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The CRISPR-Cas system is involved in bacterial immunity, virulence, gene regulation, biofilm formation and sporulation. In Salmonella enterica serovar Typhi, this system consists of five transcriptional units including antisense RNAs. It was determined that these genetic elements are expressed in minimal medium and are up-regulated by pH. In addition, a transcriptional characterization of cas3 and ascse2-1 is included herein.

Escherichia coli and Bacillus subtilis are two of the best-studied prokaryotic model organisms. Previous analyses of their transcriptional regulatory networks have shown that they exhibit high plasticity during evolution and suggested that both converge to scale-free-like structures. Nevertheless, beyond this suggestion, no analyses have been carried out to identify the common systems-level components and principles governing these organisms. Here we show that these two phylogenetically distant organisms follow a set of common novel biologically consistent systems principles revealed by the mathematically and biologically founded natural decomposition approach. The discovered common functional architecture is a diamond-shaped, matryoshka-like, three-layer (coordination, processing, and integration) hierarchy exhibiting feedback, which is shaped by four systems-level components: global transcription factors (global TFs), locally autonomous modules, basal machinery and intermodular genes. The first mathematical criterion to identify global TFs, the κ-value, was reassessed on B. subtilis and confirmed its high predictive power by identifying all the previously reported, plus three potential, master regulators and eight sigma factors. The functionally conserved cores of modules, basal cell machinery, and a set of non-orthologous common physiological global responses were identified via both orthologous genes and non-orthologous conserved functions. This study reveals novel common systems principles maintained between two phylogenetically distant organisms and provides a comparison of their lifestyle adaptations. Our results shed new light on the systems-level principles and the fundamental functions required by bacteria to sustain life., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Prokaryotes have developed multiple strategies to survive phage attack and invasive DNA. Recently, a novel genetic program denominated the CRISPR/Cas system was demonstrated to have a role in these biological processes providing genetic immunity. This defense mechanism is widespread in the Archaea and Bacteria, suggesting an ancient origin. In the last few years, progress has been made regarding the functionality of the CRISPR/Cas system; however, many basic aspects of the system remain unknown. For instance, there are few studies about the conditions and regulators involved in its transcriptional control. In this work, we analyzed the transcriptional organization of the CRISPR/Cas system as well as the positive and negative regulators involved in its genetic expression in Salmonella enterica serovar Typhi. The results obtained show that in S. Typhi the CRISPR/Cas system is a LeuO-dependent operon silenced by the global regulator LRP, in addition to the previously known nucleoid-associated protein H-NS; both LRP and H-NS bind upstream and downstream of the transcriptional start site of casA. In this study, relevant nucleotides of the casA regulatory region that mediate its LeuO transcriptional activation were identified. Interestingly, specific growth conditions (N-minimal medium) were found for the LeuO-independent expression of the CRISPR/Cas system in S. Typhi. Thus, our work provides evidence that there are multiple modulators involved in the genetic expression of this immune system in S. Typhi IMSS-1.

Extensive genomic studies on gene duplication in model organisms such as Escherichia coli and Saccharomyces cerevisiae have recently been undertaken. In these models, it is commonly considered that a duplication event may include a transcription factor (TF), a target gene, or both. Following a gene duplication episode, varying scenarios have been postulated to describe the evolution of the regulatory network. However, in most of these, the TFs have emerged as the most important and in some cases the only factor shaping the regulatory network as the organism responds to a natural selection process, in order to fulfil its metabolic needs. Recent findings concerning the regulatory role played by elements other than TFs have indicated the need to reassess these early models. Thus, we performed an exhaustive review of paralogous gene regulation in E. coli and Bacillus subtilis based on published information, available in the NCBI PubMed database and in well-established regulatory databases. Our survey reinforces the notion that despite TFs being the most prominent components shaping the regulatory networks, other elements are also important. These include small RNAs, riboswitches, RNA-binding proteins, sigma factors, protein-protein interactions and DNA supercoiling, which modulate the expression of genes involved in particular metabolic processes or induce a more complex response in terms of the regulatory networks of paralogous genes in an integrated interplay with TFs.

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.

Bacteria develop a number of devices for sensing, responding, and adapting to different environmental conditions. Understanding within a genomic perspective how the transcriptional machinery of bacteria is modulated, as a response for changing conditions, is a major challenge for biologists. Knowledge of which genes are turned on or turned off under specific conditions is essential for our understanding of cell behavior. In this study we describe how the information pertaining to gene expression and associated growth conditions (even with very little knowledge of the associated regulatory mechanisms) is gathered from the literature and incorporated into RegulonDB, a database on transcriptional regulation and operon organization in E. coli. The link between growth conditions, signal transduction, and transcriptional regulation is modeled in the database in a simple format that highlights biological relevant information. As far as we know, there is no other database that explicitly clarifies the effect of environmental conditions on gene transcription. We discuss how this knowledge constitutes a benchmark that will impact future research aimed at integration of regulatory responses in the cell; for instance, analysis of microarrays, predicting culture behavior in biotechnological processes, and comprehension of dynamics of regulatory networks. This integrated knowledge will contribute to the future goal of modeling the behavior of E. coli as an entire cell. The RegulonDB database can be accessed on the web at the URL: http://www.cifn.unam.mx/Computational_Biology/regulondb/., (Copyright 2003 Wiley Periodicals, Inc.)

The transcriptional network of Escherichia coli may well be the most complete experimentally characterized network of a single cell. A rule-based approach was built to assess the degree of consistency between whole-genome microarray experiments in different experimental conditions and the accumulated knowledge in the literature compiled in RegulonDB, a data base of transcriptional regulation and operon organization in E. coli. We observed a high and statistical significant level of consistency, ranging from 70%-87%. When effector metabolites of regulatory proteins are not considered in the prediction of the active or inactive state of the regulators, consistency falls by up to 40%. Similarly, consistency decreases when rules for multiple regulatory interactions are altered or when "on" and "off" entries were assigned randomly. We modified the initial state of regulators and evaluated the propagation of errors in the network that do not correlate linearly with the connectivity of regulators. We interpret this deviation mainly as a result of the existence of redundant regulatory interactions. Consistency evaluation opens a new space of dialogue between theory and experiment, as the consequences of different assumptions can be evaluated and compared.