2025 AIChE Annual Meeting

(345a) Self-Resistance-Gene-Guided, High-Throughput Automated Genome Mining of Bioactive Natural Products from Streptomyces

Authors

Yujie Yuan - Presenter, Wuhan university
Chunshuai Huang, University of Illinois Urbana-Champaign
Nilmani Singh, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign
Guanhua Xun, University of Illinois At Urbana-Champaign
Huimin Zhao, University of Illinois-Urbana
Natural products (NPs) from bacteria, fungi, and plants are a vital source of drug leads, with Streptomyces species being particularly significant due to their capability of producing diverse bioactive compounds. Here, we present a fully automated, scalable, high-throughput platform for discovering bioactive NPs in Streptomyces (FAST-NPS). This platform integrates computational biosynthetic gene cluster (BGC) prediction and prioritization guided by self-resistance genes, automated cloning and heterologous expression, high-throughput fermentation, and product extraction. As a proof of concept, we cloned 105 BGCs (10–100 kb) from 11 Streptomyces strains with a 95% success rate. Heterologous expression in Streptomyces lividans TK24 led to the detection of 23 NPs, including 8 with antibacterial or antitumor bioactivities from 5 BGCs. This work highlights the potential of FAST-NPS to accelerate bioactive NP discovery for biomedical and biotechnological applications.