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Fine-Scale Reconstruction of the Evolution of FII-33 Multidrug Resistance Plasmids Enables High-Resolution Genomic Surveillance.

ABSTRACT: We examined 185 complete, publicly available FII-33 plasmid sequences, characterizing their backbone and various insertions. The variable characteristic insertions facilitated evolutionary reconstruction for this plasmid group, beginning with the acquisition of a primary resistance region (PRR) over 10 years ago. FII-33 plasmids have evolved by acquiring additional resistance genes in the PRR via translocatable elements and by forming cointegrates with plasmids of other types. In all cases, IS26 is suspected to have mediated cointegration. Plasmid cointegration has contributed to the accumulation of resistance genes and may have increased the transmissibility, stability, and host range of the original FII-33 lineage. A particularly important sublineage was formed by a replicative IS26 cointegration event that fused an FII-33 plasmid with a bla_KPC-2-containing R-type plasmid, interrupting the FII-33 traI gene encoding the conjugative relaxase. The FII-33:R cointegrate arose in the Klebsiella pneumoniae ST11 clone and remains largely confined there due to the abolition of transfer ability by the FII-33:R cointegration event. However, in some cases FII-33:R cointegrates have fused with additional plasmids and acquired complete transfer regions or oriT sequences that might restore their ability to transfer horizontally. Cointegration events across FII-33 plasmid sublineages have involved plasmids of at least 15 different types. This suggests that plasmid cointegration occurs readily and is more common than previously appreciated, raising questions about the effects of cointegrate formation on plasmid host range, stability, and capacity for horizontal transfer. Resources are provided for detecting and characterizing FII-33 plasmid sublineages from complete or draft genome sequences. IMPORTANCE Effective genomic surveillance of antibiotic-resistant bacterial pathogens must consider plasmids, which are frequently implicated in the accumulation and transfer of resistance genes between bacterial strains or species. However, the evolution of plasmids is complex, and simple typing or comparison tools cannot accurately determine whether plasmids belong to the same sublineages. This precludes precise tracking of plasmid movement in bacterial populations. We have examined the FII-33 group, which has been associated with multidrug resistance and particularly carbapenem resistance in clinically significant members of the Enterobacterales in China. Our analysis has provided insight into the evolution of this important plasmid group, allowing us to develop resources for rapidly typing them to the sublineage level in complete or draft genome sequences. Our approach will improve detection and characterization of FII-33 plasmids and facilitate surveillance within and outside China. The approach can serve as a model for similar studies of other plasmid types.

SUBMITTER: Hu Y

PROVIDER: S-EPMC8765060 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set's sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source and is available at https://www.github.com/McMahonLab/TaxAssIMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets.

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Fine-Scale Reconstruction of the Evolution of FII-33 Multidrug Resistance Plasmids Enables High-Resolution Genomic Surveillance.

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