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Identification of gut microbial bile acid metabolic enzymes via an AI-assisted pipeline

Yong Ding,Xi Luo,Jiasheng Guo,Baiying Xing,Haoyu Lin,Haohan Ma,Yicun Wang,Meng Li,Chuan Ye,Sen Yan,Kangjie Lin,Jinxin Zhang,Yingying Zhuo,Qixing Nie,Donghui Yang,Zhipeng Zhang,Yanli Pang,Kai Wang,Ming Ma,Luhua Lai,Changtao Jiang

The modifications of bile acids (BAs) are fundamental to their role in host physiology and pathology. Identifying their synthetases is crucial for uncovering the diversity of BAs and developing targeted interventions, yet it remains a significant challenge. To address this hurdle, we developed an artificial intelligence (AI)-assisted workflow, bile acid enzyme announcer unit tool (BEAUT), which predicted over 600,000 candidate BA metabolic enzymes that we compiled into the human generalized microbial BA metabolic enzyme (HGBME) database ( https://beaut.bjmu.edu.cn ). We identified a series of uncharacterized BA enzymes, including monoacid acylated BA hydrolase (MABH) and 3-acetoDCA synthetase (ADS). Notably, ADS can produce an unreported skeleton BA, 3-acetoDCA, with a carbon-carbon bond extension. After determining its bacterial source and catalytic mechanism, we found that 3-acetoDCA is widely distributed among populations and regulates the microbial interactions in the gut. In conclusion, our work offers alternative insights into the relationship between microbial BAs and the host from an enzymatic perspective.