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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 response of BL21DE3 cells carrying pBFR1k_RFP_8FapR sensor plasmid and (A) pACYCmmsA, (B) pACYCcagg1256, (C) pACYCMaccABCD, or (D) pACYCmatCatoDA. The medium was supplemented with β-alanine for pACYCmmsA and pACYCcagg1256, and with Na-malonate for pACYCmatCatoDA. Colors indicate the concentration of IPTG used for induction: dark blue: 0.01 mM; magenta: 0.1 mM; yellow: 0.3 mM; cyan: 0.6 mM; purple: 1 mM; orange: 10 mM.
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Figure 6: Fluorescence response of BL21DE3 cells carrying pBFR1k_RFP_8FapR sensor plasmid and (A) pACYCmmsA, (B) pACYCcagg1256, (C) pACYCMaccABCD, or (D) pACYCmatCatoDA. The medium was supplemented with β-alanine for pACYCmmsA and pACYCcagg1256, and with Na-malonate for pACYCmatCatoDA. Colors indicate the concentration of IPTG used for induction: dark blue: 0.01 mM; magenta: 0.1 mM; yellow: 0.3 mM; cyan: 0.6 mM; purple: 1 mM; orange: 10 mM.

Mentions: These results prompted us to subclone our pathway collection into a pACYC backbone, thereby reducing the copy-number of all constructs to ~15. When expressed this way, all five constructs (matCmatB, mmsA, cagg1256, M.accABCD, and matCatoDA) allowed cell growth and displayed a scalable, IPTG-dependent fluorescence (Figures 2 and 6). As it turned out, the concise nature of the dataset obtained with the pACYC-collection allowed us to fit various models describing the response of the sensor to malonyl-CoA production (see below).


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 response of BL21DE3 cells carrying pBFR1k_RFP_8FapR sensor plasmid and (A) pACYCmmsA, (B) pACYCcagg1256, (C) pACYCMaccABCD, or (D) pACYCmatCatoDA. The medium was supplemented with β-alanine for pACYCmmsA and pACYCcagg1256, and with Na-malonate for pACYCmatCatoDA. Colors indicate the concentration of IPTG used for induction: dark blue: 0.01 mM; magenta: 0.1 mM; yellow: 0.3 mM; cyan: 0.6 mM; purple: 1 mM; orange: 10 mM.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Fluorescence response of BL21DE3 cells carrying pBFR1k_RFP_8FapR sensor plasmid and (A) pACYCmmsA, (B) pACYCcagg1256, (C) pACYCMaccABCD, or (D) pACYCmatCatoDA. The medium was supplemented with β-alanine for pACYCmmsA and pACYCcagg1256, and with Na-malonate for pACYCmatCatoDA. Colors indicate the concentration of IPTG used for induction: dark blue: 0.01 mM; magenta: 0.1 mM; yellow: 0.3 mM; cyan: 0.6 mM; purple: 1 mM; orange: 10 mM.
Mentions: These results prompted us to subclone our pathway collection into a pACYC backbone, thereby reducing the copy-number of all constructs to ~15. When expressed this way, all five constructs (matCmatB, mmsA, cagg1256, M.accABCD, and matCatoDA) allowed cell growth and displayed a scalable, IPTG-dependent fluorescence (Figures 2 and 6). As it turned out, the concise nature of the dataset obtained with the pACYC-collection allowed us to fit various models describing the response of the sensor to malonyl-CoA production (see below).

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.