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Optogenetic regulation of engineered cellular metabolism for microbial chemical production

Abstract

The optimization of engineered metabolic pathways requires careful control over the levels and timing of metabolic enzyme expression1,2,3,4. Optogenetic tools are ideal for achieving such precise control, as light can be applied and removed instantly without complex media changes. Here we show that light-controlled transcription can be used to enhance the biosynthesis of valuable products in engineered Saccharomyces cerevisiae. We introduce new optogenetic circuits to shift cells from a light-induced growth phase to a darkness-induced production phase, which allows us to control fermentation with only light. Furthermore, optogenetic control of engineered pathways enables a new mode of bioreactor operation using periodic light pulses to tune enzyme expression during the production phase of fermentation to increase yields. Using these advances, we control the mitochondrial isobutanol pathway to produce up to 8.49 ± 0.31 g l−1 of isobutanol and 2.38 ± 0.06 g l−1 of 2-methyl-1-butanol micro-aerobically from glucose. These results make a compelling case for the application of optogenetics to metabolic engineering for the production of valuable products.

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Figure 1: Characterization of optogenetic circuits.
Figure 2: A light-dependent metabolic valve for ethanol production.
Figure 3: Light-controlled isobutanol production.

References

  1. 1

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

    Article  CAS  PubMed  Google Scholar 

  2. 2

    Zhang, Y. et al. Using unnatural protein fusions to engineer resveratrol biosynthesis in yeast and Mammalian cells. J. Am. Chem. Soc. 128, 13030–13031 (2006)

    Article  CAS  PubMed  Google Scholar 

  3. 3

    Keasling, J. D. Manufacturing molecules through metabolic engineering. Science 330, 1355–1358 (2010)

    Article  ADS  CAS  PubMed  Google Scholar 

  4. 4

    Ajikumar, P. K. et al. Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science 330, 70–74 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Tan, S. Z., Manchester, S. & Prather, K. L. J. Controlling central carbon metabolism for improved pathway yields in Saccharomyces cerevisiae. ACS Synth. Biol. 5, 116–124 (2015)

    Article  CAS  PubMed  Google Scholar 

  6. 6

    Ro, D. K. et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440, 940–943 (2006)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Gu, P., Su, T., Wang, Q., Liang, Q. & Qi, Q. Tunable switch mediated shikimate biosynthesis in an engineered non-auxotrophic Escherichia coli. Sci. Rep. 6, 29745 (2016)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Brockman, I. M. & Prather, K. L. J. Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. Metab. Eng. 28, 104–113 (2015)

    Article  CAS  PubMed  Google Scholar 

  9. 9

    Toettcher, J. E., Voigt, C. A., Weiner, O. D. & Lim, W. A. The promise of optogenetics in cell biology: interrogating molecular circuits in space and time. Nat. Methods 8, 35–38 (2011)

    Article  CAS  PubMed  Google Scholar 

  10. 10

    Milias-Argeitis, A. et al. In silico feedback for in vivo regulation of a gene expression circuit. Nat. Biotechnol. 29, 1114–1116 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Kennedy, M. J. et al. Rapid blue-light-mediated induction of protein interactions in living cells. Nat. Methods 7, 973–975 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Motta-Mena, L. B. et al. An optogenetic gene expression system with rapid activation and deactivation kinetics. Nat. Chem. Biol. 10, 196–202 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Shimizu-Sato, S., Huq, E., Tepperman, J. M. & Quail, P. H. A light-switchable gene promoter system. Nat. Biotechnol. 20, 1041–1044 (2002)

    Article  CAS  PubMed  Google Scholar 

  14. 14

    Taslimi, A. et al. Optimized second-generation CRY2-CIB dimerizers and photoactivatable Cre recombinase. Nat. Chem. Biol. 12, 425–430 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Salinas, F., Rojas, V., Delgado, V., Agosin, E. & Larrondo, L. F. Optogenetic switches for light-controlled gene expression in yeast. Appl. Microbiol. Biotechnol. 101, 2629–2640 (2017)

    Article  CAS  PubMed  Google Scholar 

  16. 16

    Nash, A. I. et al. Structural basis of photosensitivity in a bacterial light-oxygen-voltage/helix-turn-helix (LOV-HTH) DNA-binding protein. Proc. Natl Acad. Sci. USA 108, 9449–9454 (2012)

    Article  ADS  Google Scholar 

  17. 17

    Rivera-Cancel, G., Motta-Mena, L. B. & Gardner, K. H. Identification of natural and artificial DNA substrates for light-activated LOV-HTH transcription factor EL222. Biochemistry 51, 10024–10034 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Zoltowski, B. D., Motta-Mena, L. B. & Gardner, K. H. Blue light-induced dimerization of a bacterial LOV-HTH DNA-binding protein. Biochemistry 52, 6653–6661 (2013)

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Reade, A. et al. TAEL: a zebrafish-optimized optogenetic gene expression system with fine spatial and temporal control. Development 144, 345–355 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Jayaraman, P. et al. Blue light-mediated transcriptional activation and repression of gene expression in bacteria. Nucleic Acids Res. 44, 6994–7005 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Da Silva, N. A. & Srikrishnan, S. Introduction and expression of genes for metabolic engineering applications in Saccharomyces cerevisiae. FEMS Yeast Res. 12, 197–214 (2012)

    Article  CAS  PubMed  Google Scholar 

  22. 22

    Usherenko, S. et al. Photo-sensitive degron variants for tuning protein stability by light. BMC Syst. Biol. 8, 128 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    van Maris, A. J. A. et al. Directed evolution of pyruvate decarboxylase-negative Saccharomyces cerevisiae, yielding a C2-independent, glucose-tolerant, and pyruvate-hyperproducing yeast. Appl. Environ. Microbiol. 70, 159–166 (2004)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Klein, J. L. C., Olsson, L. & Nielsen, J. Glucose control in Saccharomyces cerevisiae: the role of MIG1 in metabolic functions. Microbiology 144, 13–24 (1998)

    Article  CAS  PubMed  Google Scholar 

  25. 25

    Avalos, J. L., Fink, G. R. & Stephanopoulos, G. Compartmentalization of metabolic pathways in yeast mitochondria improves the production of branched-chain alcohols. Nat. Biotechnol. 31, 335–341 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Hammer, S. K. & Avalos, J. L. Uncovering the role of branched-chain amino acid transaminases in Saccharomyces cerevisiae isobutanol biosynthesis. Metab. Eng. 44, 302–312 (2017)

    Article  CAS  PubMed  Google Scholar 

  27. 27

    Park, S. H., Kim, S. & Hahn, J. S. Improvement of isobutanol production in Saccharomyces cerevisiae by increasing mitochondrial import of pyruvate through mitochondrial pyruvate carrier. Appl. Microbiol. Biotechnol. 100, 7591–7598 (2016)

    Article  CAS  PubMed  Google Scholar 

  28. 28

    Deisseroth, K. Optogenetics. Nat. Methods 8, 26–29 (2011)

    Article  CAS  PubMed  Google Scholar 

  29. 29

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    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)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Milne, N., Wahl, S. A., van Maris, A. J. A., Pronk, J. T. & Daran, J. M. Excessive by-product formation: a key contributor to low isobutanol yields of engineered Saccharomyces cerevisiae strains. Metab. Eng. Commun. 3, 39–51 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Liu, X. et al. Structure-guided engineering of Lactococcus lactis alcohol dehydrogenase LlAdhA for improved conversion of isobutyraldehyde to isobutanol. J. Biotechnol. 164, 188–195

  33. 33

    Lee, J. J., Crook, N., Sun, J. & Alper, H. S. Improvement of lactic acid production in Saccharomyces cerevisiae by a deletion of ssb1. J. Ind. Microbiol. Biotechnol. 43, 87–96 (2016)

    Article  CAS  PubMed  Google Scholar 

  34. 34

    Lee, J. Y., Kang, C. D., Lee, S. H., Park, Y. K. & Cho, K. M. Engineering cellular redox balance in Saccharomyces cerevisiae for improved production of L-lactic acid. Biotechnol. Bioeng. 112, 751–758 (2015)

    Article  CAS  PubMed  Google Scholar 

  35. 35

    Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009)

    Article  CAS  Google Scholar 

  36. 36

    Youk, H. & Lim, W. A. Secreting and sensing the same molecule allows cells to achieve versatile social behaviors. Science 343, 1242782 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Chee, M. K. & Haase, S. B. New and redesigned pRS plasmid shuttle vectors for genetic manipulation of Saccharomyces cerevisiae. G3 (Bethesda) 2, 515–526 (2012)

    Article  CAS  PubMed Central  Google Scholar 

  38. 38

    Yuan, J. & Ching, C. B. Combinatorial assembly of large biochemical pathways into yeast chromosomes for improved production of value-added compounds. ACS Synth. Biol. 4, 23–31 (2015)

    Article  CAS  PubMed  Google Scholar 

  39. 39

    Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Güldener, U., Heck, S., Fielder, T., Beinhauer, J. & Hegemann, J. H. A new efficient gene disruption cassette for repeated use in budding yeast. Nucleic Acids Res. 24, 2519–2524 (1996)

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41

    Gueldener, U., Heinisch, J., Koehler, G. J., Voss, D. & Hegemann, J. H. A second set of loxP marker cassettes for Cre-mediated multiple gene knockouts in budding yeast. Nucleic Acids Res. 30, e23 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Jones, E. W. & Fink, G. R. in The Molecular Biology of the Yeast Saccharomyces: Metabolism and Gene Expression (eds Strathern, J. N. et al.) 181–299 (Cold Spring Harbor, 1982)

  43. 43

    Kern, S. E., Price-Whelan, A. & Newman, D. K. Extraction and measurement of NAD(P)+ and NAD(P)H. Methods Mol. Biol. 1149, 311–323 (2014)

    Article  CAS  PubMed  Google Scholar 

  44. 44

    Ziv, N., Brandt, N. J. & Gresham, D. The use of chemostats in microbial systems biology. J. Vis. Exp. 80, e50168 (2013)

    Google Scholar 

  45. 45

    Collart, M. A. & Oliviero, S. Preparation of yeast RNA. Curr. Protoc. Mol. Biol. Chapter 13, 12 (2001)

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank K. Gardner and L. Motta-Mena for providing the plasmids and maps for the EL222 system (pVP16–EL222 and pC120-Fluc)12, D. Pincus for plasmid pNH603, J. J. Lee for plasmid pET28a Ldh, C. Taxis for plasmid pDS143, C. Nelson for sharing her qPCR equipment, S. Han for assistance in qPCR experiments and figure presentation, J. Rabinowitz and J. Storey for sharing their 500-ml Sixfors fermentation system for fed-batch fermentation experiments, S. Silverman for technical assistance on this equipment and C. DeCoste and the Princeton Molecular Biology Flow Cytometry Resource Center for assistance in flow cytometry experiments. This work was supported by the Alfred P. Sloan Foundation (to J.L.A.), The Pew Charitable Trusts (to J.L.A.), National Institutes of Health grant DP2EB024247 (to J.E.T.) and an Eric and Wendy Schmidt Transformative Technology Fund grant (to J.L.A. and J.E.T.).

Author information

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Authors

Contributions

E.M.Z., J.E.T. and J.L.A. conceived this project and designed the experiments. E.M.Z., Y.Z. and J.L.A. constructed the strains and plasmids. E.M.Z. and H.P. performed the experiments that are shown in Fig. 1; E.M.Z. and J.M. conducted experiments illustrated in Fig. 2; E.M.Z. performed experiments that are shown in Fig. 3; E.M.Z. performed experiments illustrated in Extended Data Figs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Y.Z. performed experiments illustrated in Extended Data Figs 1, 8. M.A.L. performed experiments illustrated in Extended Data Fig. 10. E.M.Z., J.E.T. and J.L.A. analysed the data and wrote the paper.

Corresponding authors

Correspondence to Jared E. Toettcher or José L. Avalos.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks L. Larrondo and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Maps of key vectors used in this study.

a, pYZ12-B vector used to integrate genes or circuits into the HIS3 locus. Constructs are usually transferred from pJLA vectors using XmaI and AscI sites. Final constructs are linearized with PmeI before yeast transformation. b, pYZ23 vector used to integrate genes or circuits into δ-5 sites of yeast. Constructs are usually transferred from pJLA vectors using MreI and AscI sites. Final constructs are linearized with PmeI before yeast transformation. c, The general vector map shows the relative orientation of the five positions listed in Supplementary Table 1, in which different genes (including promoters and terminators) were assembled, using a previously described multiple gene insertion strategy25. All vectors have an ampicillin-resistance marker (AMPR) for cloning in E. coli and a selection marker for S. cerevisiae (Marker). Vector types include CEN/ARS, 2μ, or integrative36,37,38,39.

Extended Data Figure 2 Flow cytometry of strain with GFP controlled by OptoEXP.

Representative flow cytometry graphs from three biological replicates under the same conditions. Experiments were performed in 24-well plates, in either glucose or glycerol and ethanol. Every graph is generated from 20,000 cells. a, Control strain (YEZ186) with GFP under PTEF1 exposed for 3 h to constant blue light (magenta) or kept for the same amount of time in the dark (cyan); these samples are almost completely superimposed on the right-hand side of graph. Control strain (YEZ140) with no GFP exposed for 3 h of constant blue light (red) or kept for the same amount of time in the dark (grey); these samples are almost completely superimposed on the left-hand side of graph. Under these conditions, there is no detectable photobleaching. b, Light-induced GFP expression in YEZ139, a strain with GFP controlled by OptoEXP. GFP expression in YEZ139 in SC-His medium supplemented with 2% glucose after 3 h of exposure to blue light (green) is homogeneous across the cell population, and 37-fold higher than in YEZ139 cells kept in the dark for the same amount of time (orange). The maximum level of GFP expression obtained by OptoEXP in YEZ139 grown in full light for 3 h (green) is 22.1% of what is achieved in YEZ186, which contains PTEF1–GFP, grown under the same conditions (magenta). Fluorescence from a control wild-type strain without GFP, YEZ140, grown for 3 h in the light, is shown for comparison (red). c, Light-induced GFP expression by OptoEXP in YEZ243 in SC-His medium supplemented with 3% glycerol and 2% ethanol. Starting from cultures grown in the dark, samples were taken (in the exponential growth phase, at an OD600 of approximately 3) and incubated in 24-well plates under the following light conditions: 2 h in the dark (orange); 1.5 h in the dark followed by 30 min in light (green); 1 h in the dark, followed by 1 h in light (brown); or 2 h in light (dark blue). YEZ140 (red) and YEZ186 (magenta) were used as controls with no GFP expression and GFP expression from a strong, constitutive promoter (PTEF1), respectively. d, Example of the gating used to make the flow cytometry plots in ac. All experiments were repeated at least three times.

Extended Data Figure 3 OptoEXP performance under LED panels of different intensities.

Light-induced expression of GFP in YEZ243 (OptoEXP driving GFP) compared to constitutive GFP expression in YEZ186 (PTEF1–GFP), using light panels of different intensities. Pulsed light was applied at duty cycles of 10 s on and 70 s off. Data are shown as mean values; dots represent individual data points; error bars represent the s.d. from four biologically independent 1-ml culture sample replicates. All experiments were repeated at least three times.

Source data

Extended Data Figure 4 Comparison of OptoINVRT light-repressible transcription circuits.

a, OptoINVRT circuit design, based on the expression of Gal80 from OptoEXP and of Gal4 from constitutive promoters of different strength, with or without a PSD domain. b, Genes controlled by OptoINVRT circuits are repressed in the light and activated in the dark by the repression activity of Gal80 on Gal4 transcription factor. The PSD fused to Gal4 in OptoINVRT3 stimulates protein degradation in the light. c, Screens for lactic acid production in 2% glucose of several colonies of strains YEZ144 (OptoINVRT1), YEZ145 (OptoINVRT2) and YEZ146 (OptoINVRT3), using growth parameters: ρ = 5 and θ = 6 h, where ρ is the cell density at which cells are moved from light to dark and θ is the time cells are incubated in the dark before starting the fermentation (n = 7 biologically independent colonies). d, Screens for isobutanol production in 2% glucose of several colonies of YEZ159 (OptoINVRT1), YEZ156 (OptoINVRT2) and HPY6 (OptoINVRT3), using growth parameters: ρ = 8 and θ = 3 h (n = 12 biologically independent colonies). e, Screens for isobutanol production in 2% glucose of several colonies of YEZ159 (OptoINVRT1), YEZ156 (OptoINVRT2) and HPY6 (OptoINVRT3), using growth parameters: ρ = 5 and θ = 3 h (n = 12 biologically independent colonies). The screens shown in c–e were performed once in our laboratory.

Extended Data Figure 5 Light-controlled lactic acid production.

a, Lactic acid is produced by the reduction of pyruvate by Ldh. PDC1 is controlled by OptoEXP and Ldh by OptoINVRT circuits. b, With optogenetic controls, light can be used to separate fermentation into two phases: a growth phase when cultures are exposed to light, during which PDC1 is expressed and Ldh is repressed, and a lactic acid production phase when cells are in the dark, during which PDC1 is not induced, and Ldh is expressed. c, Experimental design for the screening of strains and optimization of conditions. ρ and θ were varied in these experiments. d, Three OptoINVRT circuits were tested for lactic acid production: OptoINVRT1 (YEZ144); OptoINVRT2 (YEZ145) and OptoINVRT3 (YEZ146). Top, dependence of lactic acid titres on ρ. Bottom, dependence of the ratio of lactic acid to ethanol on ρ. Fermentations were done in 26.5 g l−1 glucose and run for 2 days. All samples had θ = 6 h. Data are shown as mean values; dots represent individual data points; error bars represent the s.d. of three biologically independent 1-ml culture sample replicates exposed to the same light conditions. *P < 0.05, **P < 0.01, ***P < 0.001. Statistics are derived using a one-sided t-test. All experiments were repeated at least three times.

Source data

Extended Data Figure 6 qPCR experiments.

a, Number of copies of PC120 driving PDC1 in key strains, determined with qPCR performed on genomic DNA samples (see Methods). All strains have one copy of PDC1 integrated in the HIS3 locus, and the rest are integrated in random δ-integration sites (except YEZ50lost, which only has one copy in the HIS3 locus). Data are shown as mean values; dots represent individual data points; error bars represent the s.d. from three biologically independent 1-ml culture sample replicates. All experiments were repeated at least three times. b, qPCR of PDC1 and ILV2 mRNA levels during fed-batch fermentation with periodic light stimulation for isobutanol production in 0.5-l fermenters. qPCR was performed on samples from fed-batch fermentations for isobutanol production (Fig. 3e) to measure concentrations of PDC1 and ILV2 transcripts. Gene expression was normalized to ACT1 transcripts. Lines represent average values from samples taken from two biologically independent fermentations run under the same conditions. All experiments were repeated at least two times.

Source data

Extended Data Figure 7 Optimization of light-controlled isobutanol production.

a, Dependence of isobutanol titres on ρ. Cells were grown with θ = 3 h; fermentations were done in 21.5 g l−1 glucose; isobutanol titres were measured after 2 days of fermentation in the dark. YZy335 is a control strain with a constitutive isobutanol pathway plasmid and wild-type PDC1, PDC5 and PDC6 and was used in 2-day fermentations at high cell density as a control. b, Dependence of isobutanol titres on θ. Cells were grown to ρ = 8.5. Fermentations were again done in 20.8 g l−1 glucose for 2 days in the dark. All data are shown as mean values; dots represent individual data points; error bars represent the s.d. of three biologically independent 1-ml culture sample replicates exposed to the same light conditions. All experiments were repeated at least three times.

Source data

Extended Data Figure 8 Optimization of high glucose fermentations.

a, Glucose remainders as a percentage of initial glucose concentration after 48 h (20 g l−1 initial glucose) or 80 h (150 g l−1 initial glucose) of fermentation of YEZ167-4 in the dark. Cell growth parameters: ρ = 8.5 and θ = 4 h (for fermentations in 20 g l−1 glucose) and ρ = 9.5 and θ = 3 h (for fermentations in 150 g l−1 glucose). Data are shown as mean values; dots represent individual data points; error bars represent the s.d. from three biologically independent 1-ml culture sample replicates. All experiments were repeated at least three times. b, NAD+/NADH ratio recovery through light pulsing. NAD+/NADH ratios were measured in samples under similar batch fermentation conditions as shown in Fig. 3c (see Methods). YEZ167-4 cultures were diluted into six 24-well plates and grown to an OD600 of 9.5 and left in the dark for 3 h before resuspending cells in 15% glucose medium. Four of the plates were pulsed every 10 h for 30 min at a duty cycle of 15 s on and 65 s off. Cells were harvested after 48-h fermentations, and at different times after the last light pulse (0, 1.5, 3.5, or 7.5 h). Control plates were kept under full light or in the dark throughout the 48-h fermentations. NAD+/NADH ratios in pelleted cells were measured following a previously described method43. Data are shown as mean values; dots represent individual data points; error bars represent the s.d. of four biologically independent 1-ml culture sample replicates. All experiments were repeated at least three times.

Source data

Extended Data Figure 9 Diagrams of light-stimulated laboratory-scale fermenters used to test YEZ167-4.

a, b, The 2-l bioreactor was set up so that three light panels could be placed around the fermenter. A magnetic stir plate and stir bar were used to mix the culture, and fermentations were performed in a 30 °C warm-room. c, d, The 500-ml fed-batch bioreactor was set up so that two light panels could be placed around the fermenter. The culture was mixed with a motorized propeller and a heat plate with temperature control probe was used to maintain the temperature at 30 °C.

Extended Data Figure 10 Light-dependent GFP expression in laboratory-scale fermenter at relatively high cell densities.

a, Schematic of 5-l fermenter setup with the dimensions of the area exposed to light. Red is the heating blanket around the reactor. Brown depicts the cell culture (2.5 l). Blue depicts the area being illuminated by blue LEDs. b, Picture of the functioning 5-l, light-stimulated fermenter. c, Representative flow cytometry results from two fermentation replicates using YEZ243, which has light-inducible GFP expression. Cells were grown in fed-batch mode using a glycerol feed to achieve the highest cell densities possible in this setup. Yeast cells were exposed to light when they reached an OD600 of 15 and left under continuous illumination for the rest of the experiment. Samples from the fermenter were fixed at the time of harvesting to prevent time-dependent variations. Grey was a sample of YEZ140 without GFP, which was used as a control. Light blue is pre-induction at an OD600 of 15; red is after 1 h of induction at an OD600 of 16; orange is after 6 h of induction at an OD600 of 19; green is after 24 h of induction at an OD600 of 41; purple is after 32 h of induction at an OD600 of 46 and dark blue is after 40 h of induction at an OD600 of 50. Every curve is generated from 20,000 cell counts. Data from the other fermenter run, which are very similar, are available upon request. All experiments were repeated at least three times.

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Zhao, E., Zhang, Y., Mehl, J. et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687 (2018). https://doi.org/10.1038/nature26141

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