References of "Collado-Vides, Julio"
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See detailFrom sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters.
Perez, Abel Gonzalez; Espinosa Angarica, Vladimir UL; Collado-Vides, Julio et al

in BMC molecular biology (2009), 10

BACKGROUND: The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription ... [more ▼]

BACKGROUND: The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription Factor (TF) binding sites, and their location with respect to promoters. While variations of binding sequences modulate the strength of the interaction between the TF and its binding sites, the distance between binding sites and promoters alter the interaction between the TF and the RNA polymerase (RNAP). RESULTS: In this paper we estimated the dissociation constants (K(d)) of several E. coli TFs in their interaction with variants of their binding sequences from the scores resulting from aligning them to Positional Weight Matrices. A correlation coefficient of 0.78 was obtained when pooling together sites for different TFs. The theoretically estimated K(d) values were then used, together with the dissociation constants of the RNAP-promoter interaction to analyze activated and repressed promoters. The strength of repressor sites -- i.e., the strength of the interaction between TFs and their binding sites -- is slightly higher than that of activated sites. We explored how different factors such as the variation of binding sequences, the occurrence of more than one binding site, or different RNAP concentrations may influence the promoters' response to the variations of TF concentrations. We found that the occurrence of several regulatory sites bound by the same TF close to a promoter -- if they are bound by the TF in an independent manner -- changes the effect of TF concentrations on promoter occupancy, with respect to individual sites. We also found that the occupancy of a promoter will never be more than half if the RNAP concentration-to-K(p) ratio is 1 and the promoter is subject to repression; or less than half if the promoter is subject to activation. If the ratio falls to 0.1, the upper limit of occupancy probability for repressed drops below 10%; a descent of the limits occurs also for activated promoters. CONCLUSION: The number of regulatory sites may thus act as a versatility-producing device, in addition to serving as a source of robustness of the transcription machinery. Furthermore, our results show that the effects of TF concentration fluctuations on promoter occupancy are constrained by RNAP concentrations. [less ▲]

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See detailThe role of DNA-binding specificity in the evolution of bacterial regulatory networks.
Lozada-Chavez, Irma; Espinosa Angarica, Vladimir UL; Collado-Vides, Julio et al

in Journal of molecular biology (2008), 379(3), 627-43

Understanding the mechanisms by which transcriptional regulatory networks (TRNs) change through evolution is a fundamental problem.Here, we analyze this question using data from Escherichia coli and ... [more ▼]

Understanding the mechanisms by which transcriptional regulatory networks (TRNs) change through evolution is a fundamental problem.Here, we analyze this question using data from Escherichia coli and Bacillus subtilis, and find that paralogy relationships are insufficient to explain the global or local role observed for transcription factors (TFs) within regulatory networks. Our results provide a picture in which DNA-binding specificity, a molecular property that can be measured in different ways, is a predictor of the role of transcription factors. In particular, we observe that global regulators consistently display low levels of binding specificity, while displaying comparatively higher expression values in microarray experiments. In addition, we find a strong negative correlation between binding specificity and the number of co-regulators that help coordinate genetic expression on a genomic scale. A close look at several orthologous TFs,including FNR, a regulator found to be global in E. coli and local in B.subtilis, confirms the diagnostic value of specificity in order to understand their regulatory function, and highlights the importance of evaluating the metabolic and ecological relevance of effectors as another variable in the evolutionary equation of regulatory networks. Finally, a general model is presented that integrates some evolutionary forces and molecular properties,aiming to explain how regulons grow and shrink, as bacteria tune their regulation to increase adaptation. [less ▲]

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See detailImpact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria.
Gonzalez Perez, Abel D.; Gonzalez Gonzalez, Evelyn; Espinosa Angarica, Vladimir UL et al

in BMC genomics (2008), 9

BACKGROUND: In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that ... [more ▼]

BACKGROUND: In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as E. coli among the prokaryotes. RESULTS: In this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes. CONCLUSION: The rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events - are important sources of variation of gene regulation at this evolutionary scale. [less ▲]

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See detailPrediction of TF target sites based on atomistic models of protein-DNA complexes.
Espinosa Angarica, Vladimir UL; Perez, Abel Gonzalez; Vasconcelos, Ana T. et al

in BMC bioinformatics (2008), 9

BACKGROUND: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms ... [more ▼]

BACKGROUND: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. RESULTS: Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. CONCLUSION: Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition. [less ▲]

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See detailTractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes.
Perez, Abel Gonzalez; Espinosa Angarica, Vladimir UL; Vasconcelos, Ana Tereza R. et al

in Nucleic acids research (2007), 35(Database issue), 132-6

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. This database contains a collection ... [more ▼]

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. This database contains a collection of computationally predicted Transcription Factors' binding sites in gamma-proteobacterial genomes. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms. In this paper we describe the main improvements incorporated to the database in the past year and a half which include incorporating information on the regulatory networks of 13-increasing to 30-new gamma-proteobacteria and developing a new computational strategy to complement the putative sites identified by the original weight matrix-based approach. We have also added dynamically generated navigation tabs to the navigation interfaces. Moreover, we developed a new interface that allows users to directly retrieve information on the conservation of regulatory interactions in the 30 genomes included in the database by navigating a map that represents a core of the known Escherichia coli regulatory network. [less ▲]

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See detailComplementing computationally predicted regulatory sites in Tractor_DB using a pattern matching approach.
Guia, Marylens Hernandez; Perez, Abel Gonzalez; Espinosa Angarica, Vladimir UL et al

in In silico biology (2005), 5(2), 209-19

Prokaryotic genomes annotation has focused on genes location and function. The lack of regulatory information has limited the knowledge on cellular transcriptional regulatory networks. However, as more ... [more ▼]

Prokaryotic genomes annotation has focused on genes location and function. The lack of regulatory information has limited the knowledge on cellular transcriptional regulatory networks. However, as more phylogenetically close genomes are sequenced and annotated, the implementation of phylogenetic footprinting strategies for the recognition of regulators and their regulons becomes more important. In this paper we describe a comparative genomics approach to the prediction of new gamma-proteobacterial regulon members. We take advantage of the phylogenetic proximity of Escherichia coli and other 16 organisms of this subdivision and the intensive search of the space sequence provided by a pattern-matching strategy. Using this approach we complement predictions of regulatory sites made using statistical models currently stored in Tractor_DB, and increase the number of transcriptional regulators with predicted binding sites up to 86. All these computational predictions may be reached at Tractor_DB (www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br, www.ccg.unam.mx/Computational_Genomics/tractorDB/). We also take a first step in this paper towards the assessment of the conservation of the architecture of the regulatory network in the gamma-proteobacteria through evaluating the conservation of the overall connectivity of the network. [less ▲]

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See detailComparative studies of transcriptional regulation mechanisms in a group of eight gamma-proteobacterial genomes.
Espinosa Angarica, Vladimir UL; Gonzalez, Abel D.; Vasconcelos, Ana T. et al

in Journal of molecular biology (2005), 354(1), 184-99

Experimental data on the Escherichia coli transcriptional regulation has enabled the construction of statistical models to predict new regulatory elements within its genome. Far less is known about the ... [more ▼]

Experimental data on the Escherichia coli transcriptional regulation has enabled the construction of statistical models to predict new regulatory elements within its genome. Far less is known about the transcriptional regulatory elements in other gamma-proteobacteria with sequenced genomes, so it is of great interest to conduct comparative genomic studies oriented to extracting biologically relevant information about transcriptional regulation in these less studied organisms using the knowledge from E. coli. In this work, we use the information stored in the TRACTOR_DB database to conduct a comparative study on the mechanisms of transcriptional regulation in eight gamma-proteobacteria and 38 regulons. We assess the conservation of transcription factors binding specificity across all the eight genomes and show a correlation between the conservation of a regulatory site and the structure of the transcription unit it regulates. We also find a marked conservation of site-promoter distances across the eight organisms and a correspondence of the statistical significance of co-occurrence of pairs of transcription factor binding sites in the regulatory regions, which is probably related to a conserved architecture of higher-order regulatory complexes in the organisms studied. The results obtained in this study using the information on transcriptional regulation in E. coli enable us to conclude that not only transcription factor-binding sites are conserved across related species but also several of the transcriptional regulatory mechanisms previously identified in E. coli. [less ▲]

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See detailTRACTOR_DB: a database of regulatory networks in gamma-proteobacterial genomes.
Gonzalez, Abel D.; Espinosa Angarica, Vladimir UL; Vasconcelos, Ana T. et al

in Nucleic acids research (2005), 33(Database issue), 98-102

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites ... [more ▼]

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites and operons) within its genome. As more genomes of gamma-proteobacteria are being sequenced, the prediction of these elements in a growing number of organisms has become more feasible, as a step towards the study of how different bacteria respond to environmental changes at the level of transcriptional regulation. In this work, we present TRACTOR_DB (TRAnscription FaCTORs' predicted binding sites in prokaryotic genomes), a relational database that contains computational predictions of new members of 74 regulons in 17 gamma-proteobacterial genomes. For these predictions we used a comparative genomics approach regarding which several proof-of-principle articles for large regulons have been published. TRACTOR_DB may be currently accessed at http://www.bioinfo.cu/Tractor_DB, http://www.tractor.lncc.br/ or at http://www.cifn.unam.mx/Computational_Genomics/tractorDB. Contact Email id is tractor@cifn.unam.mx. [less ▲]

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