Dataset Information

Liebal2012 - B.subtilis transcription inhibition model

ABSTRACT: Liebal2012 - B.subtilis transcription inhibition model An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models are constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the Transcription inhibition model (MODEL1212180000). This model is described in the article: Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis. Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, Völker U, Wolkenhauer O. 2012 Jun;8(6):1806-14. Abstract: In Bacillus subtilis the σ(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of σ(B) activity. In the mutant strain BSA115, σ(B) transcription is inducible by the addition of IPTG and negative control of σ(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous β-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of σ(B) and β-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or β-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the σ(B) response. Therefore, we conclude that protein instability following σ(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the σ(B) response. Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab). This model is hosted on BioModels Database and identified by: MODEL1212180000 . 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.

SUBMITTER: Ulf Liebal

PROVIDER: BIOMD0000000461 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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	BIOMD0000000461?filename=BIOMD0000000461-biopax2.owl	Owl
	BIOMD0000000461?filename=BIOMD0000000461-biopax3.owl	Owl
	BIOMD0000000461?filename=BIOMD0000000461-matlab.m	Other
	BIOMD0000000461?filename=BIOMD0000000461-octave.m	Other
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Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis.

Liebal Ulf W UW Sappa Praveen K PK Millat Thomas T Steil Leif L Homuth Georg G Völker Uwe U Wolkenhauer Olaf O

Molecular bioSystems 20120417 6

In Bacillus subtilis the σ(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of σ(B) activity. In the mutant strain BSA115, σ(B) transcription is inducible by the addition of I ...[more]

PMID: 22511268

Similar Datasets

Project description:Liebal2012 - B.subtilis post-transcription instability model An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the post-transcription instability model (MODEL1302080001). This model is described in the article: Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis. Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, Völker U, Wolkenhauer O. 2012 Jun;8(6):1806-14. Abstract: In Bacillus subtilis the σ(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of σ(B) activity. In the mutant strain BSA115, σ(B) transcription is inducible by the addition of IPTG and negative control of σ(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous β-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of σ(B) and β-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or β-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the σ(B) response. Therefore, we conclude that protein instability following σ(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the σ(B) response. Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab). This model is hosted on BioModels Database and identified by: MODEL1302080001 . 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.

Project description:Liebal2012 - B.subtilis sigB proteolysis model An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the sigma B proteolysis model (MODEL1302080000). This model is described in the article: Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis. Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, Völker U, Wolkenhauer O. 2012 Jun;8(6):1806-14. Abstract: In Bacillus subtilis the σ(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of σ(B) activity. In the mutant strain BSA115, σ(B) transcription is inducible by the addition of IPTG and negative control of σ(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous β-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of σ(B) and β-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or β-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the σ(B) response. Therefore, we conclude that protein instability following σ(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the σ(B) response. Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab). This model is hosted on BioModels Database and identified by: MODEL1302080000 . 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.

Project description:Abstract of associated manuscript: The Bacillus subtilis extracytoplasmic function (ECF) sigma(M) factor is activated by cell envelope stress elicited by antibiotics, and by acid, heat, ethanol and superoxide stresses. Here, we have used several complementary approaches to identify genes controlled by sigma(M). In many cases, expression is only partially dependent on sigma(M) because of both overlapping promoter recognition with other ECF sigma factors and the presence of additional promoter elements. Genes regulated by sigma(M) have a characteristic pattern of induction in response to cell envelope-acting antibiotics as evidenced by hierarchical clustering analysis. sigma(M) also contributes to the expression of the Spx transcription factor and thereby indirectly regulates genes of the Spx regulon. Cell envelope stress responses also include regulons controlled by sigma(W), sigma(B) and several two-component regulatory systems (e.g. LiaRS, YycFG, BceRS). Activation of the sigma(M) regulon increases expression of proteins functioning in transcriptional control, cell wall synthesis and shape determination, cell division, DNA damage monitoring, recombinational repair and detoxification. WT (-van) vs. WT (+van), sigM (-van) vs. sigM (+van), WT (-van) vs. sigM (-van), WT (+van) vs. sigM (+van), WT (-van) vs. spx (-van), WT (+van) vs. spx (+van). Each experiment was conducted at least twice using two independent total RNA preparations. For vancomycin untreated and treated experiments, untreated samples were labeled with Alexa Fluor 555 and treated samples with Alexa Fluor 647. For WT vs. mutant experiments, wild type was labeled with Alexa Fluor 555 and mutants with Alexa Fluor 647. For dye swap experiment, wild-type was labeled with Alexa Fluor 647 and mutant with Alexa Fluor 555. Bacillus subtilis CU1065, WT (-van) vs. WT (+van), sigM (-van) vs. sigM (+van), WT (-van) vs. sigM (-van), WT (+van) vs. sigM (+van), WT (-van) vs. spx (-van), WT (+van) vs. spx (+van)