Research Progress in Area 4 方向 ( 四 ) 課題進展 179 Abstract The emergence of multi-antibiotic resistant bacteria known as “superbugs” is an increasingly severe threat to global health and thus novel antibiotics killing superbugs are urgently needed. Prof. Li’s team aims to establish a big data multiomics guided discovery approach for targeted discovery of natural productswithchemical novelty and biological importance. They plan to achieve this goal through the combination of big data genome mining, metabolomics, synthetic biology, and chemical biology. Applying their approaches to the uncultured but dominant ocean microbe, they want to harness the chemical potential of the microbiome to enhance the reservoir of potentially therapeutic small molecules. Research Activities and Progress • Develop a new efficiency omics-guided discovery platform via integrating genomics, transcriptomics, proteomics, and metabolomics to mine ocean microbiome's medicinal potential; • Prioritize candidate BGCs upon sophisticated genome mining and then apply synthetic biology strategies to convert the untapped genetic potential into chemical reality; • Mine the metagenomic data of marine microbiome in large-scale especially the uncultured but dominant microbes, for the targeted bioactive small molecules. Key Findings • Developed a genome mining workflow and used correlation analysis complemented by coexpression analysis to establish the first global correlation network between lanthipeptide precursor peptides and proteases (Fig. 1); • The identified and prioritized BGCs lead to the discovery of novel lanthipeptides paenithopeptins and bacinapeptins, and responsible cryptic proteases; • Developed a deep learning aided genome mining method to mine for RiPPs from ocean microbiome (Fig. 2), which is potentially involved in bacteria-phage interaction in the ocean. Research Output Publication 0 Trained personnel 2 Multi-omics-guided Discovery of New Antibiotics from Dominant but Uncultured Bacteria in Marine Microbiota Dr. Yongxin Li The University of Hong Kong Fig.1 Overview of precursor protease correlation network for lanthipeptides.
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