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Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.
Gao, Ying; Zhong, Zheng; Zhang, Dengwei; Zhang, Jian; Li, Yong-Xin.
Affiliation
  • Gao Y; CYM305, Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, 999077, China.
  • Zhong Z; CYM305, Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, 999077, China.
  • Zhang D; CYM305, Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, 999077, China.
  • Zhang J; CYM305, Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, 999077, China.
  • Li YX; CYM305, Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, 999077, China. yxpli@hku.hk.
Microbiome ; 12(1): 94, 2024 May 24.
Article in En | MEDLINE | ID: mdl-38790030
ABSTRACT

BACKGROUND:

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS:

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS:

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peptides / Ribosomes / Bacteria / Bacteriophages / Metagenome / Metagenomics / Deep Learning Language: En Journal: Microbiome Year: 2024 Document type: Article Affiliation country: China Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peptides / Ribosomes / Bacteria / Bacteriophages / Metagenome / Metagenomics / Deep Learning Language: En Journal: Microbiome Year: 2024 Document type: Article Affiliation country: China Country of publication: United kingdom