Evolution and Dynamics of Regulatory Architectures Controlling Polymyxin B Resistance in Enteric Bacteria

Complex genetic networks consist of structural modules that determine the levels and timing of a cellular response. While the functional properties of the regulatory architectures that make up these modules have been extensively studied, the evolutionary history of regulatory architectures has remained largely unexplored. Here, we investigate the transition between direct and indirect regulatory pathways governing inducible resistance to the antibiotic polymyxin B in enteric bacteria. We identify a novel regulatory architecture—designated feedforward connector loop—that relies on a regulatory protein that connects signal transduction systems post-translationally, allowing one system to respond to a signal activating another system. The feedforward connector loop is characterized by rapid activation, slow deactivation, and elevated mRNA expression levels in comparison with the direct regulation circuit. Our results suggest that, both functionally and evolutionarily, the feedforward connector loop is the transitional stage between direct transcriptional control and indirect regulation

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain

Evolution of a Bacterial Regulon Controlling Virulence and Mg2+ Homeostasis

Related organisms typically rely on orthologous regulatory proteins to respond to a given signal. However, the extent to which (or even if) the targets of shared regulatory proteins are maintained across species has remained largely unknown. This question is of particular significance in bacteria due to the widespread effects of horizontal gene transfer. Here, we address this question by investigating the regulons controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. We establish that the ancestral PhoP protein directs largely different gene sets in ten analyzed species of the family Enterobacteriaceae, reflecting both regulation of species-specific targets and transcriptional rewiring of shared genes. The two targets directly activated by PhoP in all ten species (the most distant of which diverged >200 million years ago), and coding for the most conserved proteins are the phoPQ operon itself and the lipoprotein-encoding slyB gene, which decreases PhoP protein activity. The Mg2+-responsive PhoP protein dictates expression of Mg2+ transporters and of enzymes that modify Mg2+-binding sites in the cell envelope in most analyzed species. In contrast to the core PhoP regulon, which determines the amount of active PhoP and copes with the low Mg2+ stress, the variable members of the regulon contribute species-specific traits, a property shared with regulons controlled by dissimilar regulatory proteins and responding to different signals

Evolution of a Bacterial Regulon Controlling Virulence and Mg2+ Homeostasis

Related organisms typically rely on orthologous regulatory proteins to respond to a given signal. However, the extent to which (or even if) the targets of shared regulatory proteins are maintained across species has remained largely unknown. This question is of particular significance in bacteria due to the widespread effects of horizontal gene transfer. Here, we address this question by investigating the regulons controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. We establish that the ancestral PhoP protein directs largely different gene sets in ten analyzed species of the family Enterobacteriaceae, reflecting both regulation of species-specific targets and transcriptional rewiring of shared genes. The two targets directly activated by PhoP in all ten species (the most distant of which diverged >200 million years ago), and coding for the most conserved proteins are the phoPQ operon itself and the lipoprotein-encoding slyB gene, which decreases PhoP protein activity. The Mg2+-responsive PhoP protein dictates expression of Mg2+ transporters and of enzymes that modify Mg2+-binding sites in the cell envelope in most analyzed species. In contrast to the core PhoP regulon, which determines the amount of active PhoP and copes with the low Mg2+ stress, the variable members of the regulon contribute species-specific traits, a property shared with regulons controlled by dissimilar regulatory proteins and responding to different signals

Grammar-based distance in progressive multiple sequence alignment

Background: We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned. Results: The performance of the proposed algorithm is validated via comparison to popular progressive multiple alignment approaches, ClustalW and T-Coffee, and to the more recently developed algorithms MAFFT, MUSCLE, Kalign, and PSAlign using the BAliBASE 3.0 database of amino acid alignment files and a set of longer sequences generated by Rose software. The proposed algorithm has successfully built multiple alignments comparable to other programs with significant improvements in running time. The results are especially striking for large datasets. Conclusion: We introduce a computationally efficient progressive alignment algorithm using a grammar based sequence distance particularly useful in aligning large datasets

Speed, Sensitivity, and Bistability in Auto-activating Signaling Circuits

Cells employ a myriad of signaling circuits to detect environmental signals and drive specific gene expression responses. A common motif in these circuits is inducible auto-activation: a transcription factor that activates its own transcription upon activation by a ligand or by post-transcriptional modification. Examples range from the two-component signaling systems in bacteria and plants to the genetic circuits of animal viruses such as HIV. We here present a theoretical study of such circuits, based on analytical calculations, numerical computations, and simulation. Our results reveal several surprising characteristics. They show that auto-activation can drastically enhance the sensitivity of the circuit's response to input signals: even without molecular cooperativity, an ultra-sensitive threshold response can be obtained. However, the increased sensitivity comes at a cost: auto-activation tends to severely slow down the speed of induction, a stochastic effect that was strongly underestimated by earlier deterministic models. This slow-induction effect again requires no molecular cooperativity and is intimately related to the bimodality recently observed in non-cooperative auto-activation circuits. These phenomena pose strong constraints on the use of auto-activation in signaling networks. To achieve both a high sensitivity and a rapid induction, an inducible auto-activation circuit is predicted to acquire low cooperativity and low fold-induction. Examples from Escherichia coli's two-component signaling systems support these predictions

FITBAR: a web tool for the robust prediction of prokaryotic regulons

<p>Abstract</p> <p>Background</p> <p>The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The <it>in silico </it>prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.</p> <p>Results</p> <p>FITBAR (Fast Investigation Tool for Bacterial and Archaeal Regulons) is a web service designed to identify new protein binding sites on fully sequenced prokaryotic genomes. This tool consists in a workbench where the significance of the predictions can be compared using different statistical methods, a feature not found in existing resources. The Local Markov Model and the Compound Importance Sampling algorithms have been implemented to compute the P-value of newly discovered binding sites. In addition, FITBAR provides two optimized genomic scanning algorithms using either log-odds or entropy-weighted position-specific scoring matrices. Other significant features include the production of a detailed genomic context map for each detected binding site and the export of the search results in spreadsheet and portable document formats. FITBAR discovery of a high affinity <it>Escherichia coli </it>NagC binding site was validated experimentally <it>in vitro </it>as well as <it>in vivo </it>and published.</p> <p>Conclusions</p> <p>FITBAR was developed in order to allow fast, accurate and statistically robust predictions of prokaryotic regulons. This feature constitutes the main advantage of this web tool over other matrix search programs and does not impair its performance. The web service is available at <url>http://archaea.u-psud.fr/fitbar</url>.</p

Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison

<p>Abstract</p> <p>Background</p> <p>Detection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.</p> <p>Results</p> <p>Here, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at <url>http://www.ibt.lt/bioinformatics/coma</url>.</p> <p>Conclusion</p> <p>Due to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.</p

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

<p>Abstract</p> <p>Background</p> <p>Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.</p> <p>Methods</p> <p>We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test.</p> <p>Results</p> <p>We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for <it>EP300 </it>that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of <it>EP300 </it>is prognostic, predicting survival independent of age at diagnosis and tumor grade.</p> <p>Conclusions</p> <p>We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.</p