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<p>Plasmids are extrachromosomal mobile genetic elements whose copy numbers (PCNs) critically influence microbial evolution, antibiotic resistance and pathogenicity. Despite their importance and immense diversity, the ecological, evolutionary and molecular factors determining PCN remain poorly understood. Here, we present a theoretical model to explain the empirical power-law relationship between plasmid size and copy number, one of the fundamental quantitative principles governing PCN control. However, this relationship alone has limited predictive power. To improve PCN prediction, we introduce a data-driven approach incorporating diverse features. Trained and tested on 11,051 plasmids, our machine learning model achieves significantly enhanced accuracy, with plasmid-encoded protein domains emerging as key predictors. Applying this framework, we conduct a large-scale analysis of PCN distributions across hundreds of thousands of metagenomic plasmids (IMG/PR database) and tens of thousands of clinical isolates, revealing putative niche specific taxonomic PCN hotspots and hypothesis-generating ecological trends. These results provide valuable insights into plasmid ecology, antibiotic resistance genes (ARGs) surveillance and shed lights on the gut plasmidome, a “dark matter” in human microbiome.</p>

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Integrating theory and machine learning to reveal determinants of plasmid copy number

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

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  • Iqra Shahzadi,
  • Wenzhi Xue,
  • Hasan Ubaid Ullah,
  • Rohan Maddamsetti,
  • Lingchong You,
  • Teng Wang

摘要

Plasmids are extrachromosomal mobile genetic elements whose copy numbers (PCNs) critically influence microbial evolution, antibiotic resistance and pathogenicity. Despite their importance and immense diversity, the ecological, evolutionary and molecular factors determining PCN remain poorly understood. Here, we present a theoretical model to explain the empirical power-law relationship between plasmid size and copy number, one of the fundamental quantitative principles governing PCN control. However, this relationship alone has limited predictive power. To improve PCN prediction, we introduce a data-driven approach incorporating diverse features. Trained and tested on 11,051 plasmids, our machine learning model achieves significantly enhanced accuracy, with plasmid-encoded protein domains emerging as key predictors. Applying this framework, we conduct a large-scale analysis of PCN distributions across hundreds of thousands of metagenomic plasmids (IMG/PR database) and tens of thousands of clinical isolates, revealing putative niche specific taxonomic PCN hotspots and hypothesis-generating ecological trends. These results provide valuable insights into plasmid ecology, antibiotic resistance genes (ARGs) surveillance and shed lights on the gut plasmidome, a “dark matter” in human microbiome.