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Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Stefan D, Pinel C, Pinhal S, Cinquemani E, Geiselmann J, de Jong H - PLoS Comput. Biol. (2015)

Bottom Line: Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected.From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression.In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

View Article: PubMed Central - PubMed

Affiliation: INRIA Grenoble - Rhône-Alpes, Grenoble, France; Laboratoire Interdisciplinaire de Physique (LIPhy, CNRS UMR 5588), Université Joseph Fourier, Grenoble, France.

ABSTRACT
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

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FliA-FlgM module.The regulatory circuit composed of the flagellar-specific transcription factor FliA, a sigma factor also known as σ28, and the anti-sigma factor FlgM forms a check-point in the transcriptional hierarchy of the motility genes in E. coli. While fliA is transcribed from a single class 2 promoter (pfliA), flgM is transcribed from both a class 2 and a class 3 promoter (pflgA and pflgM, respectively). FliA binds to RNA polymerase core enzyme and directs transcription from a total of five class 3 promoters [33], including ptar and pflgM. When bound to FlgM, FliA cannot activate transcription. When the hook basal-body (HBB) structure is in place, however, FlgM is exported from the cell, thus releasing FliA from the inactive complex. FliA is subject to proteolysis by Lon, but FlgM-binding protects FliA from degradation. The fliA promoter is auto-regulated by FliA and by a number of other regulators, most importantly the motility master regulator FlhDC. The expression of FlhDC itself is under the control of a variety of regulatory factors, including RpoS, CpxR, and CsgD. The activity of the genes in the figure is measured by fusion of their promoters to a gfp reporter gene on a low-copy plasmid. Genes are shown in grey or green and their promoter regions in red. Regulatory interactions are represented by open arrows, association and dissociation of FliA and FlgM as well as degradation and export by filled arrows. The figure does not explicitly show that fliA, flgM, and tar are included in larger transcriptional units, the fliAZY, flgAMN, flgMN and tar-tap-cheRBYZ operons [33].
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pcbi.1004028.g001: FliA-FlgM module.The regulatory circuit composed of the flagellar-specific transcription factor FliA, a sigma factor also known as σ28, and the anti-sigma factor FlgM forms a check-point in the transcriptional hierarchy of the motility genes in E. coli. While fliA is transcribed from a single class 2 promoter (pfliA), flgM is transcribed from both a class 2 and a class 3 promoter (pflgA and pflgM, respectively). FliA binds to RNA polymerase core enzyme and directs transcription from a total of five class 3 promoters [33], including ptar and pflgM. When bound to FlgM, FliA cannot activate transcription. When the hook basal-body (HBB) structure is in place, however, FlgM is exported from the cell, thus releasing FliA from the inactive complex. FliA is subject to proteolysis by Lon, but FlgM-binding protects FliA from degradation. The fliA promoter is auto-regulated by FliA and by a number of other regulators, most importantly the motility master regulator FlhDC. The expression of FlhDC itself is under the control of a variety of regulatory factors, including RpoS, CpxR, and CsgD. The activity of the genes in the figure is measured by fusion of their promoters to a gfp reporter gene on a low-copy plasmid. Genes are shown in grey or green and their promoter regions in red. Regulatory interactions are represented by open arrows, association and dissociation of FliA and FlgM as well as degradation and export by filled arrows. The figure does not explicitly show that fliA, flgM, and tar are included in larger transcriptional units, the fliAZY, flgAMN, flgMN and tar-tap-cheRBYZ operons [33].

Mentions: The more than 60 genes responsible for motility in bacteria are structured in a transcriptional hierarchy of three operon classes that has been mapped in detail for Escherichia coli and Salmonella enterica [27–29, 32]. The single class 1 operon flhDC encodes the proteins FlhD and FlhC, which form a heteromultimeric complex activating σ70-dependent transcription of the class 2 operons. The latter encode the proteins making up the flagellar motor structure as well as a major regulator of the class 3 operons, the sigma factor FliA (σ28). When bound to core RNA polymerase, FliA directs the transcription of the class 3 operons [33] that code for the proteins forming the filament structure of the flagellum and the chemotaxis sensing system. The aspartate chemoreceptor Tar is an example of such a class 3 protein. The action of FliA is counteracted by the anti-sigma factor FlgM, which binds to FliA and thus prevents its association with RNA polymerase. FlgM is encoded by the gene flgM, which is transcribed from both a class 2 promoter and a class 3 promoter. FlgM can be excreted from the cell through the center of the basal-body structure (Fig. 1).


Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Stefan D, Pinel C, Pinhal S, Cinquemani E, Geiselmann J, de Jong H - PLoS Comput. Biol. (2015)

FliA-FlgM module.The regulatory circuit composed of the flagellar-specific transcription factor FliA, a sigma factor also known as σ28, and the anti-sigma factor FlgM forms a check-point in the transcriptional hierarchy of the motility genes in E. coli. While fliA is transcribed from a single class 2 promoter (pfliA), flgM is transcribed from both a class 2 and a class 3 promoter (pflgA and pflgM, respectively). FliA binds to RNA polymerase core enzyme and directs transcription from a total of five class 3 promoters [33], including ptar and pflgM. When bound to FlgM, FliA cannot activate transcription. When the hook basal-body (HBB) structure is in place, however, FlgM is exported from the cell, thus releasing FliA from the inactive complex. FliA is subject to proteolysis by Lon, but FlgM-binding protects FliA from degradation. The fliA promoter is auto-regulated by FliA and by a number of other regulators, most importantly the motility master regulator FlhDC. The expression of FlhDC itself is under the control of a variety of regulatory factors, including RpoS, CpxR, and CsgD. The activity of the genes in the figure is measured by fusion of their promoters to a gfp reporter gene on a low-copy plasmid. Genes are shown in grey or green and their promoter regions in red. Regulatory interactions are represented by open arrows, association and dissociation of FliA and FlgM as well as degradation and export by filled arrows. The figure does not explicitly show that fliA, flgM, and tar are included in larger transcriptional units, the fliAZY, flgAMN, flgMN and tar-tap-cheRBYZ operons [33].
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4295839&req=5

pcbi.1004028.g001: FliA-FlgM module.The regulatory circuit composed of the flagellar-specific transcription factor FliA, a sigma factor also known as σ28, and the anti-sigma factor FlgM forms a check-point in the transcriptional hierarchy of the motility genes in E. coli. While fliA is transcribed from a single class 2 promoter (pfliA), flgM is transcribed from both a class 2 and a class 3 promoter (pflgA and pflgM, respectively). FliA binds to RNA polymerase core enzyme and directs transcription from a total of five class 3 promoters [33], including ptar and pflgM. When bound to FlgM, FliA cannot activate transcription. When the hook basal-body (HBB) structure is in place, however, FlgM is exported from the cell, thus releasing FliA from the inactive complex. FliA is subject to proteolysis by Lon, but FlgM-binding protects FliA from degradation. The fliA promoter is auto-regulated by FliA and by a number of other regulators, most importantly the motility master regulator FlhDC. The expression of FlhDC itself is under the control of a variety of regulatory factors, including RpoS, CpxR, and CsgD. The activity of the genes in the figure is measured by fusion of their promoters to a gfp reporter gene on a low-copy plasmid. Genes are shown in grey or green and their promoter regions in red. Regulatory interactions are represented by open arrows, association and dissociation of FliA and FlgM as well as degradation and export by filled arrows. The figure does not explicitly show that fliA, flgM, and tar are included in larger transcriptional units, the fliAZY, flgAMN, flgMN and tar-tap-cheRBYZ operons [33].
Mentions: The more than 60 genes responsible for motility in bacteria are structured in a transcriptional hierarchy of three operon classes that has been mapped in detail for Escherichia coli and Salmonella enterica [27–29, 32]. The single class 1 operon flhDC encodes the proteins FlhD and FlhC, which form a heteromultimeric complex activating σ70-dependent transcription of the class 2 operons. The latter encode the proteins making up the flagellar motor structure as well as a major regulator of the class 3 operons, the sigma factor FliA (σ28). When bound to core RNA polymerase, FliA directs the transcription of the class 3 operons [33] that code for the proteins forming the filament structure of the flagellum and the chemotaxis sensing system. The aspartate chemoreceptor Tar is an example of such a class 3 protein. The action of FliA is counteracted by the anti-sigma factor FlgM, which binds to FliA and thus prevents its association with RNA polymerase. FlgM is encoded by the gene flgM, which is transcribed from both a class 2 promoter and a class 3 promoter. FlgM can be excreted from the cell through the center of the basal-body structure (Fig. 1).

Bottom Line: Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected.From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression.In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

View Article: PubMed Central - PubMed

Affiliation: INRIA Grenoble - Rhône-Alpes, Grenoble, France; Laboratoire Interdisciplinaire de Physique (LIPhy, CNRS UMR 5588), Université Joseph Fourier, Grenoble, France.

ABSTRACT
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Show MeSH
Related in: MedlinePlus