order to reconstruct the transcriptional regulatory network for a bacterial genome. We have implemented three methods in order to predict operons from a bacterial genome and evaluated them on hundreds of annotated operons of Escherichia coli and Bacillus subtilis. It turns out that a simple distance-based threshold method gives good results with about 80% of accuracy. The principle of this method is to classify pairs of adjacent genes as “within operon” or “transcription unit border”, respectively, by using a threshold on their intergenic distance: two adjacent genes are predicted to be within an operon if their intergenic distance is smaller than 55bp. In the second part of my thesis, I evaluated the performances of a phylogenetic footprinting approach based on the detection of over-represented spaced motifs. This method is particularly suitable for (but not restricted to) Bacteria, since such motifs are typically bound by factors containing a Helix-Turn-Helix domain. We evaluated footprint discovery in 368 E.coli K12 genes with annotated sites, under 40 different combinations of parameters (taxonomical level, background model, organism-specific filtering, operon inference, significance threshold). Motifs are assessed both at the level of correctness and significance. The footprint discovery method proposed here shows excellent results with E. coli and can readily be extended to predict cis-acting regulatory signals and propose testable hypotheses in bacterial genomes for which nothing is known about regulation. Moreover, the predictive power of the strategy, and its capability to track the evolutionary divergence of cis-regulatory motifs was illustrated with the example of LexA auto-regulation, for which our predictions are remarkably consistent with the binding sites characterized in different taxonomical groups. A next challenge was to identify groups of co-regulated genes (regulons), by regrouping genes with similar motifs, in order to address the challenging domain of the evolution of transcriptional regulatory networks. We tested different metrics to detect putative pairs of co-regulated genes. The comparison between predicted and annotated co-regulation networks shows a high positive predictive value, since a good fraction of the predicted associations correspond to annotated co-regulations, and a low sensitivity, which may be due to the consequence of highly connected transcription factors (global regulator). A regulon-per-regulon analysis indeed shows that the sensitivity is very weak for these transcription factors, but can be quite good for specific transcription factors. The originality of this global strategy is to be able to infer a potential network from the sole analysis of genome sequences, and without any prior knowledge about the regulation in the considered organism., Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Published
2007

108. Etude bioinformatique de l’évolution de la régulation transcriptionnelle chez les bactéries/Bioinformatic study of the evolution of the transcriptional regulation in bacteria

Author

Janky, Rekin's, van Helden, Jacques, Droogmans, Louis, Raussens, Vincent, Colet, Marc, André, Bruno, Geiselmann, Johannes, and Thieffry, Denis

Subjects
coregulation network, Bacteria, RSAT, operon prediction, pattern-discovery, dyad-analysis, Evaluation, Phylogenetic footprinting, lexA
Abstract

L'objet de cette thèse de bioinformatique est de mieux comprendre l’ensemble des systèmes de régulation génique chez les bactéries. La disponibilité de centaines de génomes complets chez les bactéries ouvre la voie aux approches de génomique comparative et donc à l’étude de l’évolution des réseaux transcriptionnels bactériens. Dans un premier temps, nous avons implémenté et validé plusieurs méthodes de prédiction d’opérons sur base des génomes bactériens séquencés. Suite à cette étude, nous avons décidé d’utiliser un algorithme qui se base simplement sur un seuil sur la distance intergénique, à savoir la distance en paires de bases entre deux gènes adjacents. Notre évaluation sur base d’opérons annotés chez Escherichia coli et Bacillus subtilis nous permet de définir un seuil optimal de 55pb pour lequel nous obtenons respectivement 78 et 79% de précision. Deuxièmement, l’identification des motifs de régulation transcriptionnelle, tels les sites de liaison des facteurs de transcription, donne des indications de l’organisation de la régulation. Nous avons développé une méthode de recherche d’empreintes phylogénétiques qui consiste à découvrir des paires de mots espacés (dyades) statistiquement sur-représentées en amont de gènes orthologues bactériens. Notre méthode est particulièrement adaptée à la recherche de motifs chez les bactéries puisqu’elle profite d’une part des centaines de génomes bactériens séquencés et d’autre part les facteurs de transcription bactériens présentent des domaines Hélice-Tour-Hélice qui reconnaissent spécifiquement des dyades. Une évaluation systématique sur 368 gènes de E.coli a permis d’évaluer les performances de notre méthode et de tester l’influence de plus de 40 combinaisons de paramètres concernant le niveau taxonomique, l’inférence d’opérons, le filtrage des dyades spécifiques de E.coli, le choix des modèles de fond pour le calcul du score de significativité, et enfin un seuil sur ce score. L’analyse détaillée pour un cas d’étude, l’autorégulation du facteur de transcription LexA, a montré que notre approche permet d’étudier l’évolution des sites d’auto-régulation dans plusieurs branches taxonomiques des bactéries. Nous avons ensuite appliqué la détection d’empreintes phylogénétiques à chaque gène de E.coli, et utilisé les motifs détectés comme significatifs afin de prédire les gènes co-régulés. Au centre de cette dernière stratégie, est définie une matrice de scores de significativité pour chaque mot détecté par gène chez l’organisme de référence. Plusieurs métriques ont été définies pour la comparaison de paires de profils de scores de sorte que des paires de gènes ayant des motifs détectés significativement en commun peuvent être regroupées. Ainsi, l’ensemble des nos méthodes nous permet de reconstruire des réseaux de co-régulation uniquement à partir de séquences génomiques, et nous ouvre la voie à l’étude de l’organisation et de l’évolution de la régulation transcriptionnelle pour des génomes dont on ne connaît rien. The purpose of my thesis is to study the evolution of regulation within bacterial genomes by using a cross-genomic comparative approach. Nowadays, numerous genomes have been sequenced facilitating in silico analysis in order to detect groups of functionally related genes and to predict the mechanism of their relative regulation. In this project, we combined prediction of operons and regulons in order to reconstruct the transcriptional regulatory network for a bacterial genome. We have implemented three methods in order to predict operons from a bacterial genome and evaluated them on hundreds of annotated operons of Escherichia coli and Bacillus subtilis. It turns out that a simple distance-based threshold method gives good results with about 80% of accuracy. The principle of this method is to classify pairs of adjacent genes as “within operon” or “transcription unit border”, respectively, by using a threshold on their intergenic distance: two adjacent genes are predicted to be within an operon if their intergenic distance is smaller than 55bp. In the second part of my thesis, I evaluated the performances of a phylogenetic footprinting approach based on the detection of over-represented spaced motifs. This method is particularly suitable for (but not restricted to) Bacteria, since such motifs are typically bound by factors containing a Helix-Turn-Helix domain. We evaluated footprint discovery in 368 E.coli K12 genes with annotated sites, under 40 different combinations of parameters (taxonomical level, background model, organism-specific filtering, operon inference, significance threshold). Motifs are assessed both at the level of correctness and significance. The footprint discovery method proposed here shows excellent results with E. coli and can readily be extended to predict cis-acting regulatory signals and propose testable hypotheses in bacterial genomes for which nothing is known about regulation. Moreover, the predictive power of the strategy, and its capability to track the evolutionary divergence of cis-regulatory motifs was illustrated with the example of LexA auto-regulation, for which our predictions are remarkably consistent with the binding sites characterized in different taxonomical groups. A next challenge was to identify groups of co-regulated genes (regulons), by regrouping genes with similar motifs, in order to address the challenging domain of the evolution of transcriptional regulatory networks. We tested different metrics to detect putative pairs of co-regulated genes. The comparison between predicted and annotated co-regulation networks shows a high positive predictive value, since a good fraction of the predicted associations correspond to annotated co-regulations, and a low sensitivity, which may be due to the consequence of highly connected transcription factors (global regulator). A regulon-per-regulon analysis indeed shows that the sensitivity is very weak for these transcription factors, but can be quite good for specific transcription factors. The originality of this global strategy is to be able to infer a potential network from the sole analysis of genome sequences, and without any prior knowledge about the regulation in the considered organism., Doctorat en Sciences, info:eu-repo/semantics/published

Published
2007

109. Acetate Metabolism and the Inhibition of Bacterial Growth by Acetate.

Author

Pinhal S, Ropers D, Geiselmann J, and de Jong H

Subjects
Biological Transport, Fermentation, Gene Expression Regulation, Bacterial, Glucose metabolism, Mutation, Acetates metabolism, Escherichia coli genetics, Escherichia coli growth & development, Metabolic Networks and Pathways
Abstract

During aerobic growth on glucose, Escherichia coli excretes acetate, a mechanism called "overflow metabolism." At high concentrations, the secreted acetate inhibits growth. Several mechanisms have been proposed for explaining this phenomenon, but a thorough analysis is hampered by the diversity of experimental conditions and strains used in these studies. Here, we describe the construction of a set of isogenic strains that remove different parts of the metabolic network involved in acetate metabolism. Analysis of these strains reveals that (i) high concentrations of acetate in the medium inhibit growth without significantly perturbing central metabolism; (ii) growth inhibition persists even when acetate assimilation is completely blocked; and (iii) regulatory interactions mediated by acetyl-phosphate play a small but significant role in growth inhibition by acetate. The major contribution to growth inhibition by acetate may originate in systemic effects like the uncoupling effect of organic acids or the perturbation of the anion composition of the cell, as previously proposed. Our data suggest, however, that under the conditions considered here, the uncoupling effect plays only a limited role. IMPORTANCE High concentrations of organic acids such as acetate inhibit growth of Escherichia coli and other bacteria. This phenomenon is of interest for understanding bacterial physiology but is also of practical relevance. Growth inhibition by organic acids underlies food preservation and causes problems during high-density fermentation in biotechnology. What causes this phenomenon? Classical explanations invoke the uncoupling effect of acetate and the establishment of an anion imbalance. Here, we propose and investigate an alternative hypothesis: the perturbation of acetate metabolism due to the inflow of excess acetate. We find that this perturbation accounts for 20% of the growth-inhibitory effect through a modification of the acetyl phosphate concentration. Moreover, we argue that our observations are not expected based on uncoupling alone., (Copyright © 2019 Pinhal et al.)

Published
2019
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110. A synthetic growth switch based on controlled expression of RNA polymerase.

Author

Izard J, Gomez Balderas CD, Ropers D, Lacour S, Song X, Yang Y, Lindner AB, Geiselmann J, and de Jong H

Subjects
DNA-Directed RNA Polymerases metabolism, Escherichia coli physiology, Systems Biology, DNA-Directed RNA Polymerases genetics, Escherichia coli genetics, Escherichia coli growth & development, Gene Expression Regulation, Bacterial genetics, Synthetic Biology methods
Abstract

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology., (© 2015 The Authors. Published under the terms of the CC BY 4.0 license.)

Published
2015
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111. Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Author

Stefan D, Pinel C, Pinhal S, Cinquemani E, Geiselmann J, and de Jong H

Subjects
Bacterial Proteins analysis, Bacterial Proteins genetics, Bacterial Proteins metabolism, Escherichia coli genetics, Green Fluorescent Proteins analysis, Green Fluorescent Proteins genetics, Green Fluorescent Proteins metabolism, RNA, Messenger genetics, Sigma Factor analysis, Sigma Factor genetics, Sigma Factor metabolism, Transcription, Genetic genetics, Gene Expression Regulation, Bacterial genetics, Genes, Reporter genetics, Models, Genetic, Promoter Regions, Genetic genetics
Abstract

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Published
2015
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112. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks.

Author

Batt G, Besson B, Ciron PE, de Jong H, Dumas E, Geiselmann J, Monte R, Monteiro PT, Page M, Rechenmann F, and Ropers D

Subjects
Computer Simulation, Mathematical Concepts, Bacteria genetics, Gene Regulatory Networks genetics, Models, Genetic, Software, Systems Biology methods
Abstract

Genetic Network Analyzer (GNA) is a tool for the qualitative modeling and simulation of gene regulatory networks, based on so-called piecewise-linear differential equation models. We describe the use of this tool in the context of the modeling of bacterial regulatory networks, notably the network of global regulators controlling the adaptation of Escherichia coli to carbon starvation conditions. We show how the modeler, by means of GNA, can define a regulatory network, build a model of the network, determine the steady states of the system, perform a qualitative simulation of the network dynamics, and analyze the simulation results using model-checking tools. The example illustrates the interest of qualitative approaches for the analysis of the dynamics of bacterial regulatory networks.

Published
2012
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113. Crl, a low temperature-induced protein in Escherichia coli that binds directly to the stationary phase sigma subunit of RNA polymerase.

Author

Bougdour A, Lelong C, and Geiselmann J

Subjects
Bacterial Proteins genetics, Bacterial Proteins metabolism, Base Sequence, Chromatography, Gel, DNA Footprinting, Deoxyribonuclease I metabolism, Electrophoresis, Polyacrylamide Gel, Escherichia coli Proteins genetics, Immunoblotting, Models, Biological, Molecular Sequence Data, Mutation, Plasmids metabolism, Promoter Regions, Genetic, Protein Binding, Sigma Factor metabolism, Temperature, Time Factors, Transcription, Genetic, Up-Regulation, Bacterial Proteins chemistry, Bacterial Proteins physiology, DNA-Directed RNA Polymerases chemistry, Escherichia coli metabolism, Sigma Factor chemistry
Abstract

The alternative sigma factor sigma(S) (RpoS) of Escherichia coli RNA polymerase regulates the expression of stationary phase and stress-response genes. sigma(S) is also required for the transcription of the cryptic genes csgBA that encode the subunits of the curli proteins. The expression of the csgBA genes is regulated in response to a multitude of physiological signals. In stationary phase, these genes are transcribed by the sigma(S) factor, and expression of the operon is enhanced by the small protein Crl. It has been shown that Crl stimulates the activity of sigma(S), leading to an increased transcription rate of a subset of genes of the rpoS regulon in stationary phase. However, the underlying molecular mechanism has remained elusive. We show here that Crl interacts directly with sigma(S) and that this interaction promotes binding of the sigma(S) holoenzyme (Esigma(S)) to the csgBA promoter. Expression of Crl is increased during the transition from growing to stationary phase. Crl accumulates in stationary phase cells at low temperature (30 degrees C) but not at 37 degrees C. We therefore propose that Crl is a second thermosensor, besides DsrA, controlling sigma(S) activity.

Published
2004
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