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Deep genome mining boosts the discovery of microbial terpenoids
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Ru LEI, Hui TAO, Tiangang LIU
Synthetic Biology Journal | 2024, 5(3) : 507 - 526
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Synthetic Biology Journal | 2024, 5(3): 507-526
Invited Review
Deep genome mining boosts the discovery of microbial terpenoids
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Ru LEI, Hui TAO, Tiangang LIU
Affiliations
  • Key Laboratory of Combinatorial Biosynthesis and Drug Discovery,Ministry of Education,School of Pharmaceutical Sciences,Wuhan University,Wuhan 430071,Hubei,China
Published: 2024-06-30 doi: 10.12211/2096-8280.2023-098
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The natural products terpenoids are widely distributed in animals (marine invertebrates), plants, microorganisms, with diverse molecular structures for bioactivities. A large number of terpenoids have been extracted directly from plants and microorganisms. However, traditional methods based on natural screening face challenges in discovering new terpenes due to the increasing number of known compounds at large quantities. The advent of next-generation sequencing and synthetic biology technologies marks the onset of the era of genome mining-driven natural product discovery, particularly in the exploration of new terpenoids. However, challenges persist in this regard, such as low efficiencies, interference of known compounds, and limited data throughput. In this review, we focus on recent advances in terpenoid discovery via microbial genome mining strategies, including the use of the precursor supplying microbial chassis (Escherichia coli, Saccharomyces cerevisiae, Aspergillus oryzae, Streptomyces albus, etc.), the microbial resources from extreme geographical environments, deep genome mining, and terpene mining platforms driven by artificial intelligence and automation techniques. To produce more terpenoids using heterologous hosts, multiple microbial chassis with enhanced precursor supply have been developed to improve their production yields and thus facilitate the discovery of structurally unique terpenoids. With the growing understanding of terpene biosynthesis machinery, the deep mining of terpenoid biosynthetic gene clusters and terpene synthases can effectively address issues related to repeated and irrelevant discoveries. Furthermore, the integration of artificial intelligence and automation platform with synthetic biology has ushered in the high-throughput intelligent discovery of terpenoids, which significantly improves the research and enables the discovery of numerous terpenoids with new structures. Finally, we address challenges and future directions for genome mining based terpenoid discovery. Driven by synthetic biology and artificial intelligence, a new chapter for the discovery of terpenoids and other natural products will open. We are looking forward to seeing more terpenoids to be developed as drugs and valuable chemicals in the future.

terpenoid natural products  /  terpene synthases  /  microbial chassis  /  genome mining  /  artificial intelligence  /  automated high-throughput platform
Ru LEI, Hui TAO, Tiangang LIU. Deep genome mining boosts the discovery of microbial terpenoids[J]. Synthetic Biology Journal, 2024 , 5 (3) : 507 -526 . DOI: 10.12211/2096-8280.2023-098
Year 2024 volume 5 Issue 3
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doi: 10.12211/2096-8280.2023-098
  • Receive Date:2023-12-01
  • Online Date:2025-07-07
  • Published:2024-06-30
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  • Received:2023-12-01
  • Revised:2024-02-22
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    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery,Ministry of Education,School of Pharmaceutical Sciences,Wuhan University,Wuhan 430071,Hubei,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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