Toyota Technological Institute at Chicago, Chicago, IL, USA.
Department of Medicine, University of Chicago, Chicago, IL, USA.
Nat Microbiol. 2024 Mar;9(3):830-847. doi: 10.1038/s41564-024-01610-3. Epub 2024 Mar 4.
Plasmids alter microbial evolution and lifestyles by mobilizing genes that often confer fitness in changing environments across clades. Yet our ecological and evolutionary understanding of naturally occurring plasmids is far from complete. Here we developed a machine-learning model, PlasX, which identified 68,350 non-redundant plasmids across human gut metagenomes and organized them into 1,169 evolutionarily cohesive 'plasmid systems' using our sequence containment-aware network-partitioning algorithm, MobMess. Individual plasmids were often country specific, yet most plasmid systems spanned across geographically distinct human populations. Cargo genes in plasmid systems included well-known determinants of fitness, such as antibiotic resistance, but also many others including enzymes involved in the biosynthesis of essential nutrients and modification of transfer RNAs, revealing a wide repertoire of likely fitness determinants in complex environments. Our study introduces computational tools to recognize and organize plasmids, and uncovers the ecological and evolutionary patterns of diverse plasmids in naturally occurring habitats through plasmid systems.
质粒通过移动基因来改变微生物的进化和生活方式，这些基因通常在跨进化枝的环境变化中赋予适应性。然而，我们对自然存在的质粒的生态和进化理解还远远不够。在这里，我们开发了一种机器学习模型 PlasX，该模型在人类肠道宏基因组中识别了 68350 个非冗余质粒，并使用我们的序列包含感知网络分区算法 MobMess 将它们组织成 1169 个进化上有凝聚力的“质粒系统”。单个质粒通常是特定于国家的，但大多数质粒系统跨越了地理位置不同的人类群体。质粒系统中的携带基因包括众所周知的适应性决定因素，如抗生素耐药性，但也包括许多其他因素，如参与必需营养素生物合成和转移 RNA 修饰的酶，揭示了在复杂环境中可能存在的适应性决定因素的广泛组合。我们的研究通过质粒系统介绍了识别和组织质粒的计算工具，并揭示了自然发生的栖息地中多样化质粒的生态和进化模式。
