Genomic Targets and Features of BarA-UvrY (-SirA) Signal Transduction Systems
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA301907 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2917944.fastq.gz | Fastqsanger.gz | |||
| SRR2917945.fastq.gz | Fastqsanger.gz |
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Project description:Using ChIP-exo, we have identified at single nucleotide resolution genomic binding sites for UvrY in E. coli and its homolog SirA in S. Typhimurium.
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Project description:The two-component signal transduction system BarA-UvrY of Escherichia coli and its orthologs globally regulate metabolism, motility, biofilm formation, stress resistance, virulence of pathogens and quorum sensing by activating the transcription of genes for regulatory sRNAs, e.g. CsrB and CsrC in E. coli. These sRNAs act by sequestering the RNA binding protein CsrA (RsmA) away from lower affinity mRNA targets. In this study, we used ChIP-exo to identify, at single nucleotide resolution, genomic sites for UvrY (SirA) binding in E. coli and Salmonella enterica. The csrB and csrC genes were the strongest targets of crosslinking, which required UvrY phosphorylation by the BarA sensor kinase. Crosslinking occurred at two sites, an inverted repeat sequence far upstream of the promoter and a site near the -35 sequence. DNAse I footprinting revealed specific binding of UvrY in vitro only to the upstream site, indicative of additional binding requirements and/or indirect binding to the downstream site. Additional genes, including cspA, encoding the cold-shock RNA-binding protein CspA, showed weaker crosslinking and modest or negligible regulation by UvrY. We conclude that the global effects of UvrY/SirA on gene expression are primarily mediated by activating csrB and csrC transcription. We also used in vivo crosslinking and other experimental approaches to reveal new features of csrB/csrC regulation by the DeaD and SrmB RNA helicases, IHF, ppGpp and DksA. Finally, the phylogenetic distribution of BarA-UvrY was analyzed and found to be uniquely characteristic of γ-Proteobacteria and strongly anti-correlated with fliW, which encodes a protein that binds to CsrA and antagonizes its activity in Bacillus subtilis. We propose that BarA-UvrY and orthologous TCS transcribe sRNA antagonists of CsrA throughout the γ-Proteobacteria, but rarely or never perform this function in other species.
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Project description:We have previously demonstrated that molecular hydrogen (H2) neutralizes hydroxyl radical (•OH) in cultured-cells and protects cells and tissues against oxidative stress. Growing evidence has confirmed that H2-consumption exhibits preventive and therapeutic effects in patients with various diseases as well as model animals. Moreover, a small amount of H2 modulates signal transduction for regulating the expression of various genes; however, the primary target of H2 for exhibiting a variety of phenotypes was completely unknown. Here we show that H2 at low amounts manipulates oxidized phospholipid mediators to modulate Ca2+-signal transduction and gene expression.
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Project description:Two component sensor-response regulator systems (TCSs) are very common in the genomes of the Streptomyces species that have been fully sequenced to date. It has been suggested that this large number is an evolutionary response to the variable environment that Streptomyces encounter in soil. Notwithstanding this, TCSs are also more common in the sequenced genomes of other Actinomycetales when these are compared to the genomes of most other eubacteria. In this study, we have used DNA/DNA genome microarray analysis to compare fourteen Streptomyces species and one closely related genus to Streptomyces coelicolor in order to identify a core group of such systems. This core group is compared to the syntenous and non-syntenous TCSs present in the genome sequences of other Actinomycetales in order to separate the systems into those present in Actinomycetales in general, the Streptomyces specific systems and the species specific systems. Horizontal transfer does not seem to play a very important role in the evolution of the TCS complement analyzed in this study. However, cognate pairs do not necessarily seem to evolve at the same pace, which may indicate the evolutionary responses to environmental variation may be reflected differently in sequence changes within the two components of the TCSs. The overall analysis allowed subclassification of the orphan TCSs and the TCS cognate pairs and identification of possible targets for further study using gene knockouts, gene overexpression, reporter genes and yeast two hybrid analysis.
2009-01-31 | GSE13621 | GEO
Project description:Two component sensor-response regulator systems (TCSs) are very common in the genomes of the Streptomyces species that have been fully sequenced to date. It has been suggested that this large number is an evolutionary response to the variable environment that Streptomyces encounter in soil. Notwithstanding this, TCSs are also more common in the sequenced genomes of other Actinomycetales when these are compared to the genomes of most other eubacteria. In this study, we have used DNA/DNA genome microarray analysis to compare fourteen Streptomyces species and one closely related genus to Streptomyces coelicolor in order to identify a core group of such systems. This core group is compared to the syntenous and non-syntenous TCSs present in the genome sequences of other Actinomycetales in order to separate the systems into those present in Actinomycetales in general, the Streptomyces specific systems and the species specific systems. Horizontal transfer does not seem to play a very important role in the evolution of the TCS complement analyzed in this study. However, cognate pairs do not necessarily seem to evolve at the same pace, which may indicate the evolutionary responses to environmental variation may be reflected differently in sequence changes within the two components of the TCSs. The overall analysis allowed subclassification of the orphan TCSs and the TCS cognate pairs and identification of possible targets for further study using gene knockouts, gene overexpression, reporter genes and yeast two hybrid analysis. DNA/DNA comparative analysis using the University of Surrey PCR Microarray chip
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Project description:MacNamara2012 - Signal transduction
A toy model of signal tranduction to illustrate how different logic formalizms (Boolean, fuzzy logic and differential equations) treat state and time, is described here.
This model was generated from the PKN-ToyPB.sif file available in cellnopt.data 0.7.8
(also on http://www.cellnopt.org (data section) using libSBML
.
This model is described in the article:
State-time spectrum of signal transduction logic models.
MacNamara A, Terfve C, Henriques D, Bernabé BP, Saez-Rodriguez J
Physical Biology [2012, 9(4):045003]
Abstract:
Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.
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2005-01-01 | MODEL1305240000 | BioModels