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In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design

Nature Chemical Biology volume 16, pages 912–919 (2020)Cite this article

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Abstract

The design and optimization of biosynthetic pathways for industrially relevant, non-model organisms is challenging due to transformation idiosyncrasies, reduced numbers of validated genetic parts and a lack of high-throughput workflows. Here we describe a platform for in vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE) to accelerate this process. In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE by screening 54 different cell-free pathways for 3-hydroxybutyrate production and optimizing a six-step butanol pathway across 205 permutations using data-driven design. Observing a strong correlation (r = 0.79) between cell-free and cellular performance, we then scaled up our highest-performing pathway, which improved in vivo 3-HB production in Clostridium by 20-fold to 14.63 ± 0.48 g l−1. We expect iPROBE to accelerate design–build–test cycles for industrial biotechnology.

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Fig. 1: A two-pot cell-free framework for iPROBE.
Fig. 2: Individual pathway enzymes can be tuned in a pathway context and ranked using TREE scores with iPROBE.
Fig. 3: Enzymatic pathways can be screened with iPROBE to inform Clostridium expression for optimizing 3-HB production.
Fig. 4: Cell-free pathway testing combined with data-driven design of experiments quickly screens 205 unique pathway combinations and selects pathways for cellular butanol production.
Fig. 5: Clostridium fermentations show improved production of 3-HB and identification of a new route to 1,3-BDO.

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Article 24 June 2021

Elizabeth L. Bell, William Finnigan, … Sabine L. Flitsch

Data availability

All cell-free data generated and shown in this manuscript are provided in Supplementary Table 2 and Supplementary Datasets 1 and 2 (.xlsx). Any additional data or unique materials presented in the manuscript may be available from the authors upon reasonable request and through a materials transfer agreement.

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Acknowledgements

We thank A. M. Mueller, R. T. Tappel, W. Allen, L. Tran and S. D. Brown (LanzaTech) for conversations regarding this work. In addition, we thank C. Reynolds (Lockheed Martin) for conversations on the design of experiments using neural networks. This work is supported by the US Department of Energy, Office of Biological and Environmental Research in the Department of Environment Office of Science under award number DE-SC0018249. M.C.J. gratefully acknowledges the David and Lucile Packard Foundation and the Camille Dreyfus Teacher–Scholar Program. We also thank the following investors in LanzaTech’s technology: BASF, CICC Growth Capital Fund I, CITIC Capital, Indian Oil Company, K1W1, Khosla Ventures, the Malaysian Life Sciences, Capital Fund, L. P., Mitsui, the New Zealand Superannuation Fund, Petronas Technology Ventures, Primetals, Qiming Venture Partners, Softbank China and Suncor.

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    Ashty S. Karim, Quentin M. Dudley, Samantha A. Crowe, Jacob T. Heggestad, William S. Grubbe, Blake J. Rasor & Michael C. Jewett

  2. Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA

    Ashty S. Karim, Quentin M. Dudley, Samantha A. Crowe, Jacob T. Heggestad, William S. Grubbe, Blake J. Rasor & Michael C. Jewett

  3. Center for Synthetic Biology, Northwestern University, Evanston, IL, USA

    Ashty S. Karim, Quentin M. Dudley, Samantha A. Crowe, Jacob T. Heggestad, William S. Grubbe, Blake J. Rasor & Michael C. Jewett

  4. LanzaTech Inc., Skokie, IL, USA

    Alex Juminaga, Yongbo Yuan, Shivani Garg, Tanus Abdalla, FungMin (Eric) Liew, Amy Quattlebaum, Rasmus O. Jensen, Sean D. Simpson & Michael Köpke

  5. Lockheed Martin Advanced Technology Laboratories, Cherry Hill, NJ, USA

    David N. Coar, Maria Torculas, Michael Krein & Jeffrey A. Stuart

  6. Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA

    Michael C. Jewett

  7. Simpson Querrey Institute, Northwestern University, Chicago, IL, USA

    Michael C. Jewett

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Contributions

A.S.K., S.D.S., M.K. and M.C.J. designed the study. A.S.K., Q.M.D. and M.C.J. developed the cell-free framework. A.S.K., S.A.C., J.T.H., W.S.G. and B.J.R. performed all cell-free experiments. A.S.K. and Q.M.D. analyzed cell-free data. A.J. performed Clostridium strain engineering for 3-HB and 1,3-BDO. T.A. performed C. autoethanogenum gas fermentation for 3-HB and 1,3-BDO. Y.Y., F.E.L., R.O.J., S.G. and M.K. performed C. autoethanogenum strain engineering and gas fermentation for butanol. A.J., Y.Y. and M.K.. analyzed C. autoethanogenum data. A.Q. developed analytical methods for 3-HB, 1,3-BDO and butanol. D.C., M.T., M.Kr. and J.S. performed all design of experiments using neural networks. A.S.K., M.K. and M.C.J. wrote the manuscript.

Corresponding authors

Correspondence to Michael Köpke or Michael C. Jewett.

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Competing interests

A.J., T.A., S.G., A.Q., Y.Y., F.E.L., R.O.J., S.D.S. and M.K. are employees of LanzaTech, which has commercial interest in gas fermentation with C. autoethanogenum. Production of 3-HB, 1,3-BDO and 1-butanol from C1 gases has been patented (US patents 9,738,875 and 9,359,611). A.S.K. and M.C.J. are co-inventors on the US provisional patent application 62/173,818 that incorporates discoveries described in this manuscript. All other authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Figs. 1–11 and Notes 1 and 2.

Reporting Summary

Supplementary Dataset 1

The dataset contains five sheets related to the 3-HB data presented in this manuscript. Sheet 1 provides enzyme nomenclature, sheet 2 lists enzyme combinations, sheet 3 lists enzyme concentrations in final reactions, sheet 4 lists metabolite concentrations over time and sheet 5 lists all TREE scores and component parts.

Supplementary Dataset 2

The dataset contains five sheets related to the butanol data presented in this manuscript. Sheet 1 provides enzyme nomenclature, sheet 2 lists enzyme combinations, sheet 3 lists enzyme concentrations in final reactions, sheet 4 lists metabolite concentrations over time and sheet 5 lists all TREE scores and component parts.

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Karim, A.S., Dudley, Q.M., Juminaga, A. et al. In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design. Nat Chem Biol 16, 912–919 (2020). https://doi.org/10.1038/s41589-020-0559-0

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