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Correlational networking guides the discovery of unclustered lanthipeptide protease-encoding genes

Author

Listed:
  • Dan Xue

    (University of South Carolina)

  • Ethan A. Older

    (University of South Carolina)

  • Zheng Zhong

    (The University of Hong Kong
    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou))

  • Zhuo Shang

    (University of South Carolina)

  • Nanzhu Chen

    (The University of Hong Kong
    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou))

  • Nolan Dittenhauser

    (University of South Carolina)

  • Lukuan Hou

    (University of South Carolina)

  • Peiyan Cai

    (The University of Hong Kong
    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou))

  • Michael D. Walla

    (University of South Carolina)

  • Shi-Hui Dong

    (State Key Laboratory of Applied Organic Chemistry, College of Chemistry and Chemical Engineering, Lanzhou University)

  • Xiaoyu Tang

    (Institute of Chemical Biology, Shenzhen Bay Laboratory)

  • Hexin Chen

    (University of South Carolina)

  • Prakash Nagarkatti

    (University of South Carolina)

  • Mitzi Nagarkatti

    (University of South Carolina)

  • Yong-Xin Li

    (The University of Hong Kong
    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou))

  • Jie Li

    (University of South Carolina)

Abstract

Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. We further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis.

Suggested Citation

  • Dan Xue & Ethan A. Older & Zheng Zhong & Zhuo Shang & Nanzhu Chen & Nolan Dittenhauser & Lukuan Hou & Peiyan Cai & Michael D. Walla & Shi-Hui Dong & Xiaoyu Tang & Hexin Chen & Prakash Nagarkatti & Mit, 2022. "Correlational networking guides the discovery of unclustered lanthipeptide protease-encoding genes," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29325-1
    DOI: 10.1038/s41467-022-29325-1
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    References listed on IDEAS

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    1. Manuel A. Ortega & Yue Hao & Qi Zhang & Mark C. Walker & Wilfred A. van der Donk & Satish K. Nair, 2015. "Structure and mechanism of the tRNA-dependent lantibiotic dehydratase NisB," Nature, Nature, vol. 517(7535), pages 509-512, January.
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    1. Zhuo Cheng & Bei-Bei He & Kangfan Lei & Ying Gao & Yuqi Shi & Zheng Zhong & Hongyan Liu & Runze Liu & Haili Zhang & Song Wu & Wenxuan Zhang & Xiaoyu Tang & Yong-Xin Li, 2024. "Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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