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Towards next-generation cell factories by rational genome-scale engineering

Abstract

Metabolic engineering holds the promise to transform the chemical industry and to support the transition into a circular bioeconomy, by engineering cellular biocatalysts that efficiently convert sustainable substrates into desired products. However, despite decades of research, the potential of metabolic engineering has only been realized to a limited extent at the industrial level. To further realize its potential, it is essential to optimize the synthetic and native metabolic networks of cell factories at a system and genome-wide level. Here we discuss the tools and strategies enabling system-wide (semi-) rational engineering. Recent advances in genome-editing technologies enable directed genome-wide engineering in a growing number of relevant microorganisms. Such system-wide engineering can benefit from machine learning and other in silico design methods, and it needs to be integrated with efficient screening or selection approaches. These approaches are expected to realize the promise of next-generation cell factories for efficient, sustainable production of a wide range of products.

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Fig. 1: Target sites for gene (expression) editing and available modification strategies.
Fig. 2: Editing ranges of different multiplex engineering tools.
Fig. 3: Single-stranded DNA recombineering allows for efficient multiplex genome engineering.
Fig. 4: CRISPR-Cas based genetic tools.
Fig. 5: Scales of different screening and selection strategies.

References

  1. Bailey, J. E. Toward a science of metabolic engineering. Science 252, 1668–1675 (1991).

    Article  CAS  PubMed  Google Scholar 

  2. Casini, A., Storch, M., Baldwin, G. S. & Ellis, T. Bricks and blueprints: methods and standards for DNA assembly. Nat. Rev. Mol. Cell Biol. 16, 568–576 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Wu, W. Y., Lebbink, J. H. G., Kanaar, R., Geijsen, N. & van der Oost, J. Genome editing by natural and engineered CRISPR-associated nucleases. Nat. Chem. Biol. 14, 642–651 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Mougiakos, I., Bosma, E. F., Ganguly, J., van der Oost, J. & van Kranenburg, R. Hijacking CRISPR-Cas for high-throughput bacterial metabolic engineering: advances and prospects. Curr. Opin. Biotechnol. 50, 146–157 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Wannier, T. et al. Recombineering and MAGE. Nat. Rev. Methods Prim. 1, 7 (2021). This review provides an overview on the mechanism, applications and optimal experimental strategies of recombineering and specifically MAGE, in diverse organisms.

    Article  CAS  Google Scholar 

  7. Liao, J. C., Mi, L., Pontrelli, S. & Luo, S. Fuelling the future: microbial engineering for the production of sustainable biofuels. Nat. Rev. Microbiol. 14, 288–304 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Claassens, N. J. et al. Harnessing the power of microbial autotrophy. Nat. Rev. Microbiol. 14, 692–706 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Lee, S. Y. et al. A comprehensive metabolic map for production of bio-based chemicals. Nat. Catal. 2, 18–33 (2019). This review article gives a comprehensive overview of industrial production for many bio-based products including their metabolic routes, current performances and challenges.

    Article  CAS  Google Scholar 

  10. Choi, K. R. et al. Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering. Trends Biotechnol. 37, 817–837 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. de Jong, E., Stichnothe, H., Bell, G. & Jørgensen, H. Bio-Based Chemicals: a 2020 Update (IEA Bioenergy, 2020).

  12. Oxford Economics. The Global Chemical Industry: Catalyzing Growth and Addressing Our World’s Sustainability Challenges (Oxford Economics, 2019); https://www.oxfordeconomics.com/recent-releases/the-global-chemical-industry-catalyzing-growth-and-addressing-our-world-sustainability-challenges

  13. Blanco, J., Iglesias, J., Morales, G., Melero, J. A. & Moreno, J. Comparative life cycle assessment of glucose production from maize starch and woody biomass residues as a feedstock. Appl. Sci. 10, 2946 (2020).

    Article  CAS  Google Scholar 

  14. Naz, T. et al. Transformation of lignocellulosic biomass into sustainable biofuels: major challenges and bioprocessing technologies. Am. J. Biochem. Biotechnol. 16, 308–327 (2020).

    Article  CAS  Google Scholar 

  15. Fackler, N. et al. Stepping on the gas to a circular economy: accelerating development of carbon-negative chemical production from gas fermentation. Annu. Rev. Chem. Biomol. Eng. 12, 439–470 (2021).

    Article  PubMed  Google Scholar 

  16. Cotton, C. A. R., Claassens, N. J., Benito-Vaquerizo, S. & Bar-even, A. Renewable methanol and formate as microbial feedstocks. Curr. Opin. Biotechnol. 62, 168–180 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Wehrs, M. et al. Engineering robust production microbes for large-scale cultivation. Trends Microbiol. 27, 524–537 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Lee, J. W. et al. Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol. 8, 536–546 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Lee, S. Y. & Kim, H. U. Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 33, 1061–1072 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Sandberg, T. E., Salazar, M. J., Weng, L. L., Palsson, B. O. & Feist, A. M. The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab. Eng. 56, 1–16 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wu, Y., Jameel, A., Xing, X. H. & Zhang, C. Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol. https://doi.org/10.1016/j.tibtech.2021.04.002 (2021).

  22. Biz, A. et al. Systems biology based metabolic engineering for non-natural chemicals. Biotechnol. Adv. 37, 107379 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Alper, H. & Stephanopoulos, G. Global transcription machinery engineering: a new approach for improving cellular phenotype. Metab. Eng. 9, 258–267 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Alper, H., Moxley, J., Nevoigt, E., Fink, G. R. & Stephanopoulos, G. Engineering yeast transcription machinery for improved ethanol tolerance and production. Science 314, 1565–1568 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Liu, R. et al. Directed combinatorial mutagenesis of Escherichia coli for complex phenotype engineering. Metab. Eng. 47, 10–20 (2018). A demonstration of the use of iCREATE to target ~40,000 mutations across 30 diverse genes in parallel, to optimized C5/C6 sugar co-utilization and tolerance to furfural and acetate in E. coli, which occur in lignocellulose hydrolyates.

    Article  PubMed  Google Scholar 

  26. Liang, L. et al. Genome engineering of E. coli for improved styrene production. Metab. Eng. 57, 74–84 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Watson, E., Yilmaz, L. S. & Walhout, A. J. M. Understanding metabolic regulation at a systems level: metabolite sensing, mathematical predictions and model organisms. Annu. Rev. Genet. 49, 553–575 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Banos, D. T., Trébulle, P. & Elati, M. Integrating transcriptional activity in genome-scale models of metabolism. BMC Syst. Biol. 11, 81–90 (2017).

    Article  Google Scholar 

  29. Nielsen, J. Systems biology of metabolism. Annu. Rev. Biochem. 86, 245–275 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Stalidzans, E., Seiman, A., Peebo, K., Komasilovs, V. & Pentjuss, A. Model-based metabolism design: constraints for kinetic and stoichiometric models. Biochem. Soc. Trans. 46, 261–267 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Burgard, A. P., Pharkya, P. & Maranas, C. D. OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Von Kamp, A. & Klamt, S. Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nat. Commun. 8, 15956 (2017).

    Article  Google Scholar 

  34. Banerjee, D. et al. Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale. Nat. Commun. 11, 5385 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sánchez, B. J. et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol. 13, 935 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kim, O. D., Rocha, M. & Maia, P. A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering. Front. Microbiol. 9, 1690 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lee, Y., Lafontaine Rivera, J. G. & Liao, J. C. Ensemble modeling for robustness analysis in engineering non-native metabolic pathways. Metab. Eng. 25, 63–71 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Chen, F. Y. H., Jung, H. W., Tsuei, C. Y. & Liao, J. C. Converting Escherichia coli to a synthetic methylotroph growing solely on methanol. Cell 182, 933–946 (2020). This article reports the kinetic-modelling informed design, rational construction, ALE and realization of full growth on methanol in E. coli via the RuMP pathway.

    Article  CAS  PubMed  Google Scholar 

  39. Zhou, H., Vonk, B., Roubos, J. A., Bovenberg, R. A. L. & Voigt, C. A. Algorithmic co-optimization of genetic constructs and growth conditions: application to 6-ACA, a potential nylon-6 precursor. Nucleic Acids Res. 43, 10560–10570 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Xu, P., Rizzoni, E. A., Sul, S. Y. & Stephanopoulos, G. Improving metabolic pathway efficiency by statistical model-based multivariate regulatory metabolic engineering. ACS Synth. Biol. 6, 148–158 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Young, E. M. et al. Iterative algorithm-guided design of massive strain libraries, applied to itaconic acid production in yeast. Metab. Eng. 48, 33–43 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Jervis, A. J. et al. Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli. ACS Synth. Biol. 8, 127–136 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Zhou, Y. et al. MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae. Metab. Eng. 47, 294–302 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Zhang, J. et al. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat. Commun. 11, 4880 (2020). Good illustration of integrated system metabolic engineering approaches in yeast, based on modeling to identify five gene targets, Cas9-based editing of promoters variants for these genes, recording data with a tryptophan biosensor and using generated data for machine learning for further improve tryptophan production.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lawson, C. E. et al. Machine learning for metabolic engineering: a review. Metab. Eng. 63, 34–60 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Volk, M. J. et al. Biosystems design by machine learning. ACS Synth. Biol. 9, 1514–1533 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Machine learning applications in systems metabolic engineering. Curr. Opin. Biotechnol. 64, 1–9 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. 5, 662–672 (2022).

    Article  Google Scholar 

  49. Urtecho, G. et al. Genome-wide functional characterization of Escherichia coli promoters and regulatory elements responsible for their function. Preprint at https://www.biorxiv.org/content/10.1101/2020.01.04.894907v1 (2020).

  50. La Fleur, T., Hossain, A. & Salis, H. M. Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria. Preprint at https://www.biorxiv.org/content/10.1101/2021.09.01.458561v1 (2021).

  51. Wang, H. H. et al. Genome-scale promoter engineering by coselection MAGE. Nat. Methods 9, 591–593 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Yona, A. H., Alm, E. J. & Gore, J. Random sequences rapidly evolve into de novo promoters. Nat. Commun. 9, 1530 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Nyerges, Á. et al. Directed evolution of multiple genomic loci allows the prediction of antibiotic resistance. Proc. Natl Acad. Sci. USA 115, E5726–E5735 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Cetnar, D. P. & Salis, H. M. Systematic quantification of sequence and structural determinants controlling mRNA stability in bacterial operons. ACS Synth. Biol. 10, 318–332 (2021).

    Article  CAS  PubMed  Google Scholar 

  55. Nieuwkoop, T., Finger-Bou, M., van der Oost, J. & Claassens, N. J. The ongoing quest to crack the genetic code for protein production. Mol. Cell 80, 193–209 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Zelcbuch, L. et al. Spanning high-dimensional expression space using ribosome-binding site combinatorics. Nucleic Acids Res. 41, e98 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, H. H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009). This landmark study established the principles of MAGE in E. coli and demonstrated its strong potential for targeted engineering of 24 genes involved in lycopene biosynthesis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Reis, A. C. & Salis, H. M. An automated model test system for systematic development and improvement of gene expression models. ACS Synth. Biol. 9, 3145–3156 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Farasat, I. et al. Efficient search, mapping and optimization of multi‐protein genetic systems in diverse bacteria. Mol. Syst. Biol. 10, 731 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Jeschek, M., Gerngross, D. & Panke, S. Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort. Nat. Commun. 7, 11163 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bonde, M. T. et al. Predictable tuning of protein expression in bacteria. Nat. Methods 13, 233–236 (2016).

    Article  CAS  PubMed  Google Scholar 

  62. Höllerer, S. et al. Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nat. Commun. 11, 3551 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kozak, M. Regulation of translation via mRNA structure in prokaryotes and eukaryotes. Gene 361, 13–37 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. De Nijs, Y., De Maeseneire, S. L. & Soetaert, W. K. 5′ untranslated regions: the next regulatory sequence in yeast synthetic biology. Biol. Rev. 95, 517–529 (2020).

    Article  PubMed  Google Scholar 

  65. Nieuwkoop, T., Claassens, N. J. & van der Oost, J. Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design. Microb. Biotechnol. 12, 173–179 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Rennig, M. et al. TARSyn: tunable antibiotic resistance devices enabling bacterial synthetic evolution and protein production. ACS Synth. Biol. 7, 432–442 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. 57, 4143–4148 (2018).

    Article  CAS  Google Scholar 

  68. Copeland, N. G., Jenkins, N. A. & Court, D. L. Recombineering: a powerful new tool for mouse functional genomics. Nat. Rev. Genet. 2, 769–779 (2001).

    Article  CAS  PubMed  Google Scholar 

  69. Bassalo, M. C. et al. Rapid and efficient one-step metabolic pathway integration in E. coli. ACS Synth. Biol. 5, 561–568 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Sharan, S. K., Thomason, L. C., Kuznetsov, S. G. & Court, D. L. Recombineering: a homologous recombination-based method of genetic engineering. Nat. Protoc. 4, 206–223 (2010).

    Article  Google Scholar 

  72. Gallagher, R. R., Li, Z., Lewis, A. O. & Isaacs, F. J. Rapid editing and evolution of bacterial genomes using libraries of synthetic DNA. Nat. Protoc. 9, 2301–2316 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Nyerges, Á. et al. A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species. Proc. Natl Acad. Sci. USA 113, 2502–2507 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Bonde, M. T. et al. Direct mutagenesis of thousands of genomic targets using microarray-derived oligonucleotides. ACS Synth. Biol. 4, 17–22 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Aparicio, T., Nyerges, A., Martínez-García, E. & de Lorenzo, V. High-efficiency multi-site genomic editing of Pseudomonas putida through thermoinducible ssDNA recombineering. iScience 23, 100946 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chang, Y., Wang, Q., Su, T. & Qi, Q. The efficiency for recombineering is dependent on the source of the phage recombinase function unit. Preprint at https://www.biorxiv.org/content/10.1101/745448v1.full.pdf (2019).

  77. van Pijkeren, J. P., Neoh, K. M., Sirias, D., Findley, A. S. & Britton, R. A. Exploring optimization parameters to increase ssDNA recombineering in Lactococcus lactis and Lactobacillus reuteri. Bioengineered https://doi.org/10.4161/bioe.2104 (2012).

  78. Filsinger, G. T. et al. Characterizing the portability of phage-encoded homologous recombination proteins. Nat. Chem. Biol. 17, 394–402 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Wannier, T. M. et al. Improved bacterial recombineering by parallelized protein discovery. Proc. Natl Acad. Sci. USA 117, 13689–13698 (2020). This study provides a platform for identifying efficient single-stranded annealing proteins and allowed for more efficient recombineering in E. coli and several other bacteria.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Hueso-Gil, A., Nyerges, Á., Pál, C., Calles, B. & De Lorenzo, V. Multiple-site diversification of regulatory sequences enables interspecies operability of genetic eevices. ACS Synth. Biol. 9, 104–114 (2020).

    Article  CAS  PubMed  Google Scholar 

  81. Barbieri, E. M., Muir, P., Akhuetie-Oni, B. O., Yellman, C. M. & Isaacs, F. J. Precise editing at DNA replication forks enables multiplex genome engineering in eukaryotes. Cell 171, 1453–1467 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Dicarlo, J. E. et al. Yeast oligo-mediated genome engineering (YOGE). ACS Synth. Biol. 2, 741–749 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Liang, Z., Metzner, E. & Isaacs, F. J. Advanced eMAGE for highly efficient combinatorial editing of a stable genome. Preprint at https://www.biorxiv.org/content/10.1101/2020.08.30.256743v1 (2020).

  84. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 337, 816–821 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Doudna, J. A. & Charpentier, E. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).

    Article  PubMed  Google Scholar 

  86. Adiego-Pérez, B. et al. Multiplex genome editing of microorganisms using CRISPR-Cas. FEMS Microbiol. Lett. 366, 1–19 (2019). This review provides a comprehensive overview of CRISPR-Cas techniques and their performance for multiplexing in prokaryotic and eukaryotic microorganisms.

    Article  Google Scholar 

  87. Zhang, Z. X. et al. Recent advances in the application of multiplex genome editing in Saccharomyces cerevisiae. Appl. Microbiol. Biotechnol. 105, 3873–3882 (2021).

    Article  CAS  PubMed  Google Scholar 

  88. Kim, H., Ji, C., Je, H., Kim, J. & Kang, H. mpCRISTAR: multiple plasmid approach for CRISPR/Cas9 and TAR-mediated multiplexed refactoring of natural product biosynthetic gene clusters. ACS Synth. Biol. 9, 175–180 (2020).

    Article  PubMed  Google Scholar 

  89. Lian, J., Schultz, C., Cao, M., HamediRad, M. & Zhao, H. Multi-functional genome-wide CRISPR system for high throughput genotype–phenotype mapping. Nat. Commun. 10, 5794 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Napolitano, M. G. et al. Emergent rules for codon choice elucidated by editing rare arginine codons in Escherichia coli. Proc. Natl Acad. Sci. USA 113, E5588–E5597 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Ronda, C., Pedersen, L. E., Sommer, M. O. A. & Nielsen, A. T. CRMAGE: CRISPR optimized MAGE recombineering. Sci. Rep. 6, 19452 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Oesterle, S., Gerngross, D., Schmitt, S., Roberts, T. M. & Panke, S. Efficient engineering of chromosomal ribosome binding site libraries in mismatch repair proficient Escherichia coli. Sci. Rep. 7, 12327 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Asin-Garcia, E., Martin-Pascual, M., Garcia-Morales, L., Van Kranenburg, R. & Martins Dos Santos, V. A. P. ReScribe: an unrestrained tool combining multiplex recombineering and minimal-PAM ScCas9 for genome recoding Pseudomonas putida. ACS Synth. Biol. 10, 2672–2688 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Jiang, Y. et al. Multigene editing in the Escherichia coli genome via the CRISPR-Cas9 system. Appl. Environ. Microbiol. 81, 2506–2514 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Pyne, M. E., Moo-Young, M., Chung, D. A. & Chou, C. P. Coupling the CRISPR/Cas9 system with lambda red recombineering enables simplified chromosomal gene replacement in Escherichia coli. Appl. Environ. Microbiol. 81, 5103–5114 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Garst, A. D. et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nat. Biotechnol. 35, 48–55 (2017). Extensive work that established the CREATE platform for high-throughput targeted and trackable genome engineering in E. coli based on CRISPR-Cas9 and recombineering.

    Article  CAS  PubMed  Google Scholar 

  97. Liu, R. et al. Iterative genome editing of Escherichia coli for 3-hydroxypropionic acid production. Metab. Eng. 47, 303–313 (2018).

    Article  CAS  PubMed  Google Scholar 

  98. Roy, K. R. et al. Multiplexed precision genome editing with trackable genomic barcodes in yeast. Nat. Biotechnol. 36, 512–520 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Gaudelli, N. M. et al. Programmable base editing of A–T to G–C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Banno, S., Nishida, K., Arazoe, T., Mitsunobu, H. & Kondo, A. Deaminase-mediated multiplex genome editing in Escherichia coli. Nat. Microbiol. 3, 423–429 (2018).

    Article  CAS  PubMed  Google Scholar 

  103. Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824–844 (2020).

    Article  CAS  PubMed  Google Scholar 

  104. Wang, Y. et al. In-situ generation of large numbers of genetic combinations for metabolic reprogramming via CRISPR-guided base editing. Nat. Commun. 12, 678 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Volke, D. C., Martino, R. A., Kozaeva, E., Smania, A. M. & Nikel, P. I. Modular (de)construction of complex bacterial phenotypes by CRISPR/nCas9-assisted, multiplex cytidine base-editing. Nat. Commun. 13, 3026 (2022). This work established and demonstated an efficient base-editing tool for multiplex editing in the emerging model organism P. putida.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Tong, Y., Jørgensen, T. S., Whitford, C. M., Weber, T. & Lee, S. Y. A versatile genetic engineering toolkit for E. coli based on CRISPR-prime editing. Nat. Commun. 12, 5206 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Reis, A. C. et al. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays. Nat. Biotechnol. 37, 1294–1301 (2019).

    Article  CAS  PubMed  Google Scholar 

  108. Cui, L. et al. A CRISPRi screen in E. coli reveals sequence-specific toxicity of dCas9. Nat. Commun. 9, 1912 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Si, T., Luo, Y., Bao, Z. & Zhao, H. RNAi-assisted genome evolution in Saccharomyces cerevisiae for complex phenotype engineering. ACS Synth. Biol. 4, 283–291 (2015).

    Article  CAS  PubMed  Google Scholar 

  110. Na, D. et al. Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs. Nat. Biotechnol. 31, 170–174 (2013).

    Article  CAS  PubMed  Google Scholar 

  111. Xie, W. H., Deng, H. K., Hou, J. & Wang, L. J. Synthetic small regulatory RNAs in microbial metabolic engineering. Appl. Microbiol. Biotechnol. 105, 1–12 (2021).

    Article  CAS  PubMed  Google Scholar 

  112. Kiattisewee, C. et al. Portable bacterial CRISPR transcriptional activation enables metabolic engineering in Pseudomonas putida. Metab. Eng. 66, 283–295 (2021).

    Article  CAS  PubMed  Google Scholar 

  113. Liu, Y., Wan, X. & Wang, B. Engineered CRISPRa enables programmable eukaryote-like gene activation in bacteria. Nat. Commun. 10, 3693 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Lin, J. L., Wagner, J. M. & Alper, H. S. Enabling tools for high-throughput detection of metabolites: metabolic engineering and directed evolution applications. Biotechnol. Adv. 35, 950–970 (2017).

    Article  CAS  PubMed  Google Scholar 

  115. Leavell, M. D., Singh, A. H. & Kaufmann-Malaga, B. B. High-throughput screening for improved microbial cell factories, perspective and promise. Curr. Opin. Biotechnol. 62, 22–28 (2020).

    Article  CAS  PubMed  Google Scholar 

  116. Sarnaik, A., Liu, A., Nielsen, D. & Varman, A. M. High-throughput screening for efficient microbial biotechnology. Curr. Opin. Biotechnol. 64, 141–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  117. Chen, L., Sun, G.-G., Fang, H.-T. & He, K. in DEStech Transactions on Engineering and Technology Research 110–117 (DEStech Publications, 2019); https://doi.org/10.12783/dtetr/amee2019/33444

  118. Yang, H. et al. Study on fluorescence spectra of thiamine, riboflavin and pyridoxine. In Proc. Seventh International Symposium on Precision Mechanical Measurements 99030H (SPIE, 2016); https://doi.org/10.1117/12.2211248

  119. Sesen, M. & Whyte, G. Image-based single cell sorting automation in droplet microfluidics. Sci. Rep. 10, 8736 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Fu, L., Zhang, J. & Si, T. Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research. BMC Energy 2, 1 (2020).

    Article  Google Scholar 

  121. Häbe, T. T. et al. Ultrahigh-throughput ESI-MS: sampling pushed to six samples per second by acoustic ejection mass spectrometry. Anal. Chem. 92, 12242–12249 (2020).

    Article  PubMed  Google Scholar 

  122. Sinclair, I., Davies, G. & Semple, H. Acoustic mist ionization mass spectrometry (AMI-MS) as a drug discovery platform. Expert Opin. Drug Discov. 14, 609–617 (2019).

    Article  CAS  PubMed  Google Scholar 

  123. Wyatt Shields Iv, C., Reyes, C. D. & López, G. P. Microfluidic cell sorting: a review of the advances in the separation of cells from debulking to rare cell isolation. Lab Chip 15, 1230–1249 (2015).

    Article  Google Scholar 

  124. Bouzetos, E., Ganar, K. A., Mastrobattista, E., Deshpande, S. & van der Oost, J. (R)evolution-on-a-chip. Trends Biotechnol. https://doi.org/10.1016/j.tibtech.2021.04.009 (2021).

  125. Caen, O. et al. High-throughput multiplexed fluorescence-activated droplet sorting. Microsyst. Nanoeng. 4, 33 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Guo, L., Zeng, W., Xu, S. & Zhou, J. Fluorescence-activated droplet sorting for enhanced pyruvic acid accumulation by Candida glabrata. Bioresour. Technol. 318, 124258 (2020).

    Article  CAS  PubMed  Google Scholar 

  127. Liang, W. F. et al. Biosensor-assisted transcriptional regulator engineering for Methylobacterium extorquens AM1 to improve mevalonate synthesis by increasing the acetyl-CoA supply. Metab. Eng. 39, 159–168 (2017). This study nicely demonstrated the high-throughput targeting of a global regulator for improving mevalonate production by using an in vivo sensor for this product in a non-conventional, methylotrophic bacterium.

    Article  CAS  PubMed  Google Scholar 

  128. van Rossum, T., Kengen, S. W. M. & van der Oost, J. Reporter-based screening and selection of enzymes. FEBS J. 280, 2979–2996 (2013).

    Article  PubMed  Google Scholar 

  129. Rogers, J. K. & Church, G. M. Genetically encoded sensors enable real-time observation of metabolite production. Proc. Natl Acad. Sci. USA 113, 2388–2393 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Fang, M. et al. Intermediate-sensor assisted push-pull strategy and its application in heterologous deoxyviolacein production in Escherichia coli. Metab. Eng. 33, 41–51 (2016).

    Article  CAS  PubMed  Google Scholar 

  131. Xu, P. et al. Design and kinetic analysis of a hybrid promoter-regulator system for malonyl-CoA sensing in Escherichia coli. ACS Chem. Biol. 9, 451–458 (2014).

    Article  CAS  PubMed  Google Scholar 

  132. Tang, S.-Y. & Cirino, P. C. Design and application of a mevalonate-responsive regulatory protein. Angew. Chem. 123, 1116–1118 (2011).

    Article  Google Scholar 

  133. Rogers, J. K., Taylor, N. D. & Church, G. M. Biosensor-based engineering of biosynthetic pathways. Curr. Opin. Biotechnol. 42, 84–91 (2016).

    Article  CAS  PubMed  Google Scholar 

  134. Raman, S., Rogers, J. K., Taylor, N. D. & Church, G. M. Evolution-guided optimization of biosynthetic pathways. Proc. Natl Acad. Sci. USA 111, 17803–17808 (2014). This study demontrates the strenght of combining rational genome-wide targetting (MAGE) with product biosensors controlling selectable markers, leading to many-fold improved naringenin and glucaric acid production in E. coli.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Orsi, E., Claassens, N. J., Nikel, P. I. & Lindner, S. N. Growth-coupled selection of synthetic modules to accelerate cell factory development. Nat. Commun. 12, 5295 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Kim, S. et al. Growth of E. coli on formate and methanol via the reductive glycine pathway. Nat. Chem. Biol. 16, 538–545 (2020).

    Article  CAS  PubMed  Google Scholar 

  137. Antonovsky, N. et al. Sugar synthesis from CO2 in Escherichia coli. Cell 166, 115–125 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Guzmán, G. I. et al. Enzyme promiscuity shapes adaptation to novel growth substrates. Mol. Syst. Biol. 15, e8462 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Saleski, T. E. et al. Syntrophic co-culture amplification of production phenotype for high- throughput screening of microbial strain libraries. Metab. Eng. 54, 232–243 (2019).

    Article  CAS  PubMed  Google Scholar 

  140. Patinios, C. et al. Streamlined CRISPR genome engineering in wild-type bacteria using SIBR-Cas. Nucleic Acids Res. 49, 11392–11404 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Chao, R., Mishra, S., Si, T. & Zhao, H. Engineering biological systems using automated biofoundries. Metab. Eng. 42, 98–108 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Hillson, N. et al. Building a global alliance of biofoundries. Nat. Commun. 10, 2040 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Isaacs, F. J. et al. Precise manipulation of chromosomes in vivo enables genome-wide codon replacement. Science 6040, 348–353 (2017).

    Google Scholar 

  144. Ostrov, N. et al. Design, synthesis and testing toward a 57-codon genome. Science 353, 819–822 (2016).

    Article  CAS  PubMed  Google Scholar 

  145. Szili, P. et al. Rapid evolution of reduced susceptibility against a balanced dual-targeting antibiotic through stepping-stone mutations. Antimicrob. Agents Chemother. 63, e00207–e00219 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Campa, C. C., Weisbach, N. R., Santinha, A. J., Incarnato, D. & Platt, R. J. Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts. Nat. Methods 16, 887–893 (2019).

    Article  CAS  PubMed  Google Scholar 

  147. Jung, S. W., Yeom, J., Park, J. S. & Yoo, S. M. Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories. Biotechnol. Adv. 50, 107767 (2021).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank R. van Kranenburg and E. Orsi for critical reading of this manuscript. S.Y., N.J.C. and J.v.d.O. acknowledge the support of the Dutch Research Council (NWO) via the Gravitation Project BaSyC (024.003.019) and Spinoza (SPI 93-537), awarded to J.v.d.O. In addition, N.J.C. acknowledges support from his NWO Veni fellowship (VI.Veni.192.156). Funding for this research was also provided by the US Department of Energy (DOE) under grant no. DE-FG02-02ER63445 and by the National Science Foundation (NSF) award no. 2123243 (both to G.M.C.). A.N. was supported by the EMBO LTF 160-2019 Long-Term fellowship.

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Correspondence to Nico J. Claassens.

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J.v.d.O. is included as inventor on several CRISPR-related patents and is scientific advisor of NTrans Technologies, Scope Biosciences and Hudson River Biotechnology. G.M.C. is a founder of companies in which he has related financial interests: ReadCoor, EnEvolv and 64x Bio. For a complete list of G.M.C.’s financial interests, see also https://arep.med.harvard.edu/gmc/tech.html. A.N. is an inventor on a patent related to directed evolution with random genomic mutations (DIvERGE) (US patent 10669537B2: Mutagenizing intracellular nucleic acids). The remaining authors declare no competing interests.

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Yilmaz, S., Nyerges, A., van der Oost, J. et al. Towards next-generation cell factories by rational genome-scale engineering. Nat Catal 5, 751–765 (2022). https://doi.org/10.1038/s41929-022-00836-w

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