Plasmids Shaped the Recent Emergence of the Major Nosocomial Pathogen Enterococcus faecium

Abstract

Enterococcus faecium is a gut commensal of humans and animals but is also listed on the WHO global priority list of multidrug-resistant pathogens. Many of its antibiotic resistance traits reside on plasmids and have the potential to be disseminated by horizontal gene transfer. Here, we present the first comprehensive population-wide analysis of the pan-plasmidome of a clinically important bacterium, by whole-genome sequence analysis of 1,644 isolates from hospital, commensal, and animal sources of E. faecium Long-read sequencing on a selection of isolates resulted in the completion of 305 plasmids that exhibited high levels of sequence modularity. We further investigated the entirety of all plasmids of each isolate (plasmidome) using a combination of short-read sequencing and machine-learning classifiers. Clustering of the plasmid sequences unraveled different E. faecium populations with a clear association with hospitalized patient isolates, suggesting different optimal configurations of plasmids in the hospital environment. The characterization of these populations allowed us to identify common mechanisms of plasmid stabilization such as toxin-antitoxin systems and genes exclusively present in particular plasmidome populations exemplified by copper resistance, phosphotransferase systems, or bacteriocin genes potentially involved in niche adaptation. Based on the distribution of k-mer distances between isolates, we concluded that plasmidomes rather than chromosomes are most informative for source specificity of E. faeciumIMPORTANCEEnterococcus faecium is one of the most frequent nosocomial pathogens of hospital-acquired infections. E. faecium has gained resistance against most commonly available antibiotics, most notably, against ampicillin, gentamicin, and vancomycin, which renders infections difficult to treat. Many antibiotic resistance traits, in particular, vancomycin resistance, can be encoded in autonomous and extrachromosomal elements called plasmids. These sequences can be disseminated to other isolates by horizontal gene transfer and confer novel mechanisms to source specificity. In our study, we elucidated the total plasmid content, referred to as the plasmidome, of 1,644 E. faecium isolates by using short- and long-read whole-genome technologies with the combination of a machine-learning classifier. This was fundamental to investigate the full collection of plasmid sequences present in our collection (pan-plasmidome) and to observe the potential transfer of plasmid sequences between E. faecium hosts. We observed that E. faecium isolates from hospitalized patients carried a larger number of plasmid sequences compared to that from other sources, and they elucidated different configurations of plasmidome populations in the hospital environment. We assessed the contribution of different genomic components and observed that plasmid sequences have the highest contribution to source specificity. Our study suggests that E. faecium plasmids are regulated by complex ecological constraints rather than physical interaction between hosts.

Keywords: Enterococcus faecium; long-read sequencing; machine learning; nosocomial pathogen; plasmidome; source specificity.

FIG 1

(A) RAxML tree based on 955 E. faecium core genes in 1,644 clade A strains. Isolates selected for long-read sequencing are indicated with + under long-read selection. Isolates were colored based on their isolation source: hospitalized patients (red), nonhospitalized persons (blue), pet (green), pig (pink), poultry (brown), and other sources (black). Arrow in the RAxML tree indicates the internal node 1227 used to split the clade A1 and non-clade A1 isolates. (B) For each isolation source (x axis), we specified the count and percentage (y axis) of isolates belonging or not to clade A1.

FIG 2

Overview of completed plasmid sequences (n = 305). (A) Pairwise Mash distances (k = 21, s = 1,000) of the completed plasmid sequences (n = 305) were transformed into a distance matrix and clustered using hierarchical clustering (ward.D2). Node positions in the dendrogram were used to sort and represent in different panels: (i) isolation source, (ii) replication initiator gene (RIP), and (iii) plasmid size (kbp) of the completed plasmid sequences. (B) Intersection plot of the combination of RIP and relaxases found in the set of completed plasmid sequences with associated RIP sequences (n = 294).

FIG 3

Predicted pan-plasmidome of 1,644 E. faecium isolates. (A) Boxplot of the numbers of base pairs (kbp) predicted as plasmid derived per isolation source. Horizontal dashed line indicates the mean cumulative plasmid length across all the groups. (B) Pairwise Mash distances (k = 21, s = 1,000) of plasmid-predicted contigs in 1,607 isolates were transformed into a distance matrix and clustered using hierarchical clustering (ward.D2). Based on the quantile function of our defined gamma distribution, we grouped isolates in five blocks: black (0 to 0.01), red (0.01 to 0.25), orange (0.25 to 0.5), yellow (0.5 to 0.75), and white (0.75 to 1.0). Dissimilarity matrix of the isolates was visualized as a heat map colored based on the previous blocks. We incorporated the defined plasmid populations (n = 9) and isolation source information on top and left dendrograms, respectively.

FIG 4

Comparison of reconstructed E. faecium core genome phylogeny and plasmidome trees. The figure includes three different panels: isolation source, sequence cluster (SC), and plasmidome population. (A) bioNJ tree based on the dissimilarity matrix of Mash distances (k = 21, n = 1,000) of 1,607 isolates uniquely considering plasmid-predicted contigs. (B) RAxML core genome tree based on 955 E. faecium core genes in 1,644 clade A strains.

FIG 5

Evaluation of the source specificity from each genomic component. Mash distances computed from chromosome-predicted (first column), plasmid-predicted (second column), and whole-genome (third column) contigs were scaled and compared between all the isolation sources. Each row corresponds to a particular isolation source (e.g., first row refers to dog isolates) and the distribution of pairwise distances against other sources (dog in green, hospitalized patient in red, nonhospitalized person in blue, pig in pink, poultry in brown, and random isolates in black) for each genomic component. These average distances were computed using a bootstrap approach (100 iterations). The distribution of pairs of isolates from the same source type with respect to the distribution of pairs from random isolates (black group) reflects the specificity of the genome component in each source. If pairs from the same source deviate to the left, it indicates a higher specificity of that particular genomic component, whereas a deviation to the right with respect to the pairs of random isolates (black group) indicates a lower specificity than expected by chance.