A stationary phase-specific bacterial green light sensor for enhancing metabolite production

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A stationary phase-specific bacterial green light sensor for enhancing metabolite production

References

  1. Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).

    Google Scholar 

  2. Murphy, A. C. Metabolic engineering is key to a sustainable chemical industry. Nat. Prod. Rep. 28, 1406 (2011).

    Google Scholar 

  3. Woolston, B. M., Edgar, S. & Stephanopoulos, G. Metabolic engineering: past and future. Annu. Rev. Chem. Biomol. Eng. 4, 259–288 (2013).

    Google Scholar 

  4. Zhang, J. et al. A microbial supply chain for production of the anti-cancer drug vinblastine. Nature 609, 341–347 (2022).

    Google Scholar 

  5. Luo, X. et al. Complete biosynthesis of cannabinoids and their unnatural analogues in yeast. Nature 567, 123–126 (2019).

    Google Scholar 

  6. 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).

    Google Scholar 

  7. Nyström, T. Stationary-phase physiology. Annu. Rev. Microbiol 58, 161–181 (2004).

    Google Scholar 

  8. Jaishankar, J. & Srivastava, P. Molecular basis of stationary phase survival and applications. Front. Microbiol. 8, 2000 (2017).

  9. Ou, J. et al. Stationary phase protein overproduction is a fundamental capability of Escherichia coli. Biochem. Biophys. Res. Commun. 314, 174–180 (2004).

    Google Scholar 

  10. Venayak, N., Anesiadis, N., Cluett, W. R. & Mahadevan, R. Engineering metabolism through dynamic control. Curr. Opin. Biotechnol. 34, 142–152 (2015).

    Google Scholar 

  11. 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).

    Google Scholar 

  12. 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).

    Google Scholar 

  13. 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).

    Google Scholar 

  14. 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).

    Google Scholar 

  15. 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).

    Google Scholar 

  16. 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).

  17. 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).

    Google Scholar 

  18. 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).

    Google Scholar 

  19. 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).

    Google Scholar 

  20. Chen, Y. et al. Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors. Nat. Commun. 9, 64 (2018).

    Google Scholar 

  21. 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).

    Google Scholar 

  22. 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).

    Google Scholar 

  23. 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).

    Google Scholar 

  24. 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).

    Google Scholar 

  25. Wegner, S. A., Barocio-Galindo, R. M. & Avalos, J. L. The bright frontiers of microbial metabolic optogenetics. Curr. Opin. Chem. Biol. 71, 102207 (2022).

    Google Scholar 

  26. Pouzet, S. et al. The promise of optogenetics for bioproduction: dynamic control strategies and scale-up instruments. Bioengineering 7, 151 (2020).

    Google Scholar 

  27. Zhao, E. M. et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687 (2018).

    Google Scholar 

  28. Lalwani, M. A. et al. Optogenetic control of the lac operon for bacterial chemical and protein production. Nat. Chem. Biol. 17, 71–79 (2021).

    Google Scholar 

  29. Olson, E. J. & Tabor, J. J. Optogenetic characterization methods overcome key challenges in synthetic and systems biology. Nat. Chem. Biol. 10, 502–511 (2014).

    Google Scholar 

  30. 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).

    Google Scholar 

  31. Tabor, J. J., Levskaya, A. & Voigt, C. A. Multichromatic control of gene expression in Escherichia coli. J. Mol. Biol. 405, 315–324 (2011).

    Google Scholar 

  32. 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).

    Google Scholar 

  33. 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).

    Google Scholar 

  34. 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).

    Google Scholar 

  35. 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).

    Google Scholar 

  36. 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).

    Google Scholar 

  37. Wang, S. et al. Development of optogenetic dual-switch system for rewiring metabolic flux for polyhydroxybutyrate production. Biotechnol. Bioeng. 119, 2345–2355 (2022).

    Google Scholar 

  38. Senoo, S. et al. Light-inducible flux control of triosephosphate isomerase on glycolysis in Escherichia coli. Biotechnol. Bioeng. 116, 3292–3300 (2019).

    Google Scholar 

  39. 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).

    Google Scholar 

  40. 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).

    Google Scholar 

  41. Fernandez-Rodriguez, J., Moser, F., Song, M. & Voigt, C. A. Engineering RGB color vision into Escherichia coli. Nat. Chem. Biol. 13, 706–708 (2017).

    Google Scholar 

  42. 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).

    Google Scholar 

  43. Larsen, B. et al. Highlighter: An optogenetic system for high-resolution gene expression control in plants. Nat. Commun. 14, 5159 (2023).

    Google Scholar 

  44. 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).

    Google Scholar 

  45. Hartsough, L. A. et al. Optogenetic control of gut bacterial metabolism to promote longevity. eLife 9, e56849 (2020).

  46. 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).

  47. Ong, N. T. & Tabor, J. J. A miniaturized Escherichia coli green light sensor with high dynamic range. ChemBioChem 19, 1255–1258 (2018).

    Google Scholar 

  48. Shao, B. et al. Single-cell measurement of plasmid copy number and promoter activity. Nat. Commun. 12, 1475 (2021).

    Google Scholar 

  49. 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).

    Google Scholar 

  50. Chen, Y.-J. et al. Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat. Methods 10, 659–664 (2013).

    Google Scholar 

  51. Fischer, A. J. & Lagarias, J. C. Harnessing phytochrome’s glowing potential. Proc. Natl. Acad. Sci. USA 101, 17334–17339 (2004).

    Google Scholar 

  52. Fischer, A. J. et al. Multiple roles of a conserved GAF domain tyrosine residue in cyanobacterial and plant phytochromes. Biochemistry 44, 15203–15215 (2005).

    Google Scholar 

  53. Saleski, T. E. et al. Optimized gene expression from bacterial chromosome by high-throughput integration and screening. Sci. Adv. 7, eabe1767 (2021).

  54. St-Pierre, F. et al. One-step cloning and chromosomal integration of DNA. ACS Synth. Biol. 2, 537–541 (2013).

    Google Scholar 

  55. Lazar, J. T. & Tabor, J. J. Bacterial two-component systems as sensors for synthetic biology applications. Curr. Opin. Syst. Biol. 28, 100398 (2021).

    Google Scholar 

  56. 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).

    Google Scholar 

  57. 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).

    Google Scholar 

  58. Korkina, L. G. Phenylpropanoids as naturally occurring antioxidants: from plant defense to human health. Cell. Mol. Biol. 53, 15–25 (2007).

    Google Scholar 

  59. 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).

    Google Scholar 

  60. 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).

    Google Scholar 

  61. 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).

    Google Scholar 

  62. 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).

    Google Scholar 

  63. 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).

    Google Scholar 

  64. Polturak, G. & Aharoni, A. “La Vie en Rose”: biosynthesis, sources, and applications of betalain pigments. Mol. Plant 11, 7–22 (2018).

    Google Scholar 

  65. Hou, Y. et al. Metabolic engineering of Escherichia coli for de novo production of betaxanthins. J. Agric. Food Chem. 68, 8370–8380 (2020).

    Google Scholar 

  66. 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).

    Google Scholar 

  67. DeLoache, W. C. et al. An enzyme-coupled biosensor enables (S)-reticuline production in yeast from glucose. Nat. Chem. Biol. 11, 465–471 (2015).

    Google Scholar 

  68. Gandía-Herrero, F., Escribano, J. & García-Carmona, F. Structural implications on color, fluorescence, and antiradical activity in betalains. Planta 232, 449–460 (2010).

    Google Scholar 

  69. 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).

    Google Scholar 

  70. Kumar, S. & Hasty, J. Stability, robustness, and containment: preparing synthetic biology for real-world deployment. Curr. Opin. Biotechnol. 79, 102880 (2023).

    Google Scholar 

  71. 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).

    Google Scholar 

  72. Moser, F. et al. Genetic circuit performance under conditions relevant for industrial fermentation. ACS Synth. Biol. 1, 555–564 (2012).

    Google Scholar 

  73. Loewe, L., Textor, V. & Scherer, S. High deleterious genomic mutation rate in stationary phase of Escherichia coli. Science 302, 1558–1560 (2003).

    Google Scholar 

  74. Amrofell, M. B. & Moon, T. S. Characterizing a propionate sensor in E. coli Nissle 1917. ACS Synth. Biol. 12, 1868–1873 (2023).

    Google Scholar 

  75. 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).

    Google Scholar 

  76. 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).

    Google Scholar 

  77. 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).

    Google Scholar 

  78. Dahl, R. H. et al. Engineering dynamic pathway regulation using stress-response promoters. Nat. Biotechnol. 31, 1039–1046 (2013).

    Google Scholar 

  79. Chen, Z. & Elowitz, M. B. Programmable protein circuit design. Cell 184, 2284–2301 (2021).

    Google Scholar 

  80. Chen, X. et al. An extraordinary stringent and sensitive light-switchable gene expression system for bacterial cells. Cell Res. 26, 854–857 (2016).

    Google Scholar 

  81. Multamäki, E. et al. Optogenetic control of bacterial expression by red light. ACS Synth. Biol. 11, 3354–3367 (2022).

    Google Scholar 

  82. Daeffler, K. N. et al. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Mol. Syst. Biol. 13, 923 (2017).

  83. Brink, K. R. et al. An E. coli display method for characterization of peptide–sensor kinase interactions. Nat. Chem. Biol. 19, 451–459 (2023).

    Google Scholar 

  84. 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).

    Google Scholar 

  85. 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).

    Google Scholar 

  86. Mutalik, V. K. et al. Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods 10, 354–360 (2013).

    Google Scholar 

  87. 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).

    Google Scholar 

  88. 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).

    Google Scholar 

  89. 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).

    Google Scholar 

  90. Gerhardt, K. P. et al. An open-hardware platform for optogenetics and photobiology. Sci. Rep. 6, 35363 (2016).

    Google Scholar 

  91. 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).

    Google Scholar 

  92. 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).

    Google Scholar 

  93. 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).

  94. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Google Scholar 

  95. 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).

  96. 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).

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