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Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast

Nature Chemical Biology volume 18, pages 520–529 (2022)Cite this article

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An Author Correction to this article was published on 16 August 2022

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Abstract

Advances in synthetic biology enable microbial hosts to synthesize valuable natural products in an efficient, cost-competitive and safe manner. However, current engineering endeavors focus mainly on enzyme engineering and pathway optimization, leaving the role of cofactors in microbial production of natural products and cofactor engineering largely ignored. Here we systematically engineered the supply and recycling of three cofactors (FADH2, S-adenosyl-l-methion and NADPH) in the yeast Saccharomyces cerevisiae, for high-level production of the phenolic acids caffeic acid and ferulic acid, the precursors of many pharmaceutical molecules. Tailored engineering strategies were developed for rewiring biosynthesis, compartmentalization and recycling of the cofactors, which enabled the highest production of caffeic acid (5.5 ± 0.2 g l−1) and ferulic acid (3.8 ± 0.3 g l−1) in microbial cell factories. These results demonstrate that cofactors play an essential role in driving natural product biosynthesis and the engineering strategies described here can be easily adopted for regulating the metabolism of other cofactors.

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Fig. 1: Biosynthesis of CaA and FA requires numerous cofactors.
Fig. 2: Construction of CaA/FA biosynthetic pathways in yeast.
Fig. 3: Engineering NADPH regeneration for CaA production by pulling nonoxidizing steps of the PPP during glucose-limited conditions.
Fig. 4: Manipulation of FADH2 biosynthesis and relocation for enhancing CaA production.
Fig. 5: Engineering SAM biosynthesis and recycling for FA production.

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Data availability

The main data supporting the findings of this study are available within the article and its Supplementary Information files. Source data are provided with this paper. Extra data are available from the corresponding author upon request.

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Acknowledgements

This work was funded by the National Key Research and Development Program of China (grant no. 2018YFA0900300 to Y.J.Z.), National Natural Science Foundation of China (grant nos. 31970316 to L.Z., 21922812 to Y.J.Z. and 32000231 to R.C.), LiaoNing Revitalization Talents Program (grant no. XLYC1807191 to Y.J.Z.) and Shanghai Sail Program (grant no. 19YF1459300 to R.C.). We appreciate Y. Zhao (East China University of Science and Technology) for helpful discussion about FAD quantification. We also thank J. Yang and C. Song for raw data summary and AiMi Academic Services (www.aimieditor.com) for English language editing.

Author information

Authors and Affiliations

  1. Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China

    Ruibing Chen, Jiaoqi Gao, Wei Yu, Xiaoxin Zhai & Yongjin J. Zhou

  2. Department of Pharmaceutical Botany, School of Pharmacy, Naval Medical University, Shanghai, China

    Ruibing Chen, Xianghui Chen & Lei Zhang

  3. Biomedical Innovation R&D Center, School of Medicine, Shanghai University, Shanghai, China

    Xianghui Chen & Lei Zhang

  4. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Yu Chen

  5. Institute of Interdisciplinary Integrative Medicine Research, School of Medicine, Nantong University, Nantong, China

    Lei Zhang

  6. CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China

    Yongjin J. Zhou

  7. Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China

    Yongjin J. Zhou

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  1. Ruibing Chen
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Contributions

Y.J.Z. and L.Z., conceived the original research. R.C designed and performed experiments and analyzed the data. R.C., J.G., X.Z. and W.Y. performed the fed-batch fermentations. R.C. and X.C performed the metabolic analysis. Y.C. performed the MFA. R.C., L.Z. and Y.J.Z. wrote the manuscript.

Corresponding authors

Correspondence to Lei Zhang or Yongjin J. Zhou.

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Competing interests

Authors have filed two patents (PCT/CN2021/138511 and PCT/CN2021/138510) for protection of the cofactor engineering strategies of the work described herein.

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Extended data

Extended Data Fig. 1 Biosynthesis of phenolic acids and their derivatives.

Structures of pCA- (green), CaA- (orange) and FA-derived (pink) natural products, and their pharmaceutical properties.

Extended Data Fig. 2 Engineering strategies for improving the production of CaA and FA.

The engineered gene targets for pathway optimization were marked as golden and the cofactor engineering was marked as blue. See Fig. 2 and Fig. 3 legends regarding abbreviations of other metabolites.

Extended Data Fig. 3 Engineering the biosynthesis of precursor pCA in yeast.

a. Overview of the engineered metabolic pathway for pCA biosynthesis. Optimization of the shikimic acid and aromatic amino acid (AAA) pathways together with the introduction of bacteria- and plant-derived phenolic acid pathways were used for pCA production. ARO10 encoding phenylpyruvate decarboxylase, PDC5 encoding pyruvate decarboxylase, ARO4 encoding 3-deoxy-D-arabino-heptulosonate-7-phosphate (DAHP) synthase, ARO7 encoding chorismate mutase, EcAROL encoding E. coli shikimate kinase II, FjTAL encoding Flavobacterium johnsoniae tyrosine ammonia lyase, SbPAL1 encoding Isatis indigotica phenylalanine ammonia lyase 1, AtCPR1 encoding Arabidopsis thaliana cytochrome P450 reductase, PtrC4H1/2 encoding Populus trichocarpa cinnamic acid hydroxylase 1/2, PtrC3H encoding P. trichocarpa coumarate-3-hydroxylase, CYP complex consisting of PtrC4H1 + PtrC4H2 + PtrC3H. PEP, phosphoenolpyruvate; DHQ, 3-dehydroquinate; DHS, 3-dehydro-shikimate; SHIK, shikimate; S3P, shikimate-3-phosphate; EPSP, 5-enolpyruvyl-shikimate-3-phosphate; CHA, chorismic acid; PPA, prephenate; PPY, phenylpyruvate; HPP, para-hydroxy-phenylpyruvate; Phe, phenylalanine; Tyr, tyrosine; CA, cinnamic acid; pCA, coumaric acid; CaA, caffeic acid; FA, ferulic acid. b. Production of pCA in yeast. c. Introduction of the CYP complex further improved pCA biosynthesis. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. Statistical analysis was performed by using Student’s t test (one-tailed; two-sample unequal variance; *p < 0.05, **p < 0.01, ***p < 0.001).

Source data

Extended Data Fig. 4 Optimization of HPAB/C and the shikimic acid pathway for CaA production.

a. Overview of the yeast metabolic pathway for CaA biosynthesis. ARO1 encoding shikimate dehydrogenase, ARO2 encoding chorismate synthase, ARO3 encoding DAHP synthase, MtPDH1 encoding Medicago truncatula prephenate dehydrogenase, PHA2 encoding S. cerevisiae prephenate dehydratase 2. See Supplementary Fig. 5 legend regarding other abbreviations. The fusion EcHPAB-EcHPAC (a) and PaHPAB-SeHPAC (b) result in a negative effect on CaA production. PaHPAB, encoding Pseudomonas aeruginosa HpaB; SeHPAC, encoding Salmonella enterica HpaC; EcHPAB: encoding E. coli HpaB; EcHPAC: encoding E. coli HpaC. d. Further optimization of the shikimic acid pathway increased CaA titers with the CYP system. Cells were grown in defined minimal medium containing 20 g/L glucose and cultures were extracted after 96 h of growth for phenolic acid detection. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. Statistical analysis was performed by using Student’s t test (one-tailed; two-sample unequal variance; *p < 0.05, **p < 0.01, ***p < 0.001).

Source data

Extended Data Fig. 5 Effect of combining MCH5 overexpression with FADH2 strategies on CaA production.

a. Schematic illustration of the combinations of FADH2 strategies. See Fig. 4 legend regarding abbreviations. (+) indicates expression under strong constitutive promoters. Strain RB19 is the reference strain for regulating FADH2. b. Relative titers of engineered strains normalized to the titers of reference strain RB19. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation.

Source data

Extended Data Fig. 6 Quantification of flavin derived cofactors in CaA producing strains.

a. Representative chromatographs of standards and samples. Total concentration of intracellular FMN(H2) (b), intracellular riboflavin (c) and extracellular riboflavin (d) was detected at different time points. μmol gDCW-1 refers to the number of moles of cofactor per gram of dry cell weight (DCW). All strains were grown at 30 °C in 20 mL of Delft-D medium containing 20 g/L glucose with an initial OD600 of 0.1. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. Statistical analysis was performed by using Student’s t test (one-tailed; two-sample unequal variance; *p < 0.05, **p < 0.01, ***p < 0.001).

Source data

Extended Data Fig. 7 Metabolic flux analysis (MFA) in the CaA producer RB209HU (right, with cofactor-engineering strategy) and RB103HU (left, without cofactor-engineering strategy).

Based on MFA, the fluxes to the different products were represented relative to the uptake of 100 mmol of glucose. Cofactor-related pathways is highlighted in blue. 1,3BPG, 1,3-biphosphoglycerate. See Fig. 2 and Fig. 3 legends regarding abbreviations of other metabolites.

Extended Data Fig. 8 Cofactor engineering and pathway optimization for FA production.

a. Schematic illustration of the SAM cycle. LiMETK1 encoding Leishmania infantum S-adenosylmethionine synthetases, MET6 encoding S. cerevisiae methionine synthase, cMTHFR encoding chemeric methylenetetrahydrofolate reductase. SAM, S-adenosylmethionine; Met, methionine; Hcys, homocysteine; SAH, S-adenosylhomocysteine; CH3-THF, 5-methyl tetrahydrogen folic acid; 5, 10-CH2-THF, 5, 10-methylene tetrahydrogen folic acid; ATP, adenosine triphosphate. b. Overexpression of the SAM pathway reduced phenolic acid titers. c. Enhancing CaA biosynthesis improved FA production. ARO1 encoding shikimate dehydrogenase, ARO2 encoding chorismate synthase, ARO3 encoding DAHP synthase, MtPDH1 encoding Medicago truncatula prephenate dehydrogenase, PHA2 encoding S. cerevisiae prephenate dehydratase 2, LmXFPK, encoding Leuconostoc mesenteroides phosphoketolase; CkPTA, encoding Clostridium kluyveri phosphotransacetylase. gpp1Δ indicates deletion of GPP1 (encoding GAP phosphatase). d. Overexpression of NtCOMT1 using multiple gene copies under of the control of PGAL or strong constitutive promoters improved FA production and the CaA conversion rate. The # indicates expression under strong constitutive promoters (PtHXT7 and PTDH3). All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. Statistical analysis was performed by using Student’s t test (one-tailed; two-sample unequal variance; *p < 0.05, **p < 0.01, ***p < 0.001).

Source data

Extended Data Fig. 9 Quantification of cellular SAM and SAH in FA producing strains.

a. Representative chromatographs of standards and samples. Corresponding mass spectra of SAM (b) and SAH (c). d. Cellular SAM/SAH ratios were detected at different time points. All strains were grown at 30 °C in 20 mL of Delft-D medium containing 20 g/L glucose. All data represent the mean of n = 3 independent biological samples and error bars show standard deviation. Statistical analysis was performed by using Student’s t test (one-tailed; two-sample unequal variance; *p < 0.05, **p < 0.01, ***p < 0.001).

Source data

Extended Data Fig. 10 Metabolic flux analysis (MFA) of the FA producer RB218HU (right, with cofactor-engineering strategy) and RB101HU (left, without cofactor-engineering strategy).

Based on MFA, the fluxes to the different products were represented relative to the uptake of 100 mmol of glucose. Cofactor-related pathways is highlighted in blue. See Fig. 2 and Fig. 3 legends regarding abbreviations of other metabolites.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Tables 1–5 and data legends.

Reporting Summary

Supplementary Data 1

Primers used in this study.

Supplementary Data 2

Overview of DNA constructs used in this study.

Supplementary Data 3

Codon-optimized genes used in this study.

Supplementary Data 4

Homology sequences for integration at selected chromosomal loci.

Supplementary Data 5

New reactions for metabolic flux analysis.

Supplementary Data 6

Code of culture simulation for metabolic flux analysis.

Supplementary Data 7

Code of yeast genome-scale metabolic model for metabolic flux analysis.

Source data

Source Data Fig. 2

Statistical source data.

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Statistical source data.

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Chen, R., Gao, J., Yu, W. et al. Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast. Nat Chem Biol 18, 520–529 (2022). https://doi.org/10.1038/s41589-022-01014-6

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