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<p>Standard pan-genome pipelines, such as Roary, use strict protein identity thresholds (e.g., ≥ 95%) that systematically misclassify highly conserved genes as absent. Single mutational events like large indels, nonsense mutations, or high sequence divergence can cause these false negatives, masking the true composition of the bacterial core genome. We address this by investigating 198 extended-core loci (present in &gt; 95% of strains) from 44 <i>Escherichia coli</i> genomes that were incorrectly flagged as absent. Using a synteny-guided pipeline to validate a representative subset of 50 genes, we determined that most apparent absences are not true deletions but distinct evolutionary outcomes: inactivating pseudogenization (e.g., <i>rlmF</i>), structural remodeling via in-frame indels (e.g., <i>yhfR</i>), and highly divergent orthologs falling below identity cutoffs. By distinguishing genuine gene loss from sequence variation, our framework provides a more accurate view of conserved gene content, enabling precise genotype–phenotype associations and revealing hidden reservoirs of genetic diversity.</p>

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Revealing the spectrum of extended-core gene variation in the Escherichia coli pan-genome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Kritika Chugh,
  • Zhenyu Xuan

摘要

Standard pan-genome pipelines, such as Roary, use strict protein identity thresholds (e.g., ≥ 95%) that systematically misclassify highly conserved genes as absent. Single mutational events like large indels, nonsense mutations, or high sequence divergence can cause these false negatives, masking the true composition of the bacterial core genome. We address this by investigating 198 extended-core loci (present in > 95% of strains) from 44 Escherichia coli genomes that were incorrectly flagged as absent. Using a synteny-guided pipeline to validate a representative subset of 50 genes, we determined that most apparent absences are not true deletions but distinct evolutionary outcomes: inactivating pseudogenization (e.g., rlmF), structural remodeling via in-frame indels (e.g., yhfR), and highly divergent orthologs falling below identity cutoffs. By distinguishing genuine gene loss from sequence variation, our framework provides a more accurate view of conserved gene content, enabling precise genotype–phenotype associations and revealing hidden reservoirs of genetic diversity.