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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.


Comparison of the malonyl-CoA levels produced by various constructs. Fluorescence observed at OD = 0.6, generated by pBFR1k_RFP_8FapR upon induction of the corresponding producer plasmid with 1 mM IPTG in a BL21DE3 host is depicted. Na-malonate was added for pACYCmatCmatB and pACYCmatCatoDA, and β-alanine was administered for pACYCmmsA and pACYCcagg1256. Each letter represents a value significantly differing from the others [i.e., if two value were found to significantly differ from each other (p < 0.05), then the letters corresponding to their bars do not overlap].
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Figure 13: Comparison of the malonyl-CoA levels produced by various constructs. Fluorescence observed at OD = 0.6, generated by pBFR1k_RFP_8FapR upon induction of the corresponding producer plasmid with 1 mM IPTG in a BL21DE3 host is depicted. Na-malonate was added for pACYCmatCmatB and pACYCmatCatoDA, and β-alanine was administered for pACYCmmsA and pACYCcagg1256. Each letter represents a value significantly differing from the others [i.e., if two value were found to significantly differ from each other (p < 0.05), then the letters corresponding to their bars do not overlap].

Mentions: In this work, we have successfully implemented a malonyl-CoA sensor for the partial optimization of malonyl-CoA production in E. coli BL21DE3. Initially, we screened several conditions to be used for the measurements, and found the optimal one to match that described earlier (Liu et al., 2015), despite the possible changes in relative gene dosage caused by the single-plasmid vs. dual-plasmid layout. We constructed a chloramphenicol-resistant version of the plasmid as well, to compare our construct collection encoding four alternative pathways for malonyl-CoA production, predicted by RetroPath. Besides their comparison, our aim was to use malonyl-CoA sensing to seek optimal conditions for efficient malonyl-CoA production, previously seen to be hindered by growth retardation (Fehér et al., 2014). Our current results also indicated, that the viability of the cells can be, in many cases strongly impaired by the high expression levels of the original pRSF-based constructs (Figure S3 in Supplementary Material). It was also apparent, that the lower-copy alternatives available for the accABCD gene complex (pMSD8, pETM6-MaccABCD, and pETM6-PaccABCD) outperformed all or most members of the RSF-collection. Therefore, a partial optimization of expression levels was carried out by using smaller copy-number vectors to obtain viable cells with measurable malonyl-CoA production upon induction, leading to a successful circumvention of the possible toxicity caused by the high-copy expression vectors. Due to the relatively high variation of the resulting data, comparison of these constructs was not conclusive enough to validate all our predicted rankings. However, the obtained measurements were quite useful for the practical purpose of selecting the most efficient implementation of our malonyl-CoA-producing constructs. From this aspect, the best performer of our construct set (in terms of fluorescence at OD = 0.6) turned out to be pACYCmatCatoDA carrying the malonate-CoA transferase on a P15a-derived low-copy-number plasmid (Figure 13). Its performance could be nonetheless matched by the expression of the acetyl-CoA carboxylase complex, which, in turn was indiscernible from the matB. The two genes encoding malonyl-CoA reductase (mmsA and cagg1256) performed significantly weaker. Importantly, the activity of these constructs uncovered two pathways, malonyl-CoA reductase and malonate-CoA transferase, which have not been implemented before in boosting malonyl-CoA biosynthesis.


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)

Comparison of the malonyl-CoA levels produced by various constructs. Fluorescence observed at OD = 0.6, generated by pBFR1k_RFP_8FapR upon induction of the corresponding producer plasmid with 1 mM IPTG in a BL21DE3 host is depicted. Na-malonate was added for pACYCmatCmatB and pACYCmatCatoDA, and β-alanine was administered for pACYCmmsA and pACYCcagg1256. Each letter represents a value significantly differing from the others [i.e., if two value were found to significantly differ from each other (p < 0.05), then the letters corresponding to their bars do not overlap].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 13: Comparison of the malonyl-CoA levels produced by various constructs. Fluorescence observed at OD = 0.6, generated by pBFR1k_RFP_8FapR upon induction of the corresponding producer plasmid with 1 mM IPTG in a BL21DE3 host is depicted. Na-malonate was added for pACYCmatCmatB and pACYCmatCatoDA, and β-alanine was administered for pACYCmmsA and pACYCcagg1256. Each letter represents a value significantly differing from the others [i.e., if two value were found to significantly differ from each other (p < 0.05), then the letters corresponding to their bars do not overlap].
Mentions: In this work, we have successfully implemented a malonyl-CoA sensor for the partial optimization of malonyl-CoA production in E. coli BL21DE3. Initially, we screened several conditions to be used for the measurements, and found the optimal one to match that described earlier (Liu et al., 2015), despite the possible changes in relative gene dosage caused by the single-plasmid vs. dual-plasmid layout. We constructed a chloramphenicol-resistant version of the plasmid as well, to compare our construct collection encoding four alternative pathways for malonyl-CoA production, predicted by RetroPath. Besides their comparison, our aim was to use malonyl-CoA sensing to seek optimal conditions for efficient malonyl-CoA production, previously seen to be hindered by growth retardation (Fehér et al., 2014). Our current results also indicated, that the viability of the cells can be, in many cases strongly impaired by the high expression levels of the original pRSF-based constructs (Figure S3 in Supplementary Material). It was also apparent, that the lower-copy alternatives available for the accABCD gene complex (pMSD8, pETM6-MaccABCD, and pETM6-PaccABCD) outperformed all or most members of the RSF-collection. Therefore, a partial optimization of expression levels was carried out by using smaller copy-number vectors to obtain viable cells with measurable malonyl-CoA production upon induction, leading to a successful circumvention of the possible toxicity caused by the high-copy expression vectors. Due to the relatively high variation of the resulting data, comparison of these constructs was not conclusive enough to validate all our predicted rankings. However, the obtained measurements were quite useful for the practical purpose of selecting the most efficient implementation of our malonyl-CoA-producing constructs. From this aspect, the best performer of our construct set (in terms of fluorescence at OD = 0.6) turned out to be pACYCmatCatoDA carrying the malonate-CoA transferase on a P15a-derived low-copy-number plasmid (Figure 13). Its performance could be nonetheless matched by the expression of the acetyl-CoA carboxylase complex, which, in turn was indiscernible from the matB. The two genes encoding malonyl-CoA reductase (mmsA and cagg1256) performed significantly weaker. Importantly, the activity of these constructs uncovered two pathways, malonyl-CoA reductase and malonate-CoA transferase, which have not been implemented before in boosting malonyl-CoA biosynthesis.

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.