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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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Related in: MedlinePlus

Promoter activities of genes in the FliA-FlgM module.The promoter activities of fliA (green), flgM (red), and tar (blue) measured by means of fluorescent reporter genes in the following experimental conditions: ΔrpoS strain grown in M9 (ΔrpoS-M9), ΔcpxR strain grown in M9 (ΔcpxR-M9), ΔcsgD strain grown in M9 (ΔcsgD-M9), ΔcsgD strain grown in LB (ΔcsgD-LB), and wild-type strain grown in LB (WT-LB). Grey lines report mean absorbance measurements in the various conditions. The promoter activities and absorbance profiles have been derived from the primary data as illustrated in Fig. 2.
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pcbi.1004028.g003: Promoter activities of genes in the FliA-FlgM module.The promoter activities of fliA (green), flgM (red), and tar (blue) measured by means of fluorescent reporter genes in the following experimental conditions: ΔrpoS strain grown in M9 (ΔrpoS-M9), ΔcpxR strain grown in M9 (ΔcpxR-M9), ΔcsgD strain grown in M9 (ΔcsgD-M9), ΔcsgD strain grown in LB (ΔcsgD-LB), and wild-type strain grown in LB (WT-LB). Grey lines report mean absorbance measurements in the various conditions. The promoter activities and absorbance profiles have been derived from the primary data as illustrated in Fig. 2.

Mentions: In each of the experimental conditions, we have acquired 5 to 8 replicate measurements, which makes it possible to estimate the uncertainty in the derived promoter activities. Fig. 3 shows the results for the five conditions considered here: (i) ΔrpoS strain grown in M9 (ΔrpoS-M9), (ii) ΔcpxR strain grown in M9 (ΔcpxR-M9), (iii) ΔcsgD strain grown in M9 (ΔcsgD-M9),(iv) ΔcsgD strain grown in LB (ΔcsgD-LB), and (v) wild-type strain grown in LB (WT-LB). As expected [36], the fluorescence signals in the wild-type strain grown in minimal M9 medium with glucose were mostly indistinguishable from the background fluorescence and therefore this condition was not further considered. In one condition (WT-LB), the activities measured by means of reporter genes were validated using RT-qPCR (Text S6).


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)

Promoter activities of genes in the FliA-FlgM module.The promoter activities of fliA (green), flgM (red), and tar (blue) measured by means of fluorescent reporter genes in the following experimental conditions: ΔrpoS strain grown in M9 (ΔrpoS-M9), ΔcpxR strain grown in M9 (ΔcpxR-M9), ΔcsgD strain grown in M9 (ΔcsgD-M9), ΔcsgD strain grown in LB (ΔcsgD-LB), and wild-type strain grown in LB (WT-LB). Grey lines report mean absorbance measurements in the various conditions. The promoter activities and absorbance profiles have been derived from the primary data as illustrated in Fig. 2.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004028.g003: Promoter activities of genes in the FliA-FlgM module.The promoter activities of fliA (green), flgM (red), and tar (blue) measured by means of fluorescent reporter genes in the following experimental conditions: ΔrpoS strain grown in M9 (ΔrpoS-M9), ΔcpxR strain grown in M9 (ΔcpxR-M9), ΔcsgD strain grown in M9 (ΔcsgD-M9), ΔcsgD strain grown in LB (ΔcsgD-LB), and wild-type strain grown in LB (WT-LB). Grey lines report mean absorbance measurements in the various conditions. The promoter activities and absorbance profiles have been derived from the primary data as illustrated in Fig. 2.
Mentions: In each of the experimental conditions, we have acquired 5 to 8 replicate measurements, which makes it possible to estimate the uncertainty in the derived promoter activities. Fig. 3 shows the results for the five conditions considered here: (i) ΔrpoS strain grown in M9 (ΔrpoS-M9), (ii) ΔcpxR strain grown in M9 (ΔcpxR-M9), (iii) ΔcsgD strain grown in M9 (ΔcsgD-M9),(iv) ΔcsgD strain grown in LB (ΔcsgD-LB), and (v) wild-type strain grown in LB (WT-LB). As expected [36], the fluorescence signals in the wild-type strain grown in minimal M9 medium with glucose were mostly indistinguishable from the background fluorescence and therefore this condition was not further considered. In one condition (WT-LB), the activities measured by means of reporter genes were validated using RT-qPCR (Text S6).

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