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Rewriting yeast central carbon metabolism for industrial isoprenoid production

Nature volume 537, pages 694–697 (2016)Cite this article

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Abstract

A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments1. Adapting these strains for biosynthesis of other isoprenoids such as β-farnesene (C15H24), a plant sesquiterpene with versatile industrial applications2,3,4,5, is straightforward. However, S. cerevisiae uses a chemically inefficient pathway for isoprenoid biosynthesis, resulting in yield and productivity limitations incompatible with commodity-scale production. Here we use four non-native metabolic reactions to rewire central carbon metabolism in S. cerevisiae, enabling biosynthesis of cytosolic acetyl coenzyme A (acetyl-CoA, the two-carbon isoprenoid precursor) with a reduced ATP requirement, reduced loss of carbon to CO2-emitting reactions, and improved pathway redox balance. We show that strains with rewired central metabolism can devote an identical quantity of sugar to farnesene production as control strains, yet produce 25% more farnesene with that sugar while requiring 75% less oxygen. These changes lower feedstock costs and dramatically increase productivity in industrial fermentations which are by necessity oxygen-constrained6. Despite altering key regulatory nodes, engineered strains grow robustly under taxing industrial conditions, maintaining stable yield for two weeks in broth that reaches >15% farnesene by volume. This illustrates that rewiring yeast central metabolism is a viable strategy for cost-effective, large-scale production of acetyl-CoA-derived molecules.

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Figure 1: Synthetic pathway for acetyl-CoA-derived bioproducts in S. cerevisiae.
Figure 2: Identification of functional enzymes.
Figure 3: Identification of interfering, previously uncharacterized native reaction.
Figure 4: Fermentation performance.

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Acknowledgements

We thank Amyris R&D, including Automated Strain Engineering (S. Chandran, D. Hollis, J. Lee, M. Patana, E. Shapland, N. Vongtharangsy, S. Annamalai, J. Dean, Y. Dharmadi, N. Klinkner, K. Patel and T. Slaby), High Throughput Screening and Automation (B. Kaufmann-Malaga, B. Carroll, L. Chao, J. Cragg, H. DePaul, C. Elliott, N. Gagelonia, R. Gonzalez, G. Hailu, J. Lau, D. Misumi, A. Navidi, J. Pantaleon, R. Perkins, S. Reisinger, M. Yu, W. Chit-Maung, L. Frenz, R. Hansen, D. Herrera, Z. Serber and C. Swimmer), Analytical Chemistry and Operations (S. Gaucher, B. Van Deren, M. Leavell, D. Diola, S. Fickes, R. Herman, F. Kha, A. Le, P. Norton, V. Rocha, R. Dutcher, B. Sauls, T. Treynor and M. Youngblood), Biology (J. Ubersax, S. Borisova, Q. Mitrovich, C. Paddon, M. Snydsman, J. Agresti, K. Dietzel, J. Kealey, N. Klinkner, J. LaBarge, R. Lowe, K. Takeoka, T. Treynor, T. Slaby, I. Zamora and B. Zhang), Scientific Computing and Software Engineering (C. Allen, M. Bissell, B. Hawthorne, A. Singh, S. Zhang, S. Lobosco, S. Pegg, J. Schonbrun, H. Tu, M. Ward and E. Wong), Fermentation Process Development (D. Pitera, E. Porcel, E. Bellissimi, J. Galazzo, T. Horning, J. Lievense, D. Melis and H. Tsuruta), Fermentation Operations (T. Leaf, J. Villar, M. Braunstein, M. Corpus, D. Do, S. Fernandez, C. Fuller, B. Friedrikson, S. Gottlieb, S. Leng, S. Louie, S. Moser, A. Porter, J. Paulas, T. Duong, J. Laoyan, P. Lovasik, R. Phan, H. Ta, N. Vongtharangsy, A. Wong and K. Yu), Bioanalytics (D. Abbott, M. Ayson, J. Denery, B. Lieu, C. Sandoval, T. Geistlinger, L. Kizer and N. Moss), and Laboratory services (R. Campbell, J. Allison, J. Davis, B. DiCarlo, A. Hammang, C. Love, B. Gellman and A. Salmon). We also thank K. Curran, A. Horwitz, S. de Kok, J. Lerman, Q. Mitrovich, C. Paddon, W. Szeto, J. Walter and J. Ubersax for feedback, and J. Cherry, W. Szeto, N. Renninger, J. Newman and J. Ubersax for support.

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Author notes

  1. Adam L. Meadows, Kristy M. Hawkins, Yoseph Tsegaye, Eugene Antipov and Annie E. Tsong: These authors contributed equally to this work.

Authors and Affiliations

  1. Amyris, 5885 Hollis Street, Suite 100, Emeryville, 94608, California, USA

    Adam L. Meadows, Kristy M. Hawkins, Yoseph Tsegaye, Eugene Antipov, Youngnyun Kim, Lauren Raetz, Robert H. Dahl, Anna Tai, Tina Mahatdejkul-Meadows, Lan Xu, Lishan Zhao, Madhukar S. Dasika, Abhishek Murarka, Jacob Lenihan, Diana Eng, Joshua S. Leng, Chi-Li Liu, Jared W. Wenger, Hanxiao Jiang, Lily Chao, Patrick Westfall, Jefferson Lai, Savita Ganesan, Peter Jackson, Robert Mans, Darren Platt, Christopher D. Reeves, Poonam R. Saija, Gale Wichmann, Victor F. Holmes, Kirsten Benjamin, Paul W. Hill, Timothy S. Gardner & Annie E. Tsong

Authors
  1. Adam L. Meadows
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Contributions

A.L.M., A.M., M.S.D., and T.S.G. performed metabolic modelling. A.E.T., K.M.H., Y.T., E.A., Y.K., L.R., A.T., T.M.M., L.X., L.Z., L.C., J.L., S.G., P.J., R.M., H.J., P.W., D.P., G.W., and V.F.H. identified and characterized enzymes. R.H.D., A.M., J.L., D.E., C.-L.L., and J.S.L. developed fermentation processes with direction from P.W.H. L.R., A.E.T., T.M.M., M.S.D., A.T., K.M.H. and A.L.M. identified the phosphatase. K.M.H., Y.T., E.A., Y.K., A.T., T.M.M., L.R., H.J., J.W.W., R.H.D., M.S.D., C.D.R., P.R.S., A.E.T. and K.B. engineered production strains. A.E.T. and T.S.G. directed the work. A.E.T. and A.L.M. wrote the manuscript.

Corresponding author

Correspondence to Annie E. Tsong.

Ethics declarations

Competing interests

Some authors are stockholders of Amyris.

Additional information

Contractual obligations from commercial partnerships prohibit us from distributing (by ourselves or through a third party) strains described in our manuscript. However, we provide extensive genotypic descriptions of our strains, fully annotated metabolic models, and detailed methods that enable others to build upon our work.

Reviewer Information Nature thanks J. Dueber, J. D. Rabinowitz and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 S. pomeroyi HMG CoA reductase is NADH-specific.

HMG CoA reductase variants from S. cerevisiae and S. pomeroyi were expressed in S. cerevisiae and tested for activity in cell-free extract. S. cerevisiae tHMGr has detectable activity only in the presence of NADPH, whereas S. pomeroyi HMGr has detectable activity only in the presence of NADH. Error bars represent standard deviation of n = 3.

Extended Data Figure 2 Exclusive use of heterologous pathways for cytosolic acetyl-CoA.

a, Deletion of ALD4 and ALD6 in the AMR-4 background results in a 22-fold decrease in total NAD+- and NADP+-dependent aldehyde dehydrogenase activity, relative to an ALD4+ALD6+ predecessor, with remaining activity probably from ALD5 (ref. 55). Cell-free extract samples were normalized by protein concentration. Error bars represent standard deviation of n = 3. b–d, An ald4Δ ald5Δ ald6Δ strain expressing ADA, NADH-HMGr, xPK, and PTA, has the same yield (b), productivity (c), and titre (d) as its ALD5+ parental strain, indicating that the native PDH-bypass carries minimal flux for farnesene production in the engineered strains. Error bars represent range, n = 2.

Extended Data Figure 3 Strains achieve nearly identical yield and productivity under laboratory and manufacturing conditions.

a, b, Yields (a) and productivities (b) are compared for four strains at the 2-litre bench scale (x axis) and 200,000-litre manufacturing scale (y axis). The black dotted line shows the relationship y = x. Open blue circles represent strains that use the native S. cerevisiae central metabolism, whereas open green circles represent strains that use the synthetic pathway described in this work. Figures show cumulative yield and productivity averages over a 10-day fermentation, and are normalized to the highest yield and productivity at either scale. Error bars represent standard deviations for over 10 fermentation runs per strain, at each scale: for a, b, the n values on the x axis for the bench scale from left (first blue circle) to right (last green circle) were n = 34, n = 92, n = 22 and n = 13, respectively, and the n values on the y axis for the fermentation scale were from bottom (first blue circle) to top (last green circle) were n = 63, n = 33, n = 29, n = 14, respectively.

Extended Data Figure 4 Sensitivity of maximum F/O2 ratio to biomass stoichiometry.

The maximal F/O2 ratio at a given yield (using only native S. cerevisiae reactions) changes with the O2/sugar (O/S) molar ratio used for S. cerevisiae biomass generation. The same fermentation data are shown in Fig. 4e. The orange line (O/S = 2.29) uses empirical biomass stoichiometry of CBS 8066 (ref. 34), the blue line (O/S = 2.50) is the model output for iSc-AMRS-1 (native network) maximizing biomass flux on glucose in fully aerobic conditions, and the purple line (O/S = 2.55) uses the empirical biomass stoichiometry for CEN.PK113-7D56. Strains carrying the synthetic network (green circles) show farnesene stoichiometry exceeding the native network (blue circles) regardless of which O/S is used.

Extended Data Figure 5 Yield stability and titre in an industrial process.

a, Yield is almost constant over a 13-day-long fermentation. The strain shown is related to AMR-5, and uses the synthetic metabolic network to biosynthesize farnesene. b, Farnesene titres exceed 130 g farnesene per kg broth by the second week of the fermentation. Error bars represent the range, n = 2.

Extended Data Table 1 List of heterologous metabolic reactions evaluated in the context of wild-type reaction network, iSc-AMRS-1
Full size table
Extended Data Table 2 Example sets of enzymes or pathways that increase theoretical maximum mass yield of farnesene from glucose to 29.8%
Full size table
Extended Data Table 3 Strains with alternative acetyl-CoA pathways are less viable than unengineered strains after carbon exhaustion, and more sensitive to heatshock
Full size table
Extended Data Table 4 Effect of iSc-AMRS-2 reaction network on maximum theoretical yield and O2 requirement for six molecules that could be derived from acetyl-CoA
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data comprising: Section 1 - Oxygen transfer rate in a fermentation vessel >100m3; Section 2 - Productivity of farnesene production using the native S. cerevisiae central metabolism; Section 3 - Description of AMR-5; Section 4 - Calculating upper and lower limits of farnesene produced / total O2 consumed (F/O2) at all yields; Section 5 - Strain genotypes. (PDF 509 kb)

Supplementary Table 1

This table shows the optimal flux distributions underlying the farnesene biosynthesis stoichiometries reported throughout the text and in Table 1, and also provides flux distribution maps. (XLSX 352 kb)

Supplementary Table 2

This table shows the calculations underlying the upper bound curves shown in Figure 4d and 4e, and also shows lower bounds. (XLSX 29 kb)

Supplementary Table 3

This table contains the definition of iSc-AMRS models, a description of the genome scale model used for stoichiometric calculations and determination of flux distributions. (XLSX 149 kb)

Supplementary Table 4

This table shows the lower bounds. (XLSX 8 kb)

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Meadows, A., Hawkins, K., Tsegaye, Y. et al. Rewriting yeast central carbon metabolism for industrial isoprenoid production. Nature 537, 694–697 (2016). https://doi.org/10.1038/nature19769

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