Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology

Abstract

Plasmids are extra-chromosomal genetic elements ubiquitous in bacteria, and commonly transmissible between host cells. Their genomes include variable repertoires of ‘accessory genes,’ such as antibiotic resistance genes, as well as ‘backbone’ loci which are largely conserved within plasmid families, and often involved in key plasmid-specific functions (e.g., replication, stable inheritance, mobility). Classifying plasmids into different types according to their phylogenetic relatedness provides insight into the epidemiology of plasmid-mediated antibiotic resistance. Current typing schemes exploit backbone loci associated with replication (replicon typing), or plasmid mobility (MOB typing). Conventional PCR-based methods for plasmid typing remain widely used. With the emergence of whole-genome sequencing (WGS), large datasets can be analyzed using in silico plasmid typing methods. However, short reads from popular high-throughput sequencers can be challenging to assemble, so complete plasmid sequences may not be accurately reconstructed. Therefore, localizing resistance genes to specific plasmids may be difficult, limiting epidemiological insight. Long-read sequencing will become increasingly popular as costs decline, especially when resolving accurate plasmid structures is the primary goal. This review discusses the application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology; novel in silico plasmid analysis tools are highlighted. Due to the diverse and plastic nature of plasmid genomes, current typing schemes do not classify all plasmids, and identifying conserved, phylogenetically concordant genes for subtyping and phylogenetics is challenging. Analyzing plasmids as nodes in a network that represents gene-sharing relationships between plasmids provides a complementary way to assess plasmid diversity, and allows inferences about horizontal gene transfer to be made.

Plasmid evolution in carbapenemase‐producing Enterobacteriaceae: a review

“…Unlike the PBRT, which detects plasmids at higher resolution, the MOB typing uses lower resolution to classify plasmids . However, PBRT and DPMT have been combined to successfully classify plasmids in clinically relevant pathogens …”

A megaplasmid family responsible for dissemination of multidrug resistance inPseudomonas

“…Whole genome sequencing can be a powerful epidemiological tool in diagnostic and public health microbiology (12,13) , but it has limitations that have restricted our understanding of the role of MGE. The use of short-read data from the most popular high-throughput platforms makes the assembly of plasmids, which often contain repetitive sequences, difficult (14) . Long read sequencing, such as PacBio or Oxford Nanopore, can resolve the status of plasmids, allowing comparative analysis that can elucidate the routes by which AMR genes spread between lineages (15) .…”

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein-sequence-based replicon distribution scores

“…Such analysis can be performed by plasmid DNA isolation followed by sequencing [9] . However, due to decreased sequencing costs, nowadays it is affordable and often easier to sequence the entire genome of bacterial organisms by next-generation whole-genome shotgun sequencing [10] . Furthermore, this approach allows the re-analysis of already sequenced genomes to identify plasmids which have not been detected before.…”