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通过深度学习赋能的宏基因组挖掘探索核糖体肽在原核生物-噬菌体相互作用中的作用。

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.

机构信息

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.

出版信息

Microbiome. 2024 May 24;12(1):94. doi: 10.1186/s40168-024-01807-y.

DOI:10.1186/s40168-024-01807-y
PMID:38790030
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11118758/
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.

摘要

背景

微生物次生代谢产物在自然环境中的复杂相互作用中起着至关重要的作用。在这些代谢产物中,核糖体合成和翻译后修饰的肽(RiPPs)由于其结构多样性和功能多样性,成为治疗药物的有前途的来源。然而,它们的生物合成能力和生态功能在很大程度上仍未得到充分探索。

结果

在这里,我们旨在探索 RiPPs 的生物合成特征及其在海洋中微生物与病毒相互作用中的潜在作用,海洋包含了丰富的独特生物群落,这些生物群落富含相互作用,且在化学上仍未得到充分探索。我们首先开发了 TrRiPP 来鉴定海洋宏基因组中的 RiPPs,这是一种深度学习方法,可以在不依赖标志性基因的情况下检测 RiPP 前体,从而克服经典方法在处理高度碎片化宏基因组数据方面的局限性。将该方法应用于全球海洋微生物组的宏基因组,我们揭示了一系列以前未被表征的具有高度新颖性和多样性的假定 RiPP 家族。通过基于宏转录组数据的相关分析,我们观察到与抗噬菌体防御和噬菌体相关的蛋白质家族与 RiPP 家族的高共表达率。基于 RiPP 与噬菌体感染之间的这种假定关联,我们构建了海洋病毒数据库(OVD),并通过宿主预测和共表达分析构建了一个 RiPP 涉及的宿主-噬菌体相互作用网络,揭示了将 RiPP 编码原核生物、RiPP 家族、病毒蛋白家族和噬菌体联系在一起的复杂连接。这些发现强调了参与原核生物-噬菌体相互作用和共同进化的 RiPP 家族的潜力,为了解它们在海洋微生物组中的生态功能提供了深入的见解。

结论

本研究在全球范围内对海洋微生物组的 RiPP 生物合成潜力进行了系统研究,通过整合深度学习方法、宏转录组数据和宿主-噬菌体连接,深入了解 RiPP 在原核生物-噬菌体相互作用中的生态功能。这项研究为探索细菌次生代谢物的生态功能提供了一个有价值的范例,特别是它们与未被探索的微生物相互作用的关联。视频摘要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/3584063f6e1b/40168_2024_1807_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/11dd993c53d7/40168_2024_1807_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/fd11c6d37cbd/40168_2024_1807_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/981696caeac5/40168_2024_1807_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/017fc3d22c2a/40168_2024_1807_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/435454af2095/40168_2024_1807_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/3584063f6e1b/40168_2024_1807_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/11dd993c53d7/40168_2024_1807_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/fd11c6d37cbd/40168_2024_1807_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/981696caeac5/40168_2024_1807_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/017fc3d22c2a/40168_2024_1807_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/435454af2095/40168_2024_1807_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1754/11118758/3584063f6e1b/40168_2024_1807_Fig6_HTML.jpg

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