Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Canonical and non-canonical EcfG sigma factors control the general stress response in Rhizobium etli

ABSTRACT: A core component of the M-NM-1-proteobacterial general stress response is the extracytoplasmic function (ECF) sigma factor EcfG, exclusively present in this taxonomic class. Half of the completed M-NM-1-proteobacterial genome sequences contain two or more copies of genes encoding M-OM-^CEcfG-like sigma factors, with the primary copy typically located adjacent to genes coding for a cognate anti-sigma factor (NepR) and two-component response regulator (PhyR). So far, the widespread occurrence of additional, non-canonical M-OM-^CEcfG copies has not satisfactorily been explained. This work explores the hierarchical relation between Rhizobium etli M-OM-^CEcfG1 and M-OM-^CEcfG2, canonical and non-canonical M-OM-^CEcfG proteins, respectively. Contrary to reports in other species, we find that M-OM-^CEcfG1 and M-OM-^CEcfG2 act in parallel, as nodes of a complex regulatory network, rather than in series, as elements of a linear regulatory cascade. We demonstrate that both sigma factors control unique yet also shared target genes, corroborating phenotypic evidence. M-OM-^CEcfG1 drives expression of rpoH2, explaining the increased heat sensitivity of an ecfG1 mutant, while katG is under control of M-OM-^CEcfG2, accounting for reduced oxidative stress resistance of an ecfG2 mutant. We also identify non-coding RNA genes as novel M-OM-^CEcfG targets. We propose a modified model for general stress response regulation in R. etli, in which M-OM-^CEcfG1 and M-OM-^CEcfG2 function largely independently. Based on a phylogenetic analysis and considering the prevalence of M-NM-1-proteobacterial genomes with multiple M-OM-^CEcfG copies, this model may also be applicable to numerous other species. In this study, we investigate to which degree R. etli EcfG1 and EcfG2 regulation is interdependent. Furthermore, we demonstrate that both sigma factors control distinct regulons and pinpoint unique EcfG2 target genes. We also identify non-coding RNAs (ncRNAs) as novel targets of EcfG1 and EcfG2 and show that expression of at least one of these ncRNAs is under direct EcfG control. The presence of ncRNAs in the regulon of an EcfG type sigma factor has not been described previously and adds an extra level of complexity to the regulation of the general stress response in Alpha-proteobacteria. Moreover, considering the widespread existence of Alpha-proteobacterial genomes with multiple EcfG copies, the here presented results not only lead to a better understanding of the general stress response in R. etli, but also contribute to a conceptual paradigm for general stress response regulation by multiple EcfG proteins in Alpha-proteobacteria. Comparative transcriptome analyses were carried out on four samples: the parental strain (WT) and mutants in either sigma factor gene ecfG1, sigma factor gene ecfG2, and both genes combined.

ORGANISM(S): Rhizobium etli CFN 42

SUBMITTER: Kristof Engelen

PROVIDER: E-GEOD-53517 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Canonical and non-canonical EcfG sigma factors control the general stress response in Rhizobium etli.

Jans Ann A Vercruysse Maarten M Gao Shanjun S Engelen Kristof K Lambrichts Ivo I Fauvart Maarten M Michiels Jan J

MicrobiologyOpen 20131028 6

A core component of the α-proteobacterial general stress response (GSR) is the extracytoplasmic function (ECF) sigma factor EcfG, exclusively present in this taxonomic class. Half of the completed α-proteobacterial genome sequences contain two or more copies of genes encoding σ(EcfG) -like sigma factors, with the primary copy typically located adjacent to genes coding for a cognate anti-sigma factor (NepR) and two-component response regulator (PhyR). So far, the widespread occurrence of addition ...[more]

PMID: 24311555

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Project description:A core component of the α-proteobacterial general stress response is the extracytoplasmic function (ECF) sigma factor EcfG, exclusively present in this taxonomic class. Half of the completed α-proteobacterial genome sequences contain two or more copies of genes encoding σEcfG-like sigma factors, with the primary copy typically located adjacent to genes coding for a cognate anti-sigma factor (NepR) and two-component response regulator (PhyR). So far, the widespread occurrence of additional, non-canonical σEcfG copies has not satisfactorily been explained. This work explores the hierarchical relation between Rhizobium etli σEcfG1 and σEcfG2, canonical and non-canonical σEcfG proteins, respectively. Contrary to reports in other species, we find that σEcfG1 and σEcfG2 act in parallel, as nodes of a complex regulatory network, rather than in series, as elements of a linear regulatory cascade. We demonstrate that both sigma factors control unique yet also shared target genes, corroborating phenotypic evidence. σEcfG1 drives expression of rpoH2, explaining the increased heat sensitivity of an ecfG1 mutant, while katG is under control of σEcfG2, accounting for reduced oxidative stress resistance of an ecfG2 mutant. We also identify non-coding RNA genes as novel σEcfG targets. We propose a modified model for general stress response regulation in R. etli, in which σEcfG1 and σEcfG2 function largely independently. Based on a phylogenetic analysis and considering the prevalence of α-proteobacterial genomes with multiple σEcfG copies, this model may also be applicable to numerous other species. In this study, we investigate to which degree R. etli EcfG1 and EcfG2 regulation is interdependent. Furthermore, we demonstrate that both sigma factors control distinct regulons and pinpoint unique EcfG2 target genes. We also identify non-coding RNAs (ncRNAs) as novel targets of EcfG1 and EcfG2 and show that expression of at least one of these ncRNAs is under direct EcfG control. The presence of ncRNAs in the regulon of an EcfG type sigma factor has not been described previously and adds an extra level of complexity to the regulation of the general stress response in Alpha-proteobacteria. Moreover, considering the widespread existence of Alpha-proteobacterial genomes with multiple EcfG copies, the here presented results not only lead to a better understanding of the general stress response in R. etli, but also contribute to a conceptual paradigm for general stress response regulation by multiple EcfG proteins in Alpha-proteobacteria.

Project description:Background Regulation of transcription is essential for any organism and Rhizobium etli (a multi-replicon, nitrogen-fixing symbiotic bacterium) is no exception. This bacterium is commonly found in the rhizosphere (free-living) or inside of root-nodules of the common bean (Phaseolus vulgaris) in a symbiotic relationship. Abiotic stresses, such as high soil temperatures and salinity, compromise the genetic stability of R. etli and therefore its symbiotic interaction with P. vulgaris. However, it is still unclear which genes are up- or down-regulated to cope with these stress conditions. The aim of this study was to identify the genes and non-coding RNAs (ncRNAs) that are differentially expressed under heat and saline shock, as well as the promoter regions of the up-regulated loci. Results Analysing the heat and saline shock responses of R. etli CE3 through RNA-Seq, we identified 756 and 392 differentially expressed genes, respectively, and 106 were up-regulated under both conditions. Notably, the set of genes over-expressed under either condition was preferentially encoded on plasmids, although this observation was more significant for the heat shock response. In contrast, during either saline shock or heat shock, the down-regulated genes were principally chromosomally encoded. Our functional analysis shows that genes encoding chaperone proteins were up-regulated during the heat shock response, whereas genes involved in the metabolism of compatible solutes were up-regulated following saline shock. Furthermore, we identified thirteen and nine ncRNAs that were differentially expressed under heat and saline shock, respectively, as well as eleven ncRNAs that had not been previously identified. Finally, using an in silico analysis, we studied the promoter motifs in all of the non-coding regions associated with the genes and ncRNAs up-regulated under both conditions. Conclusions Our data suggest that the replicon contribution is different for different stress responses and that the heat shock response is more complex than the saline shock response. In general, this work exemplifies how strategies that not only consider differentially regulated genes but also regulatory elements of the stress response provide a more comprehensive view of bacterial gene regulation. mRNA of nine independent cultures of wild type strain Rhizobium etli CE3 were sequenced using Illumina GAIIx. Our parameters were: all cultures were taken from exponential phase, at 30M-BM-0C for 30min in control condition; 42M-BM-0C for 30min for the culture subject to heat -shock, and 30min at 30M-BM-0C in supplementary PY medium with 80mM NaCl for the saline shock condition.

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: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.

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.

Dataset Information

Canonical and non-canonical EcfG sigma factors control the general stress response in Rhizobium etli

Publications

Canonical and non-canonical EcfG sigma factors control the general stress response in Rhizobium etli.

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