Igor Zwir

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.

Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation

MOTIVATION A critical challenge of the post-genomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis regulatory features and map such features into expression patterns to classify genes into distinct networks. However, these methods are not focused on distinguishing between differentially regulated genes within a given network. Here we describe an unsupervised machine learning method, termed GPS for gene promoter scan, that discriminates among co-regulated promoters by simultaneously considering both cis-acting regulatory features and gene expression. GPS is particularly useful for knowledge discovery in environments with reduced datasets and high levels of uncertainty. RESULTS Application of this method to the enteric bacteria Escherichia coli and Salmonella enterica uncovered novel members, as well as regulatory interactions in the regulon controlled by the PhoP protein that were not discovered using previous approaches. The predictions made by GPS were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription, and is a central element in a highly connected network. AVAILABILITY The scripts and programs used in this work are accessible from the gps-tools.wustl.edu website. Data and predictions are available by request.

The promoter architectural landscape of the Salmonella PhoP regulon

The DNA‐binding protein PhoP controls virulence and Mg2+ homeostasis in the Gram‐negative pathogen Salmonella enterica serovar Typhimurium. PhoP regulates expression of a large number of genes that differ both in their ancestry and in the biochemical functions and physiological roles of the encoded products. This suggests that PhoP‐regulated genes are differentially expressed. To understand how a bacterial activator might generate varied gene expression behaviour, we investigated the cis‐acting promoter features (i.e. the number of PhoP binding sites, as well as their orientation and location with respect to the sites bound by RNA polymerase and the sequences that constitute the PhoP binding sites) in 23 PhoP‐activated promoters. Our results show that natural PhoP‐activated promoters utilize only a limited number of combinations of cis‐acting features – or promoter architectures. We determine that PhoP activates transcription by different mechanisms, and that ancestral and horizontally acquired PhoP‐activated genes have distinct promoter architectures.

Profile analysis and prediction of tissue-specific CpG island methylation classes

BackgroundThe computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.ResultsWe defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation.ConclusionOur approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.

A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database

Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical conceptual clustering approaches normally recover the most obvious relations, but fail to discover the less frequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), relying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in structural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.

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 Mg2+ 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.

A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes

One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein–protein interactions among regulatory proteins and with RNA polymerase(s), and protein–DNA interactions of these trans-acting factors withcis-acting DNA sequences in the promoter regions of those regulated genes. Therefore, identifying these protein–DNA interactions, by means of the DNA motifs that characterize the regulatory factors operating in the transcription of a gene, becomes crucial for determining which genes participate in a regulation process, how they behave and how they are connected to build genetic networks. In this paper, we propose a hybrid promoter analysis methodology (HPAM) to discover complex promoter motifs that combines: the neural network efficiency and ability of representing imprecise and incomplete patterns; the flexibility and interpretability of fuzzy models; and the multi-objective evolutionary algorithms capability to identify optimal instances of a model by searching according to multiple criteria. We test our methodology by learning and predicting the RNA polymerase motif in prokaryotic genomes. This constitutes a special challenge due to the multiplicity of the RNA polymerase targets and its connectivity with other transcription factors, which sometimes require multiple functional binding sites even in close located regulatory regions; and the uncertainty ∗ Corresponding author. E-mail addresses:vcotik@dc.uba.ar(V. Cotik), rromero@dc.uba.ar(R. Romero Zaliz), zwir@borcim.wustl.edu , zwir@decsai.ugr.es (I. Zwir). 0165-0114/

Automated Biological Sequence Description by Genetic Multiobjective Generalized Clustering

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Intramolecular arrangement of sensor and regulator overcomes relaxed specificity in hybrid two-component systems

Abstract: Recent advances in the accessibility of databases containing representations of complex objects—exemplified by repositories of time‐series data, information about biological macromolecules, or knowledge about metabolic pathways—have not been matched by availability of tools that facilitate the retrieval of objects of particular interest and aid understanding their structure and relations. In applications, such as the analysis of DNA sequences, on the other hand, requirements to retrieve objects on the basis of qualitative characteristics are poorly met by descriptions that emphasize precision and detail rather than structural features. This paper presents a method for identification of interesting qualitative features in biological sequences. Our approach relies on a generalized clustering methodology in which the features being sought correspond to the solutions of a multivariable, multiobjective optimization problem with features generally corresponding to fuzzy subsets of the object being represented. Foremost among the optimization objectives being considered are measures of the degree by which features resemble prototypical structures deemed to be interesting by database users. Other objectives include feature size and, in some cases, performance criteria related to domain‐specific constraints. Genetic‐algorithm methods are employed to solve the multiobjective optimization problem. These optimization algorithms discover candidate features as subsets of the object being described and that lie in the set of all Pareto‐optimal solutions—of that problem. These candidate features are then summarized, employing again evolutionary‐computation methods, and interrelated by employing domain‐specific relations of interest to the end users. We present results of the application of this two‐step method to the recognition and summarization of interesting features in DNA sequences of Tripanosoma cruzi.

PGMRA: a web server for (phenotype × genotype) many-to-many relation analysis in GWAS

Cellular processes require specific interactions between cognate protein partners and concomitant discrimination against noncognate partners. Signal transduction by classical two-component regulatory systems typically entails an intermolecular phosphoryl transfer between a sensor kinase (SK) and a cognate response regulator (RR). Interactions between noncognate partners are rare because SK/RR pairs coevolve unique interfaces that dictate phosphotransfer specificity. Here we report that the in vitro phosphotransfer specificity is relaxed in hybrid two-component systems (HTCSs) from the human gut symbiont Bacteroides thetaiotaomicron, which harbor both the SK and RR in a single polypeptide. In contrast, phosphotransfer specificity is retained in classical two-component regulatory systems from this organism. This relaxed specificity enabled us to rewire a HTCS successfully to transduce signals between noncognate SK/RR pairs. Despite the relaxed specificity between SK and RRs, HTCSs remained insulated from cross-talk with noncognate proteins in vivo. Our data suggest that the high local concentration of the SK and RR present in the same polypeptide maintains specificity while relaxing the constraints on coevolving unique contact interfaces.