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Elucidation of genes enhancing natural product biosynthesis through co-evolution analysis

Abstract

Streptomyces has the largest repertoire of natural product biosynthetic gene clusters (BGCs), yet developing a universal engineering strategy for each Streptomyces species is challenging. Given that some Streptomyces species have larger BGC repertoires than others, we proposed that a set of genes co-evolved with BGCs to support biosynthetic proficiency must exist in those strains, and that their identification may provide universal strategies to improve the productivity of other strains. We show here that genes co-evolved with natural product BGCs in Streptomyces can be identified by phylogenomics analysis. Among the 597 genes that co-evolved with polyketide BGCs, 11 genes in the ‘coenzyme’ category have been examined, including a gene cluster encoding for the cofactor pyrroloquinoline quinone. When the pqq gene cluster was engineered into 11 Streptomyces strains, it enhanced production of 16,385 metabolites, including 36 known natural products with up to 40-fold improvement and several activated silent gene clusters. This study provides an innovative engineering strategy for improving polyketide production and finding previously unidentified BGCs.

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Fig. 1: Phylogenomic analysis of the Streptomyces genus and the number of PKS gene clusters.
Fig. 2: Exploration and evaluation of unique genes in PKS rich clade.
Fig. 3: PQQ increases actinorhodin production by enhancing cofactor biosynthesis and modulating intracellular TAG level.
Fig. 4: Introduction of the pqq gene cluster results in a positive impact on the metabolome of 11 Streptomyces strains.
Fig. 5: Introduction of pqq gene cluster resulted in increased production of known natural products.
Fig. 6: Natural products activated by introduction of the pqq BGC.

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Data availability

The transcriptomic data of S. griseus and S. rapamycinicus are available at NCBI GEO database (GSE256209). The mass spectrometry proteomics data are available at the ProteomeXchange Consortium via the iProX repository (PXD049454). Other data supporting the findings of this study are included in the published article and Supplementary Information. Requests for any additional information can be made to the corresponding authors. Source data are provided with this paper.

Code availability

The code of pan-genomic analysis was described in the Methods section of the paper. All the code is openly available in GitHub.

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Acknowledgements

We acknowledge the financial support provided by National Key R&D Program of China (to X.L., grant no. 2018YFA0903200), National Natural Science Foundation of China (to X.L., grant no. 32071421 and to H.D., grant no. 32201203), Shenzhen Science and Technology Program (to X.L., grant nos. ZDSYS20210623091810032 and RCYX20200714114736026), China Postdoctoral Science Foundation (to X.W., grant no. 31800023), Shenzhen Medical Research Fund (to X.W., grant no. D2301005), Shenzhen Science and Technology Program (to X.W., grant no. JCYJ20220531100207017), Shenzhen Bay Scholar Fellowship (to X.L. and X.T., grant no. 229100002), Novo Nordisk Foundation (to P.C.-M., grant no. CFB 2.0, NNF20CC0035580), which made this research possible. We thank J. Nikodinovic-Runic for kindly providing the strain Streptomyces sp. NP10, G. Liu, F. Ni and L. Zhou for help in industrial strains experiments, M. Wu for technique support on proteomics analysis, H. He and G. Zhang for project discussion and Z. Wei for meeting organization for this project.

Author information

Authors and Affiliations

Authors

Contributions

X.W., P.C.-M., X.T., J.D.K. and X.L. conceived and supervised the project. X.W. and N.C. designed and performed the main experiments. P.C.-M. and S.A. performed the bioinformatics analysis. Y.S., L.B., J.W., Y.X. and X.F. designed and participated in the industry strain experiments. X.T. and Y.L. designed and participated in the bioassay test. B.Z. participated in the metabolomics analysis. Y.Z. participated in RNA sequencing data analysis. Z.L. and H.D. participated in fermentation data analysis. X.W., P.C.-M., J.D.K. and X.L. wrote and revised the paper.

Corresponding authors

Correspondence to Xiaoyu Tang, Jay D. Keasling or Xiaozhou Luo.

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Competing interests

J.D.K. has financial interests in Amyris, Ansa Biotechnologies, Apertor Pharma, Berkeley Yeast, Cyklos Materials, Demetrix, Lygos, Napigen, ResVita Bio and Zero Acre Farms. X.L. has financial interests in Demetrix and Synceres. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Kenji Arakawa, Hyun Uk Kim and Lixin Zhang for their contribution to the peer review of this work. Primary Handling Editor: Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team.

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Supplementary information

Supplementary Information

Supplementary Materials and Methods, Tables 1–14 and Figs. 1–18.

Reporting Summary

Supplementary Data 1–7

1. Information of selected 720 actinobacterial genomes. 2. 604 orthologous proteins shared by 201 Streptomyces. 3. Information of the 597 unique proteins. 4. Presence or absence of each unique gene in the 201 genomes. Correlation of each unique gene with PKS proficiency. 5. Upregulated proteins after pqq operon introduction in S. coelicolor. 6. Change of various precursors after pqq operon introduction in S. coelicolor. 7. Change of all metabolites after pqq operon introduction in different Streptomyces.

Source data

Source Data Figs. 1–6

Statistical source data.

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Wang, X., Chen, N., Cruz-Morales, P. et al. Elucidation of genes enhancing natural product biosynthesis through co-evolution analysis. Nat Metab 6, 933–946 (2024). https://doi.org/10.1038/s42255-024-01024-9

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