Genomic Targets and Features of BarA-UvrY (-SirA) Signal Transduction Systems
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ABSTRACT:
PROVIDER: PRJNA301907 | ENA |
REPOSITORIES: ENA
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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.
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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. 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.
This model is hosted on BioModels Database
and identified
by: MODEL1305240000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2005-01-01 | MODEL1305240000 | BioModels
Project description:We have previously demonstrated that molecular hydrogen (H2) neutralizes hydroxyl radical (M-bM-^@M-"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. Gene expression was measured at 4 hours after exposure to phospholipid, PAPC or oxidized PAPC that had been air-oxidized for 3 days with 0, 1.3 or 5% H2. Three independent experiments were performed for each experiment.
2014-10-17 | E-GEOD-62434 | biostudies-arrayexpress