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CRISPR–dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization

Nature Chemical Biology volume 19, pages 367–377 (2023)Cite this article

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Abstract

The production efficiency of microbial cell factories is sometimes limited by the lack of effective methods to regulate multiple targets in a coordinated manner. Here taking the biosynthesis of glucosamine-6-phosphate (GlcN6P) in Bacillus subtilis as an example, a ‘design–build–test–learn’ framework was proposed to achieve efficient multiplexed optimization of metabolic pathways. A platform strain was built to carry biosensor signal-amplifying circuits and two genetic regulation circuits. Then, a synthetic CRISPR RNA array blend for boosting and leading (ScrABBLE) device was integrated into the platform strain, which generated 5,184 combinatorial assemblies targeting three genes. The best GlcN6P producer was screened and engineered for the synthesis of valuable pharmaceuticals N-acetylglucosamine and N-acetylmannosamine. The N-acetylglucosamine titer reached 183.9 g liter–1 in a 15-liter bioreactor. In addition, the potential generic application of the ScrABBLE device was also verified using three fluorescent proteins as a case study.

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Fig. 1: The framework of CRISPR–dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization.
Fig. 2: Construction and demonstration of the BSAC.
Fig. 3: Design and construction of the CRISPRi-based phase-dependent GRC.
Fig. 4: The GlcN6P-responsive BSAC guided multiplexed and phase-dependent regulation.
Fig. 5: Generic application of CRISPR–dCas12a-mediated multiplexed regulation by targeting three fluorescent proteins.
Fig. 6: Characterization and application of the engineered strains.

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Data availability

All data supporting the findings of this study are available in the article and its supplementary files. Reads from NGS of the ScrABBLE device for GlcN6P production are available at Sequence Read Archive (PRJNA818127). Sanger sequencing source data can be found on GitHub (https://github.com/KevinW03/Dynamic-model-of-metabolic-pathways-related-to-GlcN6P-and-GlcNAc). Sequences with the necessary annotations of the plasmids used in this study are uploaded to Benchling, and the corresponding URLs are shown in Supplementary Note 3. Flow cytometry data are deposited at Flow Repository (FR-FCM-Z55M, FR-FCM-Z56B and FR-FCM-Z5F6). Source data are provided with this paper.

Code availability

The MATLAB code used in this study can be found on GitHub (https://github.com/KevinW03/Dynamic-model-of-metabolic-pathways-related-to-GlcN6P-and-GlcNAc).

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Acknowledgements

This work was supported by the National Key R&D Program of China (2020YFA0908300 to L.L. and 2018YFA0900300 to G.D.), the National Natural Science Foundation of China (32021005 and 32070085 to L.L. and 31930085 to G.D.), the China National Postdoctoral Program for Innovative Talents (BX2021113 to Y.W.), the China Postdoctoral Science Foundation (2021M701458 to Y.W.), the Natural Science Foundation of Jiangsu Province (BK20221083 to Y.W.) and the Fundamental Research Funds for the Central Universities (USRP52019A and JUSRP221013 to L.L. and JUSRP121010 to X.L.).

Author information

Authors and Affiliations

  1. Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

    Yaokang Wu, Yang Li, Ke Jin, Jianghua Li, Yanfeng Liu, Xueqin Lv & Long Liu

  2. Science Center for Future Foods, Jiangnan University, Wuxi, China

    Yaokang Wu, Yang Li, Ke Jin, Jianghua Li, Yanfeng Liu, Guocheng Du, Xueqin Lv, Jian Chen & Long Liu

  3. Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China

    Linpei Zhang

  4. Department of Bioengineering and Centre for Synthetic Biology, Imperial College London, London, UK

    Rodrigo Ledesma-Amaro

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Contributions

Y.W. and L.L. conceived the study and wrote the manuscript with assistance from X.L., Y. Liu, J.L., G.D., R.L.-A. and J.C. Y.W. performed strain construction and screening, cell cultivation, fluorescence measurements, flow cytometry assays and FACS, fluorescence imaging and analysis, analytical experiments, sequencing analysis, kinetic model simulation and data visualization. Y. Li assisted in strain construction and screening, cell cultivation, fluorescence measurements, flow cytometry assays and FACS and analytical experiments. K.J. assisted in fluorescence imaging and analysis. L.Z. assisted in flow cytometry assays and FACS. All authors contributed ideas and reviewed the manuscript. L.L. supervised and held overall responsibility for the study.

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Correspondence to Long Liu.

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Extended data

Extended Data Fig. 1 Knocking out of genes related to the catabolic pathway of aminosaccharide and the by-product (lactate and acetate) formation.

a Schematic representation of the metabolites (x1-x8) and reactions (v1-v11) used for reprogramming and kinetic model simulation. b Engineered strains, including BNZR0.00, BNZR0.10, BNZR0.40, and BNZR0.60, were constructed by knocking out the related genes. Seven colonies were picked to perform colony PCR for each strain, and the experiment was independently repeated three times with similar results. c The kinetic model simulations of metabolism in the engineered strains (BNZR0.00, BNZR0.10, BNZR0.40, and BNZR0.60) were performed in three situations, including untreated, supplying GlcN in the media, and introducing GNA1 to produce GlcNAc. Lines indicate mean and shaded areas are the SD, and the colors of the lines represent: black-Glc, red-GlcNAc, green-GlcN6P, yellow-HMP, blue-EMP, and magenta-PSP. Source data are provided as a Source Data file. The codes used in c can be found on GitHub.

Source data

Extended Data Fig. 2 Characterization of glucose-responsive promoters that were used for the construction of the feedforward GRC.

These promoters were investigated by expressing eGFP under different glucose concentrations. The expression of the commonly used constitutive promoter Pveg was also improved by adding glucose, but its dose-response pattern was far from the tested glucose-responsive promoters, suggesting that the improvement may result from the enhanced metabolism after supplying glucose. The numbered promoters with red shade were used for the construction of strains BNZR1.00, BNZR1.02, BNZR1.03, BNZR1.06, and BNZR1.12, respecitively. The values marked in bule represent the basal expression of these promoters without glucose. Data are presented as mean values ± SD from three independent biological replicates (n = 3). Source data are provided as a Source Data file.

Source data

Extended Data Fig. 3 Kinetic model simulations of cell metabolism after removing the feedback regulation in glmS.

a The definition of metabolites and reactions for kinetic model simulation. b The kinetic model simulations of metabolism in the engineered strains (BNZR1.00, BNZR1.06, and BNZR1.12) were performed in three situations: untreated, supplying GlcN in the media, and introducing GNA1 to produce GlcNAc. Lines indicate mean and shaded areas are the SD, and the colors of the lines represent: black-Glc, red-GlcNAc, green-GlcN6P, yellow-HMP, blue-EMP, and magenta-PSP. The codes used in b can be found on GitHub.

Extended Data Fig. 4 Evaluation of CRISPRi-based phase-dependent GRC.

Using P43-mCherry as the reporter, the performances of the phase-dependent GRC by replacing the promoter of dCas12a (a) or crRNA (b) were analyzed under glucose. By using PyteJ-mCherry as the reporter, the performances of the phase-dependent GRC by replacing the promoter of dCas12a (c) or crRNA (d) were analyzed, and these two designs were also tested under glucose (e,f). The red shade shows the time frame in which the phase-dependent GRC worked. a-f Lines indicate mean and shaded areas are the SD, and the data are presented as mean values ± SD from three independent biological replicates (n = 3). Source data are provided as a Source Data file.

Source data

Extended Data Fig. 5 Kinetic model simulations of cell metabolism after repressing HMP, EMP, and PSP.

a Schematic diagram of the metabolites (x1-x8) and reactions (v1-v11) used for reprogramming and kinetic model simulation. b The kinetic model simulation of GlcN6P level without and with repressing the above three pathways. Random values between 0–97%, 0–95%, or 0–77% were generated to serve as repression intensities of genes zwf, pfkA, or glmM, and the levels of the intracellular GlcN6P in these three situations (weakening zwf, pfkA, or glmM) were studied by performing 1000 simulations (lines indicate mean and shaded areas the standard deviation). Lines indicate mean and shaded areas are the SD, and the colors of the lines represent: black-Glc, red-GlcNAc, green-GlcN6P, yellow-HMP, blue-EMP, and magenta-PSP. c The relationships between the predicted final concentration of GlcN6P and the generated repression intensities of these three target genes. Positive correlations can be found between the predicted GlcN6P concentrations and the repression intensities of the target genes. Source data are provided as a Source Data file. The codes used in b can be found on GitHub.

Source data

Extended Data Fig. 6 The first-round screening of the cells possesses higher fluorescence after installing the ScrABBLE device.

a FACS was performed to acquire the cells that possess higher fluorescence. After spreading them into the plates, the whole population in gate Q4 was sorted for further screening. b A total of 190 colonies were randomly selected and inoculated into two 96-well plates for fluorescence measurement. The colonies in green and yellow were picked, while the colonies in red and grey were discarded. c The crRNA arrays in 24 strains with the highest fluorescence were amplified for Sanger sequencing. Source data are provided in the Source Data file. Flow cytometry data is deposited at Flow Repository.

Source data

Extended Data Fig. 7 An erythromycin resistance gene was put into the BSAC of BNZR2.00 to couple the the cell growth with biosensor response.

a Schematic representation of the biosensor amplifier circuits integrated with an erythromycin resistance gene. b The effects on cell growth by changing the adding amount of erythromycin. The adding amount of erythromycin was optimized, and 50 μg/mL was the best. In this concentration of erythromycin, supplying IPTG (to achieve the BSAC activation mediated by the native intracellular GlcN6P) could increase cell growth, and supplying GlcN to further improve the GlcN6P level could get a better cell growth. b Data are presented as mean values ± SD from three independent biological replicates (n = 3), and the circles represent individual data points. Significance (p-value) was evaluated by two-sided t-test, n.s. presents p > 0.05. Source data are provided as a Source Data file.

Source data

Extended Data Fig. 8 Flow cytometry analysis of BSAC response in the engineered strains.

The fluorescence of eGFP in strains BNZR0.00, BNZR0.60, BNZR1.06 L, BNZR2.00, and BNZR2.01R-BNZR2.14R was determined by by flow cytometry at 12 h (green shade) and 24 h (blue shade). Flow cytometry data is deposited at Flow Repository.

Extended Data Fig. 9 Flow cytometry analysis of the repression intensities by the phase-dependent GRC on the target genes.

The fusion proteins composed of gene-sfGFP and mCherry expressed by a strong constitutive promoter P43 were introduced into strains BNZR2.00, BNZR2.03R, BNZR2.06R, BNZR2.12R, and BNZR2.09R, respectively. The samples at 36 h were analyzed by flow cytometry. Flow cytometry data is deposited at Flow Repository.

Extended Data Fig. 10 The analysis of the secondary structures of the RNAs in the crRNA arrays of BNZR2.03R, BNZR2.12R, and BNZR2.09R.

The results show that restricted access of dCas12a’s to some cleavage sites, and only the crRNA array of BNZR2.06R exposes the five cleavage sites of dCas12a.

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Wu, Y., Li, Y., Jin, K. et al. CRISPR–dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization. Nat Chem Biol 19, 367–377 (2023). https://doi.org/10.1038/s41589-022-01230-0

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