Navigable Genome Engineering: Stepwise Correlation for Precision-Guided Optimization of Microbial Cell Factory Phenotypes

Abstract

Microbial cell factories, akin to “chips” in biomanufacturing, concentrate the most intricate scientific challenges, technical bottlenecks, and densest intellectual property. However, despite extensive efforts in rational engineering, the inherent complexity of biological systems and the limited knowledge of their underlying mechanisms still incur substantial trial-and-error costs. This Perspective seeks to explore the potential of a prior-knowledge-independent approach for optimizing microbial cell factory phenotypes. We discuss the feasibility of stepwise genotypic navigation in genome engineering and emphasize its ability to generate high-quality genotype-phenotype association data, thereby advancing AI-assisted genome modeling and further enabling precision-guided optimization.

Keywords

Microbial cell factories / Phenotypic optimization / Genome engineering / Stepwise genotypic navigation

Acknowledgments

We thank members of the laboratory for helpful discussions.

Author Contributions

Conceptualization, C.Z.; Writing—Original Draft Preparation, X.Y.; Writing—Review & Editing, J.G. and J.S.; Project Administration, C.Z.; Funding Acquisition, C.Z.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No datasets were generated or analyzed during the current study.

Funding

This research received funding from the National Key Research and Development Program of China (2023YFC3402300), National Natural Science Foundation of China (U2032210), R&D projects in key areas of Guangdong Province (2022B1111050002) and the R&D projects of Hebei Province (22375503D).

Declaration of Competing Interest

The authors declare no competing financial interest.

References

[1] Wu YN, Jameel A, Xing XH, Zhang C. Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol. 2022, 40, 38-59.

[2] Simon AJ, d’Oelsnitz S, Ellington AD. Synthetic evolution. Nat. Biotechnol. 2019, 37, 730-743.

[3] Yang YF, Qiu MY, Yang Q, Wang Y, Wei H, Yang SH. Connecting Microbial Genotype with Phenotype in the Omics Era. Metab. Pathw. Eng. 2020, 2096, 217-233.

[4] Ma ZY, Li WJ, Shen YH, Xu YX, Liu GJ, Chang JM, et al. EvoAI enables extreme compression and reconstruction of the protein sequence space. Nat. Methods 2024, 22, 102-112.

[5] Papkou A, Garcia-Pastor L, Escudero JA, Wagner A. A rugged yet easily navigable fitness landscape. Science 2023, 382, 6673.

[6] Przybyla L, Gilbert LA. A new era in functional genomics screens. Nat. Rev. Genet. 2022, 23, 89-103.

[7] Teng YX, Jiang T, Yan YJ. The expanded CRISPR toolbox for constructing microbial cell factories. Trends Biotechnol. 2024, 42, 104-118.

[8] Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 2015, 16, 299-311.

[9] Liu Y, Wang RY, Liu JH, Lu H, Li HR, Wang Y, et al. Base editor enables rational genome-scale functional screening for enhanced industrial phenotypes in Corynebacterium glutamicum. Sci. Adv. 2022, 8, 35.

[10] Banta AB, Myers KS, Ward RD, Cuellar RA, Place M, Freeh CC, et al. A Targeted Genome-scale Overexpression Platform for Proteobacteria. bioRxiv 2024. doi: 10.1101/2024.03.01.582922.

[11] Sun L, Zheng P, Sun J, Wendisch VF, Wang Y. Genome-scale CRISPRi screening: A powerful tool in engineering microbiology. Eng. Microbiol. 2023, 3, 100089-100089.

[12] Wu YK, Li Y, Jin K, Zhang LP, Li JH, Liu YF, et al. CRISPR-dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization. Nat. Chem. Biol. 2023, 19, 367.

[13] Yin JA, Frick L, Scheidmann MC, Liu TT, Trevisan C, Dhingra A, et al. Arrayed CRISPR libraries for the genome-wide activation, deletion and silencing of human protein-coding genes. Nat. Biomed. Eng. 2025, 9, 127-148.

[14] Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 2009, 460, 894.

[15] Garst AD, Bassalo MC, Pines G, Lynch SA, Halweg-Edwards AL, Liu RM, et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nat. Biotechnol. 2017, 35, 48-55.

[16] Liu W, Pan Y, Zhang Y, Dong C, Huang L, Lian J. Intracellularly synthesized ssDNA for continuous genome engineering. Trends Biotech. 2024, 7799, 293. doi:10.1016/j.tibtech.2024.10.011.

[17] Liu WQ, Zuo SQ, Shao YR, Bi K, Zhao JR, Huang L, et al. Retron-mediated multiplex genome editing and continuous evolution in Escherichia coli. Nucleic Acids Res. 2023, 51, 8293-8307.

[18] Xu P, Liu ZH, Liu Y, Ma HZ, Xu YY, Bao Y, et al. Genome-wide interrogation of gene functions through base editor screens empowered by barcoded sgRNAs. Nat. Biotechnol. 2021, 39, 1403.

[19] Halperin SO, Tou CJ, Wong EB, Modavi C, Schaffer DV, Dueber JE. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window. Nature 2018, 560, 248.

[20] Moore CL, Papa LJ, Shoulders MD. A Processive Protein Chimera Introduces Mutations across Defined DNA Regions. J. Am. Chem. Soc. 2018, 140, 11560-11564.

[21] Zimmermann A, Prieto-Vivas JE, Cautereels C, Gorkovskiy A, Steensels J, Van de Peer Y, et al. A Cas3-base editing tool for targetable in vivo mutagenesis. Nat. Commun. 2023, 14, 3389.

[22] Chen XD, Chen Z, Wythes G, Zhang Y, Orr BC, Sun G, et al. Helicase-assisted continuous editing for programmable mutagenesis of endogenous genomes. Science 2024, 386, 6718.

[23] Deng HX, Yu H, Deng YW, Qiu YL, Li FF, Wang XR, et al. Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing. Adv. Sci. 2024, 11, e2306935.

[24] Nguyen E, Poli M, Durrant MG, Kang B, Katrekar D, Li DB, et al. Sequence modeling and design from molecular to genome scale with Evo. Science 2024, 386, eado9336.

[25] Shao B, Yan J. A long-context language model for deciphering and generating bacteriophage genomes. Nat. Commun. 2024, 15, 9392-9392.