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Limited information is available on whether bla  KPC -containing plasmids from isolates in a hospital outbreak can be differentiated from epidemiologically unrelated bla  KPC -containing plasmids based on sequence data. This study aimed to evaluate the performance of three approaches to distinguish epidemiologically related from unrelated bla  KPC -containing pKpQiL-like IncFII(k2)-IncFIB(pQiL) plasmids. Epidemiologically related isolates were subjected to short- and long-read whole-genome sequencing. A hybrid assembly was performed, and plasmid sequences were extracted from the assembly graph. Epidemiologically unrelated plasmid sequences were extracted from GenBank. Pairwise comparisons of epidemiologically related and unrelated plasmids based on SNPs using snippy and of phylogenetic distance using Roary and using a similarity index that penalizes size differences between plasmids (Stoesser index) were performed. The percentage of pairwise comparisons misclassified as genetically related or as clonally unrelated was determined using different genetic thresholds for genetic relatedness. The ranges of number of SNPs, Roary phylogenetic distance, and Stoesser index overlapped between the epidemiologically related and unrelated plasmids. When a genetic similarity threshold that classified 100% of epidemiologically related plasmid pairs as genetically related was used, the percentages of plasmids misclassified as epidemiologically related ranged from 6.7% (Roary) to 20.8% (Stoesser index). Although epidemiologically related plasmids can be distinguished from unrelated plasmids based on genetic differences, bla  KPC -containing pKpQiL-like IncFII(k2)-IncFIB(pQiL) plasmids show a high degree of sequence similarity. The phylogenetic distance as determined using Roary showed the highest degree of discriminatory power between the epidemiologically related and unrelated plasmids.

antimicrobial agents and chemotherapy

Distinguishing bla KPC Gene-Containing IncF Plasmids from Epidemiologically Related and Unrelated Enterobacteriaceae Based on Short- and Long-Read Sequence Data