Plasmid genomes generally include a ‘backbone’ of core genetic loci, which are somewhat conserved amongst broadly related plasmids of the same family (Phan et al., 2009), and associated with key plasmid-specific functions such as replication and mobility. Accessory genes may also be present, and often confer clinically relevant traits such as virulence and antibiotic resistance (Thomas and Summers, 2008). Plasmids can act as efficient vectors of horizontal gene transfer (HGT). Notably, during conjugation, a plasmid promotes its own transfer (and/or that of a co-resident plasmid) from one bacterial cell to another (Norman et al., 2009). Accessory genes are therefore frequently spread by virtue of being located on transmissible plasmids; moreover, they are commonly associated with smaller mobile elements such as transposons, facilitating intracellular mobilization amongst plasmids, or to the chromosome (Stokes and Gillings, 2011). Due to their ability to transmit genes encoding adaptive traits across bacterial populations, plasmids can enable bacteria to evolve rapidly under environmental pressure (Heuer and Smalla, 2012). A striking example of bacterial adaptive evolution is that of antibiotic resistance, which is driven, in part, by dissemination of resistance plasmids (plasmids conferring antibiotic resistance), and now threatens modern medicine (Carattoli, 2013; World Health Organization, 2014).

Classifying plasmids according to a typing scheme provides useful insights into the epidemiology of plasmid-mediated antibiotic resistance: for example, studying the composition of plasmid types can indicate whether an antibiotic resistance epidemic is driven by diverse plasmids or one dominant plasmid type (Valverde et al., 2009). In addition, hypotheses about resistance transmission during outbreaks can be refined according to the relatedness of resistance plasmids harbored by clinical strains (Pecora et al., 2015). The principal plasmid classification schemes are replicon and MOB typing, based on backbone loci encoding plasmid replication and mobility functions, respectively (Carattoli et al., 2005; Garcillán-Barcia et al., 2009). Whilst these single-locus typing schemes have been widely and successfully applied, they provide limited resolution (Fricke et al., 2009), restricting epidemiological inference: in an outbreak context, if two patients are infected by unrelated strains harboring resistance plasmids of the same type, this raises the possibility of plasmid transmission, but plasmid transmission cannot be conclusively ruled-in using single-locus plasmid typing alone; further higher-resolution investigation would be required (Foxman et al., 2005). If resistance plasmids are unrelated, plasmid transmission can be ruled-out, though a transmission link via resistance gene transposition is possible.

Plasmid typing may provide a stepping-stone to higher resolution analyses; identifying shared (‘core’) genes amongst related plasmids can inform development of plasmid multi-locus sequence typing (pMLST) schemes (García-Fernández et al., 2011), or allow phylogenetic relationships to be reconstructed based on core gene single nucleotide polymorphisms (SNPs) (de Been et al., 2014). Unfortunately, determining high-resolution plasmid relationships is challenging: the tendency of plasmids to gain, lose and rearrange genetic content means sets of plasmids – even if of the same type – will tend to share few phylogenetically concordant core genes (Fondi et al., 2010; Tazzyman and Bonhoeffer, 2014), impeding subtyping and phylogenetic analysis (Maiden, 2006). Even backbone genes may not be well conserved across all plasmids of the same type (Lanza et al., 2014), and sometimes show mosaic phylogenetic origins (Sen et al., 2013).

Whole-genome sequencing (WGS) data can now be obtained for many bacterial isolates, at relatively low cost, within short timescales (Metzker, 2010). Whilst sequencing reads from a bacterial isolate represent plasmid(s) as well as the chromosome, WGS-based studies have often focused on the host strain chromosome as the unit of interest (Croucher and Didelot, 2015). For strain-level clinical surveillance to elucidate antibiotic resistance transmission routes, dissemination should primarily involve clonal transmission of particular antibiotic-resistant strains. However, recent analyses indicate that plasmids may transmit between strains frequently, even over short timescales (Conlan et al., 2014; Sheppard et al., 2016). Therefore, the chain of transmission no longer simply corresponds to strain transmission; resistance plasmid dissemination across strains recruits different recipient strains into the outbreak too, resulting in a ‘plasmid outbreak.’ Although insight may be limited by difficulties in determining high-resolution plasmid relationships, these dynamics mean that plasmid analysis across a variety of strains is important, including for short-term surveillance studies (Adler and Carmeli, 2011).

Conventional PCR-based plasmid typing methods are commonly used, but in silico approaches for classifying sequenced plasmids are also available. WGS datasets from short-read sequencing projects offer exciting opportunities for large-scale plasmid analysis, while presenting the additional challenge of assembling reads to resolve individual plasmid structures. After summarizing current plasmid classification schemes (replicon and MOB typing), this review discusses the opportunities and challenges of conducting in silico plasmid typing on WGS datasets to gain insight into plasmid-mediated resistance epidemiology. We highlight novel tools for WGS-based plasmid analysis, and examine gene-sharing networks as a complementary approach for analyzing plasmid relationships. This review focuses on WGS datasets from cultured rather than metagenomic samples; for the latter, see recent reviews (Jørgensen et al., 2014; Martínez et al., 2016).

Replicon typing schemes exploit genetic elements of the replicon region (encoding replication machinery) (Table 1). Couturier et al. (1988) typed plasmids according to Southern blot hybridization, using replicons from plasmids of different incompatibility groups as probes. However, this method is limited by probe cross-hybridization amongst closely related replicon sequences (Carattoli, 2009). PCR-based replicon typing (PBRT) – where plasmids are typed according to PCRs targeting various replicon sequences – is less laborious, and shows higher specificity in detecting replicons (Carattoli et al., 2005). For gram-negative bacteria, PBRT schemes targeting replicons found in Enterobacteriaceae and Acinetobacter baumannii plasmids are available (Carattoli et al., 2005; Bertini et al., 2010). A PBRT scheme for plasmids of gram-positive bacteria has been developed, focusing on enterococcal (Jensen et al., 2010) and staphylococcal (Lozano et al., 2012) plasmids. For common Enterobacteriaceae replicon types, pMLST schemes have been devised for subtyping (Brolund and Sandegren, 2016; Hancock et al., 2016). Availability of WGS data has motivated the development of in silico replicon typing and subtyping tools, which have been validated for Enterobacteriaceae plasmids (Carattoli et al., 2014). For plasmids from taxa not represented by existing in silico tools, ad hoc in silico methods have been derived from PBRT schemes (Shintani et al., 2015; Brodrick et al., 2016).

MOB typing exploits the conserved N-terminal sequence of the relaxase proteins encoded by transmissible plasmids (Francia et al., 2004; Garcillán-Barcia et al., 2009). As with replicon typing, both PCR-based and in silico approaches are used for MOB typing (Table 1). Compared with replicon typing, MOB typing classifies plasmids at lower resolution (Garcillán-Barcia et al., 2011).

A drawback of replicon typing is that individual plasmids can contain multiple replicons, complicating classification, whereas usually just one relaxase is encoded. However, due to its finer resolution, replicon typing provides more detailed information on plasmid relatedness, particularly if a pMLST subtyping scheme is available (Garcillán-Barcia and de la Cruz, 2013). Even within relatively well-studied taxa, neither scheme classifies all plasmids, likely reflecting diversity in plasmid backbones. Shintani et al. (2015) assessed in silico typing, and found that the proportion of Enterobacteriaceae plasmids that could be replicon typed was 75%; for Acinetobacter plasmids the proportion was 67%. Only around half of plasmids from major gram-positive taxa could be replicon typed (51% Firmicutes plasmids, 49% Actinobacteria plasmids), although the proportion was higher for enterococcal and staphylococcal plasmids (83 and 85% respectively) (Supplementary Table S1 in Shintani et al., 2015). Lanza et al. (2015) also highlight gaps in replicon typing of Firmicutes plasmids. MOB typing only types transmissible plasmids (∼50% γ-Proteobacterial plasmids; ∼35% Firmicutes plasmids) (Smillie et al., 2010).

When analyzing whole genomic DNA, limited information can be derived from plasmid typing alone: bacterial cells may contain multiple different plasmids, and a single plasmid may contain multiple replicons, obscuring correspondence between detected replicons and the set of plasmid types within a host cell (Johnson et al., 2007). Therefore, in PCR-based studies – where genomic context of an amplicon remains unknown – plasmids are commonly isolated first, before being individually characterized by replicon and resistance typing (see Table 11 in EFSA, 2011). This is time-consuming, restricts the number of isolates that can be analyzed, and has inherent limitations: if plasmids from the same isolate are of similar size they cannot be separated by pulsed-field gel electrophoresis, and isolation by transfer to recipient cells is not always achieved (Dib et al., 2015).

Potentially, WGS enables in silico analyses in which plasmid typing and analysis of loci of interest, such as resistance genes (and their plasmid or chromosomal genetic context), are performed in a unified way. Consequently, much larger isolate collections can be analyzed – a key advantage given that plasmid studies have indicated a need for including more strains in analyses (e.g., environmental strains, or isolates exhibiting only low-level resistance) to uncover more complex transmission routes (Carrër et al., 2010; Stoesser et al., 2014, 2015a). Furthermore, a thorough mechanistic understanding of clinically important aspects of plasmid biology, such as host range and phenotypic effects, may require analysis of the wider plasmid genome rather than specific loci.

Unfortunately, there are significant obstacles to in silico analysis of WGS data. Popular high-throughput sequencing technologies (e.g., Illumina) produce short (∼100–300 bp) reads, for which assembly is inherently challenging (Nagarajan and Pop, 2013). Isolating individual plasmids prior to sequencing simplifies assembly, potentially enabling complete plasmid reconstruction (Mathers et al., 2015), but is laborious. Long-read sequencing vastly simplifies assembly, but high costs restrict its use (Koren and Phillippy, 2015). Therefore, a major challenge lies in extracting useful information from short sequencing reads derived from different sources (different co-resident plasmids, the chromosome).

Current plasmid typing schemes exploit a relatively small number of loci thought to best reflect the vertical (tree-like) component of plasmid evolution. However, plasmid genomes tend not to conform to tree-like evolution: co-integration events and genetic exchanges amongst plasmids mean that different parts of a plasmid genome may have different evolutionary origins (Bapteste et al., 2009). A complementary way to assess plasmid diversity involves ordering plasmids into dendrograms (de Been et al., 2014) or networks according to their gene content similarity, irrespective of whether gene-sharing stems from vertical inheritance or horizontal acquisition. In a gene-sharing network, plasmids are nodes that are linked to other plasmids if they share genes at a given sequence identity threshold (Brilli et al., 2008; Corel et al., 2015). The network topology can identify plasmids with interesting gene-sharing patterns. For example, some plasmids may act as ‘bridges’ in the gene-sharing network, straddling different groups that respectively share few genes (in graph theory terms, these plasmids have high ‘betweenness centrality’). Such plasmids may represent co-integrate plasmids, or may be important in shuttling genes across the network (Halary et al., 2010).

Making well-supported inferences from gene-sharing networks is challenging since gene-sharing might result from vertical inheritance, horizontal acquisition, or acquisition from a source not represented within the network. However, there are various ways to infer HGT events (Fondi and Fani, 2010; Tamminen et al., 2012; Fondi et al., 2016). For example, finding genes with very high sequence identity from plasmids that otherwise share few genes has been used to identify putative recent HGT events (Yamashita et al., 2014). Overall, networks are a powerful complementary tool for visualizing relationships across diverse plasmids, and generating hypotheses about the horizontal component of plasmid evolution. Gene-sharing networks only require assembly of genes, not complete plasmids, so are suited to analysis of fragmented assemblies. Since network topology is determined by constituent nodes, large unbiased plasmid datasets will produce the most informative network analyses.

In future, optical mapping of intact plasmids could complement sequencing-based analysis. Specifically, AT-rich plasmid sequence can be fluorescently labeled, after which plasmids are elongated within nanofluidic channels and visualized with fluorescence microscopy. This allows a course-grained optical ‘barcode’ to be obtained. Optical plasmid barcoding could be used for classifying plasmids, and the course-grained structural information from the barcodes could guide plasmid assembly (Nyberg et al., 2016).

Meanwhile, current algorithms fail to accurately reconstruct all plasmids from short-read WGS datasets. Long-read sequencing will be increasingly used to determine accurate plasmid structures. Use of replicon and MOB typing for plasmid taxonomy will probably continue, but future developments could include methods to incorporate plasmid structural variation into a phylogenetic framework. Networks are a powerful tool for assessing plasmid relationships, from a functional rather than a phylogenetic perspective. Perhaps future plasmid databases could be structured as networks; when novel plasmids are added, information about their potential importance (e.g., centrality within the network) could be determined, and putative HGT events could be continually inferred. Future advances in plasmid metagenomics will enhance our knowledge of plasmids across a range of environments, and improve understanding of resistance gene reservoirs.

AO undertook literature searching, and wrote a draft manuscript. AO, HP, AS, NS, AW, MA, ME, MD, and NW suggested/implemented revisions. HP, AS, NS, AW, MA, and MD helped in planning the manuscript. AO, NS, MA, MD, ME, TP, DC, NW, AW, HP, and AS read and approved the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.