Scalable secondary metabolite production in Streptomyces using a plug-and-play system

scalable-secondary-metabolite-production-in-streptomyces-using-a-plug-and-play-system

Data availability

The primary data supporting the findings of this study are accessible within the paper and Supplementary Information. Additional data can be obtained from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (W2411016 and 32170095 to W.W. and 32121005 and 32327801 to L.Z.), the National Key Research and Development Program of China (2020YFA0907800 to L.Z. and 2022YFC2105400 to C.Z.), the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-CSCB-202401 to S.L.), the Youth Innovation Promotion Association CAS (Y202027 to W.W.) and the 111 Project (B18022 to L.Z). We would like to thank Z. Fan, G. Ai, E. Li and T. Zhao from IMCAS for the BLI assay, LC–MS/MS analysis, UPLC–MS analysis and flow cytometry assay, respectively.

Author information

Author notes

  1. These authors contributed equally: Bowen Yang, Zilong Li, Jingyu Zhang.

Authors and Affiliations

  1. State Key Laboratory of Bioreactor Engineering, and School of Biotechnology, East China University of Science and Technology (ECUST), Shanghai, China

    Bowen Yang, Jingyu Zhang, Xueting Liu, Chengyu Zhang, Gao-Yi Tan, Lixin Zhang & Weishan Wang

  2. State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

    Bowen Yang, Zilong Li, Shiwen Qiu, Zonglin Liang, Hao Yan, Defeng Li, Shanshan Zhou, Chengyu Zhang & Weishan Wang

  3. Beijing Key Laboratory of Genetic Element Biosourcing & Intelligent Design for Biomanufacturing, Beijing, China

    Zilong Li & Weishan Wang

  4. State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China

    Yanyan Zhang & Shanshan Li

  5. Hebei Xingbai Agrochem Group Company, Ltd, Shijiazhuang, China

    Lihong Liu, Bing Xia & Lianqun Bao

  6. School of Life Sciences, University of Warwick, Coventry, UK

    Christophe Corre

  7. College of Life Sciences, Shanghai Normal University, Shanghai, China

    Yinhua Lu

  8. Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Xuekui Xia

  9. Shanghai Collaborative Innovation Center for Biomanufacturing Technology (SCIBT), Shanghai, China

    Lixin Zhang

  10. University of Chinese Academy of Sciences, Beijing, China

    Weishan Wang

Authors

  1. Bowen Yang
  2. Zilong Li
  3. Jingyu Zhang
  4. Shiwen Qiu
  5. Xueting Liu
  6. Zonglin Liang
  7. Hao Yan
  8. Yanyan Zhang
  9. Lihong Liu
  10. Bing Xia
  11. Lianqun Bao
  12. Defeng Li
  13. Shanshan Zhou
  14. Christophe Corre
  15. Chengyu Zhang
  16. Yinhua Lu
  17. Gao-Yi Tan
  18. Xuekui Xia
  19. Shanshan Li
  20. Lixin Zhang
  21. Weishan Wang

Contributions

W.W., Z. Li, S.L. and L.Z. conceptualized and supervised the project. B.Y. and S.Q. designed and performed the main experiments. B.X., L.L. and L.B. performed the large-scale fermentations and field trials. S.Q. and D.L. performed the molecular docking analysis. Y.L. conducted the response surface analysis. Z. Liang, C.Z. and Y.L. participated in the experiments. W.W., B.Y. and Z. Li wrote the manuscript. J.Z., S.Z., C.C., X.X. and L.Z. edited the manuscript.

Corresponding authors

Correspondence to Shanshan Li, Lixin Zhang or Weishan Wang.

Ethics declarations

Competing interests

W.W., B.Y. and Z.L. have filed provisional patents for this work to the China National Intellectual Property Administration (CN 202510569133.9 and CN 202510569134.3). The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Shuguang Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data

Extended Data Fig. 1 DNA alignment of identified binding sites of the promoters regulated by quorum sensing receptors.

The sequences were aligned using MUSCLE, the conserved palindromic sequences (identity > 70%) were highlighted in yellow, the completely conserved DNA bases were marked with asterisks.

Extended Data Fig. 2 Determination of interaction between Ptrig and receptors in vitro.

a, Purification of known quorum sensing signalling molecule receptors. The purity was confirmed by SDS-PAGE analysis. Lane 1: His6-ScbR (24.9 kDa), lane 2: His6-AvaR1 (26.9 kDa), lane 3: His6-SabR1 (25.5 kDa), lane 4: His6-MmfR (25.1 kDa). b, Purification of putative quorum sensing signaling molecule receptors. The purity of the putative receptors was also confirmed by SDS-PAGE analysis. Lane 1: His6-WP_067441803.1 (24.4 kDa), Lane 2: His6-WP_205368416.1 (26.7 kDa), lane 3: His6-WP_185944307.1 (26.2 kDa), lane 4: His6-WP_012999107.1 (24.3 kDa), lane 5: His6-WP_242709678.1 (26.9 kDa), lane 6: His6-WP_226048631.1 (23.5 kDa). c, d, e and f, Interaction between Ptrig and representative receptors determined by EMSA. KasO* intergenic region (97 bp) was used as negative control (lane 1). The concentration of both kasO* and Ptrig DNA fragments used was 1 pmol. Lanes 2 to 6 show the addition of increasing amounts of receptors (0, 0.8, 1.6, 2.4, 3.2 pmol respectively) to the protein–DNA complexes. The SDS-PAGE and the EMSA assay were performed three times (n = 3; replicates are shown in Source Data files).

Source data

Extended Data Fig. 3 Determination of Ptrig response to native quorum sensing systems.

a, Temporal profiles of fluorescence intensity and intracellular MMF1 quorum sensing signaling molecule concentration in S. coelicolor A3(2) Δ scbA. b, Temporal profiles of fluorescence in quorum sensing mutant strains Δ scbR and Δ scbA, respectively. Values are shown as mean ± s.d. from three (n = 3) independent biological replicates.

Source data

Extended Data Fig. 4 Interaction between Ptrig and representative receptors from cluster V to X.

The curves were fitted by the data determined by BLI. a, WP_067441803.1 in cluster V. b, WP_205368416.1 in cluster VI. c, WP_185944307.1 in cluster VII. d, WP_012999107.1 in cluster VIII. e, WP_242709678.1 in cluster IX. f, WP_226048631.1 in cluster X.

Source data

Extended Data Fig. 5 Characterization and optimization of repression systems.

a, Determination of leakage in four inducible systems by evaluating the fluorescence without inducers. b, Evaluation of the toggle effect of bistable genetic circuit. Medium with cumate or cellulose was refreshed every 18 h. c, Temporal variation of fluorescence without inducers in the strain harboring unoptimized bistable circuit. d, Schematic illustration of the tetramer design of the repressors. e, Schematic of genetic circuits used for evaluating the regulatory behavior of the optimized repressors. The output promoter activity was indicated by fluorescence of sfgfp reporter, while the expression of the optimized repressor was driven by the PTAC promoter which is induced by IPTG. The input promoter activity was measured by the same sfgfp gene, and the IPTG inducer was employed to induce the input promoter via deactivating to the LacI repressor. f, and g, Dose−response curves of the native dimer and artificial tetramer CymR*/CebR*. h, Evaluation of four variants of PcebR. i, Evaluation of the toggle effect of redesigned bistable gene circuit. Medium with cumate or cellulose was changed every 18 h. For a, b, c, f, g, h and i, values are shown as mean ± s.d. from three (n = 3) independent biological replicates.

Source data

Extended Data Fig. 6 Performance of stabilizer and amplifier module on transcriptional level.

a, Quantification of gene expression outputs from strains with or without packaged stabilizer module. The expression levels were measured by RT-qPCR. Relative expression values at 12 hours were normalized to a value of one. b, Characterization the amplification effect by comparing the relative transcriptional levels in the strains with and without amplifier module. c, Comparison of the GFP fluorescence intensity between S. venezuelae wild type (left) and engineering strain harboring sfgfp driven by PON mutants (right) using flow cytometry. d, Fluorescence activated cell sorting to obtain S. venezuelae with varying strength of PON mutants. Gating strategy was based on GFP fluorescence intensity. e, Correlation between transcriptional and translational profiles of 10 selected mutant promoters derived from PON. The transcriptional profile was evaluated by quantitative analysis of sfgfp driven by these promoters, while translational efficiency was evaluated based on the fluorescence intensity of sfGFP. For a and b, values are shown as mean ± s.d. from three (n = 3) independent biological replicates.

Source data

Extended Data Fig. 7 Comparison of native quorum sensing system and artificial control system SMARTS.

A quorum sensing signal transduction pathway in Streptomyces griseus was selected as a representative to elucidate the native regulatory mechanism. This signal transduction pathway can be divided into three stages: recognition, transduction and output. The native quorum sensing system was adapted for dynamic physiological regulation in Streptomyces species. In contrast, by integrating trigger, stabilizer and multiplexer module, the artificial control system SMARTS can response to diverse quorum sensing, and consequently convert the transient quorum sensing signals into stable, multiplexed outputs with adjustable strength in different Streptomyces strains. Furthermore, after responding to the quorum sensing signal, SMARTS is independently regulated and demonstrates complete orthogonality with the endogenous regulatory system.

Extended Data Fig. 8 Baiweimectin activity against soil parasitic nematodes.

a, Survival curve of nematodes treated with baiweimectin compared to commercially available fosthiazate. b, LC50 and LC90 values derived from the survival curve. For a, values are shown as mean ± s.d. from four (n = 4) independent biological replicates.

Source data

Extended Data Fig. 9 Central composite design of baiweimectin optimization targets.

a, aveR. b, avtAB. c, meiC. d, fadD. e, ecaA–bicA. f, gRNA targeting sucCD. Values are shown as mean ± s.d. from four (n = 3) independent biological replicates. The statistical analysis is based on two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 10 Optimization the production of epidoxorubicin.

a, Group I targets for enhancing epidoxorubicin production. Sources and the corresponding reactions of the targets were annotated. DOX, doxorubicin; Epi, epidoxorubicin; ε-RHO, ε-rhodomycinone; RHOD, rhodomycin D. b, Group II targets for enhancing epidoxorubicin production with annotated sources and corresponding enzymatic reactions. Ac-CoA, acetyl-CoA; M-CoA, malonyl-CoA bldD. c-i, Central composite design of epidoxorubicin optimization targets. c, dnrIN. d, avtE. e, dnrSQ. f, gRNA targeting dnrH. g, panK. h, acc. i, bldD. For c-i, values are shown as mean ± s.d. from four (n = 3) independent biological replicates. The statistical analysis is based on two-tailed unpaired Student’s t-test.

Source data

Supplementary information

Source data

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Yang, B., Li, Z., Zhang, J. et al. Scalable secondary metabolite production in Streptomyces using a plug-and-play system. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02762-1

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