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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.


Variability of parameters K, τp, τz of the sensor for each construct at several IPTG concentrations (0.01, 0.1, 0.3, 0.6, 1, and 10 mM). Calculated correlation between parameters K, τp, τz, and IPTG levels are shown.
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Figure 12: Variability of parameters K, τp, τz of the sensor for each construct at several IPTG concentrations (0.01, 0.1, 0.3, 0.6, 1, and 10 mM). Calculated correlation between parameters K, τp, τz, and IPTG levels are shown.

Mentions: According to our simplified model of the sensor consisting on a second-order transfer function, gain between biomass and RFP should depend on the level of induction of the malonyl-CoA-producing enzyme. Time constants, on the contrary, should not depend on the level of induction, but should be constitutive parameters of the sensor that can be calibrated for each construct. As shown in Figure 11, we found effectively a good correlation between IPTG levels and sensor gain (r = 0.72, p-value = 8.1 × 10−6), indicating that the sensor is responsive to changes in induction of the enzyme. Time constants, as expected, were not significantly affected by changes in the IPTG concentration. These time constants, on the other hand, appear as distinctively clustered for each construct, showing the robustness of the sensor (Figure 12).


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)

Variability of parameters K, τp, τz of the sensor for each construct at several IPTG concentrations (0.01, 0.1, 0.3, 0.6, 1, and 10 mM). Calculated correlation between parameters K, τp, τz, and IPTG levels are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 12: Variability of parameters K, τp, τz of the sensor for each construct at several IPTG concentrations (0.01, 0.1, 0.3, 0.6, 1, and 10 mM). Calculated correlation between parameters K, τp, τz, and IPTG levels are shown.
Mentions: According to our simplified model of the sensor consisting on a second-order transfer function, gain between biomass and RFP should depend on the level of induction of the malonyl-CoA-producing enzyme. Time constants, on the contrary, should not depend on the level of induction, but should be constitutive parameters of the sensor that can be calibrated for each construct. As shown in Figure 11, we found effectively a good correlation between IPTG levels and sensor gain (r = 0.72, p-value = 8.1 × 10−6), indicating that the sensor is responsive to changes in induction of the enzyme. Time constants, as expected, were not significantly affected by changes in the IPTG concentration. These time constants, on the other hand, appear as distinctively clustered for each construct, showing the robustness of the sensor (Figure 12).

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.