Limits...
A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Fehér T, Libis V, Carbonell P, Faulon JL - Front Bioeng Biotechnol (2015)

Bottom Line: Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis.In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels.The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences , Szeged , Hungary.

ABSTRACT
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

No MeSH data available.


Fluorescence generated by the pBFR1k_RFP_8FapR sensor plasmid in response to various arabinose and IPTG concentrations. (A–D) depict control BL21DE3 cells harboring only the pBFR1k_RFP_8FapR plasmid, while (E–H) show BL21DE3 cells carrying the pBFR1k_RFP_8FapR and the pACYCmatCmatB plasmids. Arabinose concentrations are shown under each pair of graphs. IPTG concentrations are as follows: blue: 0.001 mM; purple: 0.01 mM; green: 0.1 mM; orange: 1 mM. Na-malonate was included in the medium for all cultures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4389729&req=5

Figure 2: Fluorescence generated by the pBFR1k_RFP_8FapR sensor plasmid in response to various arabinose and IPTG concentrations. (A–D) depict control BL21DE3 cells harboring only the pBFR1k_RFP_8FapR plasmid, while (E–H) show BL21DE3 cells carrying the pBFR1k_RFP_8FapR and the pACYCmatCmatB plasmids. Arabinose concentrations are shown under each pair of graphs. IPTG concentrations are as follows: blue: 0.001 mM; purple: 0.01 mM; green: 0.1 mM; orange: 1 mM. Na-malonate was included in the medium for all cultures.

Mentions: In its original publication by Liu et al. (2015), the malonyl-CoA sensor was characterized in a split form: the FapR and RFP genes were carried by two separate plasmids. Therefore, we re-characterized pBFR1k_RFP_8FapR in order to find the optimal conditions of comparing various malonyl-CoA producer enzymes using the single-plasmid sensor construct. The expression level of the FapR repressor was varied by administering a dilution series of arabinose, while the expression level of the malonate transporter (matC) and the matB from plasmid pACYCmatCmatB was controlled by altering the IPTG concentration. A 4 × 4 concentration matrix was thus applied to cells carrying either both plasmids or just the sensor plasmid. As apparent from the OD-normalized fluorescence values shown on Figure 2, an arabinose concentration of 0.01% proved to be optimal, taking both specificity and sensitivity into account, similarly to the originally published sensor system. At lower arabinose concentrations, specificity deteriorated, since the sensor plasmid produced slight increases in fluorescence in response to IPTG even in the absence of the malonyl-CoA producer plasmid. This could indicate the promoter’s non-specific binding to LacI, which is competitively inhibited by sufficient quantities of the FapR protein. Increasing arabinose levels to 0.1% strongly reduced fluorescence levels of the sample, leading to a decrease of sensitivity and of the signal to noise ratio, possibly resulting from an over-repression by FapR. The initial decrease in fluorescence/OD values, seen on all graphs is likely due to the delay of RFP expression compared to culture growth.


A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Fehér T, Libis V, Carbonell P, Faulon JL - Front Bioeng Biotechnol (2015)

Fluorescence generated by the pBFR1k_RFP_8FapR sensor plasmid in response to various arabinose and IPTG concentrations. (A–D) depict control BL21DE3 cells harboring only the pBFR1k_RFP_8FapR plasmid, while (E–H) show BL21DE3 cells carrying the pBFR1k_RFP_8FapR and the pACYCmatCmatB plasmids. Arabinose concentrations are shown under each pair of graphs. IPTG concentrations are as follows: blue: 0.001 mM; purple: 0.01 mM; green: 0.1 mM; orange: 1 mM. Na-malonate was included in the medium for all cultures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389729&req=5

Figure 2: Fluorescence generated by the pBFR1k_RFP_8FapR sensor plasmid in response to various arabinose and IPTG concentrations. (A–D) depict control BL21DE3 cells harboring only the pBFR1k_RFP_8FapR plasmid, while (E–H) show BL21DE3 cells carrying the pBFR1k_RFP_8FapR and the pACYCmatCmatB plasmids. Arabinose concentrations are shown under each pair of graphs. IPTG concentrations are as follows: blue: 0.001 mM; purple: 0.01 mM; green: 0.1 mM; orange: 1 mM. Na-malonate was included in the medium for all cultures.
Mentions: In its original publication by Liu et al. (2015), the malonyl-CoA sensor was characterized in a split form: the FapR and RFP genes were carried by two separate plasmids. Therefore, we re-characterized pBFR1k_RFP_8FapR in order to find the optimal conditions of comparing various malonyl-CoA producer enzymes using the single-plasmid sensor construct. The expression level of the FapR repressor was varied by administering a dilution series of arabinose, while the expression level of the malonate transporter (matC) and the matB from plasmid pACYCmatCmatB was controlled by altering the IPTG concentration. A 4 × 4 concentration matrix was thus applied to cells carrying either both plasmids or just the sensor plasmid. As apparent from the OD-normalized fluorescence values shown on Figure 2, an arabinose concentration of 0.01% proved to be optimal, taking both specificity and sensitivity into account, similarly to the originally published sensor system. At lower arabinose concentrations, specificity deteriorated, since the sensor plasmid produced slight increases in fluorescence in response to IPTG even in the absence of the malonyl-CoA producer plasmid. This could indicate the promoter’s non-specific binding to LacI, which is competitively inhibited by sufficient quantities of the FapR protein. Increasing arabinose levels to 0.1% strongly reduced fluorescence levels of the sample, leading to a decrease of sensitivity and of the signal to noise ratio, possibly resulting from an over-repression by FapR. The initial decrease in fluorescence/OD values, seen on all graphs is likely due to the delay of RFP expression compared to culture growth.

Bottom Line: Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis.In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels.The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences , Szeged , Hungary.

ABSTRACT
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

No MeSH data available.