Abstract
Genetically-encoded sensors are used to control protein and metabolite production in bacterial fermentations. However, these sensors are generally optimized for exponential growth rather than stationary phase where production occurs. Here, we find that our previously engineered E. coli green light sensor CcaSR, which functions robustly in exponential phase, fails in stationary phase due to spontaneous loss of an engineered chromophore biosynthetic pathway and accumulation of CcaS and CcaR. We optimize the genetic context and expression determinants of each component, resulting in a stable system named CcaSRstat that imposes little metabolic burden, exhibits low leakiness and an 80-fold green light response, and functions exclusively in stationary phase. We combine CcaSR…
Abstract
Genetically-encoded sensors are used to control protein and metabolite production in bacterial fermentations. However, these sensors are generally optimized for exponential growth rather than stationary phase where production occurs. Here, we find that our previously engineered E. coli green light sensor CcaSR, which functions robustly in exponential phase, fails in stationary phase due to spontaneous loss of an engineered chromophore biosynthetic pathway and accumulation of CcaS and CcaR. We optimize the genetic context and expression determinants of each component, resulting in a stable system named CcaSRstat that imposes little metabolic burden, exhibits low leakiness and an 80-fold green light response, and functions exclusively in stationary phase. We combine CcaSRstat-driven enzyme expression with varied static and periodic illumination patterns to achieve high titers of the industrially-relevant phenylpropanoid p-Coumaric acid and demonstrate that these optimizations scale to benchtop bioreactor conditions. Finally, we use CcaSRstat to optimize the expression level of a co-transcribed multi-enzyme metabolic pathway encoding production of plant-derived betaxanthin family pigments. Stationary phase-optimized bacterial sensors should enhance fermentation productivity by enabling rapid interrogation of the impact of enzyme expression level and induction dynamics.
Data availability
The raw flow cytometry data (.fcs) files for Figs. 1c, 3b–d, 3f–h and 4c, d, Supplementary Figs. 1c–j, 3d, 5b, d, f, 6b, 7c–f, 8b, c, 9b–f, 20b and 23b, c, raw HPLC data (.lcd) files for Figs. 4f, g and 5b, d, Supplementary Figs. 18d and 21b, raw LC-MS data (.d) files for Supplementary Figs. 10b–d and 11–14, raw gel images for Fig. 2b, Supplementary Figs. 2c and 4b, and raw sequencing data for Supplementary Fig. 2 have been posted to Figshare (https://doi.org/10.6084/m9.figshare.30203842)95. ho1-pcyA specific primers and plasmids, strains, plasmids, and genetic parts used in this study are listed in Supplementary Data 1–4, respectively. CcaSRstat ccaS (pJTL269), ccaR (pJTL257.2), PcpcG2-172:sfgfp (pJTL256), PcpcG2-172:tal (pJTL282), PcpcG2-172:tal-sfgfp (pJTL283), and PcpcG2-172:Betaxanthin pathway (pDJH019) plasmids, and the cph1(Y176H) expression plasmid (pSC0025) are available from Addgene with Accession IDs given in Supplementary Data 3. All strains and other plasmids used in this study are available under a Materials Transfer Agreement with Rice University upon request to J.J.T. Requests will be processed within 2 weeks. Source data are provided with this paper.
Code availability
Custom Python code for the generation of light pulsing functions with the LPA is available on GitHub (https://doi.org/10.5281/zenodo.17634803)96.
References
Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).
Murphy, A. C. Metabolic engineering is key to a sustainable chemical industry. Nat. Prod. Rep. 28, 1406 (2011).
Woolston, B. M., Edgar, S. & Stephanopoulos, G. Metabolic engineering: past and future. Annu. Rev. Chem. Biomol. Eng. 4, 259–288 (2013).
Zhang, J. et al. A microbial supply chain for production of the anti-cancer drug vinblastine. Nature 609, 341–347 (2022).
Luo, X. et al. Complete biosynthesis of cannabinoids and their unnatural analogues in yeast. Nature 567, 123–126 (2019).
Liew, F. E. et al. Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale. Nat. Biotechnol. 40, 335–344 (2022).
Nyström, T. Stationary-phase physiology. Annu. Rev. Microbiol 58, 161–181 (2004).
Jaishankar, J. & Srivastava, P. Molecular basis of stationary phase survival and applications. Front. Microbiol. 8, 2000 (2017). 1.
Ou, J. et al. Stationary phase protein overproduction is a fundamental capability of Escherichia coli. Biochem. Biophys. Res. Commun. 314, 174–180 (2004).
Venayak, N., Anesiadis, N., Cluett, W. R. & Mahadevan, R. Engineering metabolism through dynamic control. Curr. Opin. Biotechnol. 34, 142–152 (2015).
Hartline, C. J., Schmitz, A. C., Han, Y. & Zhang, F. Dynamic control in metabolic engineering: theories, tools, and applications. Metab. Eng. 63, 126–140 (2021).
Lalwani, M. A., Zhao, E. M. & Avalos, J. L. Current and future modalities of dynamic control in metabolic engineering. Curr. Opin. Biotechnol. 52, 56–65 (2018).
Lo, T.-M., Chng, S. H., Teo, W. S., Cho, H.-S. & Chang, M. W. A two-layer gene circuit for decoupling cell growth from metabolite production. Cell Syst. 3, 133–143 (2016).
Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2019).
Biggs, B. W., De Paepe, B., Santos, C. N. S., De Mey, M. & Kumaran Ajikumar, P. Multivariate modular metabolic engineering for pathway and strain optimization. Curr. Opin. Biotechnol. 29, 156–162 (2014).
Jones, J. A. & Koffas, M. A. G. Optimizing metabolic pathways for the improved production of natural products. 179–193. https://doi.org/10.1016/bs.mie.2016.02.010 (2016). 1.
Walsh, K. & Koshland, D. E. Characterization of rate-controlling steps in vivo by use of an adjustable expression vector. Proc. Natl. Acad. Sci. USA 82, 3577–3581 (1985).
Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997).
Khlebnikov, A., Datsenko, K. A., Skaug, T., Wanner, B. L. & Keasling, J. D. Homogeneous expression of the PBAD promoter in Escherichia coli by constitutive expression of the low-affinity high-capacity AraE transporter. Microbiology 147, 3241 (2001).
Chen, Y. et al. Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors. Nat. Commun. 9, 64 (2018).
Chang, D.-E., Smalley, D. J. & Conway, T. Gene expression profiling of Escherichia coli growth transitions: an expanded stringent response model. Mol. Microbiol. 45, 289–306 (2002).
Gefen, O., Fridman, O., Ronin, I. & Balaban, N. Q. Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. Proc. Natl. Acad. Sci. USA 111, 556–561 (2014).
Schmidl, S. R., Sheth, R. U., Wu, A. & Tabor, J. J. Refactoring and optimization of light-switchable Escherichia coli two-component systems. ACS Synth. Biol. 3, 820–831 (2014).
Carrasco-López, C., García-Echauri, S. A., Kichuk, T. & Avalos, J. L. Optogenetics and biosensors set the stage for metabolic cybergenetics. Curr. Opin. Biotechnol. 65, 296–309 (2020).
Wegner, S. A., Barocio-Galindo, R. M. & Avalos, J. L. The bright frontiers of microbial metabolic optogenetics. Curr. Opin. Chem. Biol. 71, 102207 (2022).
Pouzet, S. et al. The promise of optogenetics for bioproduction: dynamic control strategies and scale-up instruments. Bioengineering 7, 151 (2020).
Zhao, E. M. et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687 (2018).
Lalwani, M. A. et al. Optogenetic control of the lac operon for bacterial chemical and protein production. Nat. Chem. Biol. 17, 71–79 (2021).
Olson, E. J. & Tabor, J. J. Optogenetic characterization methods overcome key challenges in synthetic and systems biology. Nat. Chem. Biol. 10, 502–511 (2014).
Olson, E. J., Hartsough, L. A., Landry, B. P., Shroff, R. & Tabor, J. J. Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nat. Methods 11, 449–455 (2014).
Tabor, J. J., Levskaya, A. & Voigt, C. A. Multichromatic control of gene expression in Escherichia coli. J. Mol. Biol. 405, 315–324 (2011).
Akagi, H., Shimizu, H. & Toya, Y. Multicolor optogenetics for regulating flux ratio of three glycolytic pathways using EL222 and CcaSR in Escherichia coli. Biotechnol. Bioeng. 121, 1016–1025 (2024).
Hueso-Gil, A., Nyerges, Á, Pál, C., Calles, B. & de Lorenzo, V. Multiple-site diversification of regulatory sequences enables interspecies operability of genetic devices. ACS Synth. Biol. 9, 104–114 (2020).
Castillo-Hair, S. M., Baerman, E. A., Fujita, M., Igoshin, O. A. & Tabor, J. J. Optogenetic control of Bacillus subtilis gene expression. Nat. Commun. 10, 3099 (2019).
Forbes, L., Papanatsiou, M., Palombo, A., Christie, J. M. & Amtmann, A. Optogenetic control of gene expression in the cyanobacterium Synechococcus sp. PCC 7002. Front. Bioeng. Biotechnol. 12, 1529022 (2025).
Chait, R., Ruess, J., Bergmiller, T., Tkačik, G. & Guet, C. C. Shaping bacterial population behavior through computer-interfaced control of individual cells. Nat. Commun. 8, 1535 (2017).
Wang, S. et al. Development of optogenetic dual-switch system for rewiring metabolic flux for polyhydroxybutyrate production. Biotechnol. Bioeng. 119, 2345–2355 (2022).
Senoo, S. et al. Light-inducible flux control of triosephosphate isomerase on glycolysis in Escherichia coli. Biotechnol. Bioeng. 116, 3292–3300 (2019).
Lugagne, J.-B., Blassick, C. M. & Dunlop, M. J. Deep model predictive control of gene expression in thousands of single cells. Nat. Commun. 15, 2148 (2024).
Wang, J. et al. Implementing optogenetic-controlled bacterial systems in Drosophila melanogaster for alleviation of heavy metal poisoning. ACS Synth. Biol. 13, 3312–3325 (2024).
Fernandez-Rodriguez, J., Moser, F., Song, M. & Voigt, C. A. Engineering RGB color vision into Escherichia coli. Nat. Chem. Biol. 13, 706–708 (2017).
Milias-Argeitis, A., Rullan, M., Aoki, S. K., Buchmann, P. & Khammash, M. Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun. 7, 12546 (2016).
Larsen, B. et al. Highlighter: An optogenetic system for high-resolution gene expression control in plants. Nat. Commun. 14, 5159 (2023).
Tandar, S. T., Senoo, S., Toya, Y. & Shimizu, H. Optogenetic switch for controlling the central metabolic flux of Escherichia coli. Metab. Eng. 55, 68–75 (2019).
Hartsough, L. A. et al. Optogenetic control of gut bacterial metabolism to promote longevity. eLife 9, e56849 (2020). 1.
Olson, E. J., Tzouanas, C. N. & Tabor, J. J. A photoconversion model for full spectral programming and multiplexing of optogenetic systems. Mol. Syst. Biol. 13, 926 (2017). 1.
Ong, N. T. & Tabor, J. J. A miniaturized Escherichia coli green light sensor with high dynamic range. ChemBioChem 19, 1255–1258 (2018).
Shao, B. et al. Single-cell measurement of plasmid copy number and promoter activity. Nat. Commun. 12, 1475 (2021).
Zhao, X., Gao, H., Wang, Y., Wang, Z. & Zhou, J. Efficient synthesis of phycocyanobilin by combinatorial metabolic engineering in Escherichia coli. ACS Synth. Biol. 11, 2089–2097 (2022).
Chen, Y.-J. et al. Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat. Methods 10, 659–664 (2013).
Fischer, A. J. & Lagarias, J. C. Harnessing phytochrome’s glowing potential. Proc. Natl. Acad. Sci. USA 101, 17334–17339 (2004).
Fischer, A. J. et al. Multiple roles of a conserved GAF domain tyrosine residue in cyanobacterial and plant phytochromes. Biochemistry 44, 15203–15215 (2005).
Saleski, T. E. et al. Optimized gene expression from bacterial chromosome by high-throughput integration and screening. Sci. Adv. 7, eabe1767 (2021). 1.
St-Pierre, F. et al. One-step cloning and chromosomal integration of DNA. ACS Synth. Biol. 2, 537–541 (2013).
Lazar, J. T. & Tabor, J. J. Bacterial two-component systems as sensors for synthetic biology applications. Curr. Opin. Syst. Biol. 28, 100398 (2021).
Yang, D., Park, S. Y., Park, Y. S., Eun, H. & Lee, S. Y. Metabolic engineering of Escherichia coli for natural product biosynthesis. Trends Biotechnol. 38, 745–765 (2020).
Ververidis, F. et al. Biotechnology of flavonoids and other phenylpropanoid-derived natural products. Part I: chemical diversity, impacts on plant biology and human health. Biotechnol. J. 2, 1214–1234 (2007).
Korkina, L. G. Phenylpropanoids as naturally occurring antioxidants: from plant defense to human health. Cell. Mol. Biol. 53, 15–25 (2007).
Neelam, Khatkar, A. & Sharma, K. K. Phenylpropanoids and its derivatives: biological activities and its role in food, pharmaceutical and cosmetic industries. Crit. Rev. Food Sci. Nutr. 60, 2655–2675 (2020).
Santos, C. N. S., Koffas, M. & Stephanopoulos, G. Optimization of a heterologous pathway for the production of flavonoids from glucose. Metab. Eng. 13, 392–400 (2011).
Sariaslani, F. S. Development of a combined biological and chemical process for production of industrial aromatics from renewable resources. Annu. Rev. Microbiol 61, 51–69 (2007).
Cui, P. et al. Characterization of two new aromatic amino acid lyases from actinomycetes for highly efficient production of p-coumaric acid. Bioprocess Biosyst. Eng. 43, 1287–1298 (2020).
Chhikara, N., Kushwaha, K., Sharma, P., Gat, Y. & Panghal, A. Bioactive compounds of beetroot and utilization in food processing industry: a critical review. Food Chem. 272, 192–200 (2020).
Polturak, G. & Aharoni, A. “La Vie en Rose”: biosynthesis, sources, and applications of betalain pigments. Mol. Plant 11, 7–22 (2018).
Hou, Y. et al. Metabolic engineering of Escherichia coli for de novo production of betaxanthins. J. Agric. Food Chem. 68, 8370–8380 (2020).
Guerrero-Rubio, M. A., López-Llorca, R., Henarejos-Escudero, P., García-Carmona, F. & Gandía-Herrero, F. Scaled-up biotechnological production of individual betalains in a microbial system. Microb. Biotechnol. 12, 993–1002 (2019).
DeLoache, W. C. et al. An enzyme-coupled biosensor enables (S)-reticuline production in yeast from glucose. Nat. Chem. Biol. 11, 465–471 (2015).
Gandía-Herrero, F., Escribano, J. & García-Carmona, F. Structural implications on color, fluorescence, and antiradical activity in betalains. Planta 232, 449–460 (2010).
Steel, H., Habgood, R., Kelly, C. L. & Papachristodoulou, A. In situ characterisation and manipulation of biological systems with Chi.Bio. PLoS Biol. 18, e3000794 (2020).
Kumar, S. & Hasty, J. Stability, robustness, and containment: preparing synthetic biology for real-world deployment. Curr. Opin. Biotechnol. 79, 102880 (2023).
Chemla, Y., Sweeney, C. J., Wozniak, C. A. & Voigt, C. A. Design and regulation of engineered bacteria for environmental release. Nat. Microbiol. 10, 281–300 (2025).
Moser, F. et al. Genetic circuit performance under conditions relevant for industrial fermentation. ACS Synth. Biol. 1, 555–564 (2012).
Loewe, L., Textor, V. & Scherer, S. High deleterious genomic mutation rate in stationary phase of Escherichia coli. Science 302, 1558–1560 (2003).
Amrofell, M. B. & Moon, T. S. Characterizing a propionate sensor in E. coli Nissle 1917. ACS Synth. Biol. 12, 1868–1873 (2023).
Sleight, S. C., Bartley, B. A., Lieviant, J. A. & Sauro, H. M. Designing and engineering evolutionary robust genetic circuits. J. Biol. Eng. 4, 12 (2010).
Gupta, A., Reizman, I. M. B., Reisch, C. R. & Prather, K. L. J. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat. Biotechnol. 35, 273–279 (2017).
Guido, N. J., Lee, P., Wang, X., Elston, T. C. & Collins, J. J. A pathway and genetic factors contributing to elevated gene expression noise in stationary phase. Biophys. J. 93, L55–L57 (2007).
Dahl, R. H. et al. Engineering dynamic pathway regulation using stress-response promoters. Nat. Biotechnol. 31, 1039–1046 (2013).
Chen, Z. & Elowitz, M. B. Programmable protein circuit design. Cell 184, 2284–2301 (2021).
Chen, X. et al. An extraordinary stringent and sensitive light-switchable gene expression system for bacterial cells. Cell Res. 26, 854–857 (2016).
Multamäki, E. et al. Optogenetic control of bacterial expression by red light. ACS Synth. Biol. 11, 3354–3367 (2022).
Daeffler, K. N. et al. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Mol. Syst. Biol. 13, 923 (2017). 1.
Brink, K. R. et al. An E. coli display method for characterization of peptide–sensor kinase interactions. Nat. Chem. Biol. 19, 451–459 (2023).
Landry, B. P., Palanki, R., Dyulgyarov, N., Hartsough, L. A. & Tabor, J. J. Phosphatase activity tunes two-component system sensor detection threshold. Nat. Commun. 9, 1433 (2018).
Xun, L. & Sandvik, E. R. Characterization of 4-hydroxyphenylacetate 3-hydroxylase (HpaB) of Escherichia coli as a reduced flavin adenine dinucleotide-utilizing monooxygenase. Appl. Environ. Microbiol. 66, 481–486 (2000).
Mutalik, V. K. et al. Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods 10, 354–360 (2013).
Jansen, Z. et al. Interrogating the function of bicistronic translational control elements to improve consistency of gene expression. ACS Synth. Biol. 12, 1608–1615 (2023).
Salis, H. M., Mirsky, E. A. & Voigt, C. A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946–950 (2009).
Engler, C., Gruetzner, R., Kandzia, R. & Marillonnet, S. Golden gate shuffling: a one-pot DNA shuffling method based on type IIs restriction enzymes. PLoS ONE 4, e5553 (2009).
Gerhardt, K. P. et al. An open-hardware platform for optogenetics and photobiology. Sci. Rep. 6, 35363 (2016).
Castillo-Hair, S. M. et al. FlowCal: a user-friendly, open source software tool for automatically converting flow cytometry data from arbitrary to calibrated units. ACS Synth. Biol. 5, 774–780 (2016).
Karthikeyan, R., Devadasu, C. & Srinivasa Babu, P. Isolation, characterization, and RP-HPLC estimation of P-Coumaric Acid from methanolic extract of Durva Grass (Cynodon dactylon Linn.) (Pers.). Int. J. Anal. Chem. 2015, 201386 (2015).
Newville, M. et al. LMFIT: Non-Linear Least-Squares Minimization and Curve-Fitting for Python (1.3.3). Zenodo. https://zenodo.org/records/15014437 (2025). 1.
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Lazar, J. T. et al. A stationary phase-specific bacterial green light sensor for enhancing metabolite production data set. Figshare https://doi.org/10.6084/m9.figshare.30203842 (2025). 1.
Lazar, J. T. et al. A stationary phase-specific bacterial green light sensor for enhancing metabolite production data set. Github, https://doi.org/10.5281/zenodo.17634803 (2025).
Acknowledgements
This work was supported by National Science Foundation awards CAREER 1553317 and MCB-2204402 to J.J.T. and Welch Foundation award 235019 to R.T. J.T.L. was supported by a US National Defense Science and Engineering Graduate Fellowship. D.J.H. is supported by NSF Graduate Research Fellowship 1842494. The content is solely the responsibility of the authors and does not necessarily represent the views of the funding agencies. This work was done in part using the resources of the Shared Equipment Authority (SEA) at Rice University. The authors thank Jose Avalos for suggesting the use of the Chi.Bio reactor, Chris Pennington for LC-MS guidance, and Biki Kundu for HPLC guidance.
Author information
Authors and Affiliations
Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA
John T. Lazar, Ross Thyer & Jeffrey J. Tabor 1.
Ph.D. Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA
Daniel J. Haller, Andrew R. Gilmour, Ross Thyer & Jeffrey J. Tabor 1.
Department of Bioengineering, Rice University, Houston, TX, USA
Abbas Ghaddar, Jae J. Kim, Kevin Yang, Sebastián M. Castillo-Hair & Jeffrey J. Tabor 1.
Department of Biosciences, Rice University, Houston, TX, USA
Jeffrey J. Tabor
Authors
- John T. Lazar
- Daniel J. Haller