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Horizontal gene transfer (HGT) plays a critical role in the evolution and diversification of many microbial species. The resulting dynamics of gene gain and loss can have important implications for the development of antibiotic resistance and the design of vaccine and drug interventions. Methods for the analysis of gene presence/absence patterns typically do not account for errors introduced in the automated annotation and clustering of gene sequences. In particular, methods adapted from ecological studies, including the pangenome gene accumulation curve, can be misleading as they may reflect the underlying diversity in the temporal sampling of genomes rather than a difference in the dynamics of HGT. Here, we introduce Panstripe, a method based on Generalised Linear Regression that is robust to population structure, sampling bias and errors in the predicted presence/absence of genes. We demonstrate using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analysing several diverse bacterial genome datasets representing major human pathogens. Panstripe is freely available as an R package at https://github.com/gtonkinhill/panstripe.

Enterococcus faecium is a nosocomial, multidrug-resistant pathogen. Whole genome sequence studies revealed that hospital-associated E. faecium isolates are clustered in a separate clade A1. Here, we investigated the distribution, integration site and function of a putative iol gene cluster that encodes for myo-inositol (MI) catabolism. This iol gene cluster was found as part of an ~20 kbp genetic element (iol element), integrated in ICEEfm1 close to its integrase gene in E. faecium isolate E1679. Among 1644 E. faecium isolates, ICEEfm1 was found in 789/1227 (64.3 %) clade A1 and 3/417 (0.7 %) non-clade A1 isolates. The iol element was present at a similar integration site in 180/792 (22.7 %) ICEEfm1-containing isolates. Examination of the phylogenetic tree revealed genetically closely related isolates that differed in presence/absence of ICEEfm1 and/or iol element, suggesting either independent acquisition or loss of both elements. E. faecium iol gene cluster containing isolates E1679 and E1504 were able to grow in minimal medium with only myo-inositol as carbon source, while the iolD-deficient mutant in E1504 (E1504∆iolD) lost this ability and an iol gene cluster negative recipient strain gained this ability after acquisition of ICEEfm1 by conjugation from donor strain E1679. Gene expression profiling revealed that the iol gene cluster is only expressed in the absence of other carbon sources. In an intestinal colonization mouse model the colonization ability of E1504∆iolD mutant was not affected relative to the wild-type E1504 strain. In conclusion, we describe and functionally characterise a gene cluster involved in MI catabolism that is associated with the ICEEfm1 island in hospital-associated E. faecium isolates. We were unable to show that this gene cluster provides a competitive advantage during gut colonisation in a mouse model. Therefore, to what extent this gene cluster contributes to the spread and ecological specialisation of ICEEfm1-carrying hospital-associated isolates remains to be investigated.

Background Enterococcus faecium is a commensal of the gastrointestinal tract of animals and humans but also a causative agent of hospital-acquired infections. Resistance against glycopeptides and to vancomycin has motivated the inclusion of E. faecium in the WHO global priority list. Vancomycin resistance can be conferred by the vanA gene cluster on the transposon Tn1546, which is frequently present in plasmids. The vanA gene cluster can be disseminated clonally but also horizontally either by plasmid dissemination or by Tn1546 transposition between different genomic locations. Methods We performed a retrospective study of the genomic epidemiology of 309 vancomycin-resistant E. faecium (VRE) isolates across 32 Dutch hospitals (2012–2015). Genomic information regarding clonality and Tn1546 characterization was extracted using hierBAPS sequence clusters (SC) and TETyper, respectively. Plasmids were predicted using gplas in combination with a network approach based on shared k-mer content. Next, we conducted a pairwise comparison between isolates sharing a potential epidemiological link to elucidate whether clonal, plasmid, or Tn1546 spread accounted for vanA-type resistance dissemination. Results On average, we estimated that 59% of VRE cases with a potential epidemiological link were unrelated which was defined as VRE pairs with a distinct Tn1546 variant. Clonal dissemination accounted for 32% cases in which the same SC and Tn1546 variants were identified. Horizontal plasmid dissemination accounted for 7% of VRE cases, in which we observed VRE pairs belonging to a distinct SC but carrying an identical plasmid and Tn1546 variant. In 2% of cases, we observed the same Tn1546 variant in distinct SC and plasmid types which could be explained by mixed and consecutive events of clonal and plasmid dissemination. Conclusions In related VRE cases, the dissemination of the vanA gene cluster in Dutch hospitals between 2012 and 2015 was dominated by clonal spread. However, we also identified outbreak settings with high frequencies of plasmid dissemination in which the spread of resistance was mainly driven by horizontal gene transfer (HGT). This study demonstrates the feasibility of distinguishing between modes of dissemination with short-read data and provides a novel assessment to estimate the relative contribution of nested genomic elements in the dissemination of vanA-type resistance.

Enterococcus faecium is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that E. faecium strains from hospitalized patients form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here we further explored the adaptive evolution of E. faecium using a genome-wide co-evolution study (GWES) to identify co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are not proximal to each other based on the completely assembled chromosome of clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data.Impact statementEnterococcus faecium has emerged as an important nosocomial pathogen around the world. Population genetics revealed that clinical E. faecium strains form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here, the adaptive evolution of E. faecium was further explored using an unsupervised machine learning method (SuperDCA) to identify genome-wide co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are separated by a large chromosomal distance in a clinical clade A1 reference isolate, but appeared adjacent to each other in non-clade A1 isolates. We identified four different types of large-scale genomic rearrangements and in all cases, we found insertion of two different genomic islands and an insertion element at the border. In contrast, no genomic rearrangement and insertions were identified among a group of clade A1 pet isolates, suggesting a distinct evolutionary trajectory. Based on the in silico predicted biological functions, we found a common link to a stress response for the genes encoded in three regions. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed.Data summaryRaw core-genome alignment (1.1 MB, Harvest suite v1.1.2), including the 1,644 Clade A isolates and the complete E. faecium AUS0004 (accession number CP003351) as a reference is available under the following gitlab repository https://gitlab.com/sirarredondo/efm_gwes.

BackgroundEnterococcus faecium is a commensal of the gastrointestinal tract of animals and humans but also a causative agent of hospital-acquired infections. Resistance against glycopeptides and especially to vancomycin, a first-line antibiotic to treat infections with multidrug-resistant Gram-positive pathogens, has motivated the inclusion of E. faecium in the WHO global priority list. Vancomycin resistance can be conferred by the vanA gene cluster on the transposon Tn1546, which is frequently present in plasmids. The vanA gene cluster can be disseminated clonally but also horizontally either by plasmid dissemination or Tn1546 transposition between different genomic locations. Here, we reconstructed all nested genetic elements (clone, plasmid, transposon) to study how the dissemination of vanA-type vancomycin resistance occurred in Dutch hospitals (2012-2015).MethodsWe performed a retrospective study of the genomic epidemiology of 309 vancomycin-resistant E. faecium (VRE) isolates across 32 Dutch hospitals (2012-2015). Genomic information regarding clonality and Tn1546 characterisation was extracted using hierBAPS sequence clusters (SC) and TETyper, respectively. Plasmids were predicted using gplas in combination with a network approach based on shared k-mer content. This allowed determining all nested genomic elements (clone, plasmid and transposon) involved in the dissemination of the vanA gene cluster. Next, we conducted an “all vs. all” pairwise comparison between isolates sharinga potential epidemiological link to elucidate whether clonal, plasmid or Tn1546 spread accounted for the dissemination of vanA resistance.ResultsThe 309 VRE isolates belonged to 18 different SCs of which SC13 (n = 102, 33%), SC17 (n = 52, 16.8%) and SC18 (n = 42, 13.6%) were predominant. We identified seven different plasmid types bearing the vanA gene cluster, four of which were highly similar (identity ∼99%, coverage∼84%) to previously described complete plasmid sequences. We estimated that clonal dissemination contributed most (∼45%) to the spread of vancomycin-resistance in Dutch hospitals, followed by Tn1546 mobilisation (∼12%) and plasmid dissemination (∼6%).ConclusionsThe dissemination of the vanA gene cluster in Dutch hospitals between 2012 and 2015 was dominated by clonal spread. However, we also identified outbreak settings with high frequencies of Tn1546 transposition and/or plasmid dissemination in which the spread of resistance was mainly driven by horizontal gene transfer (HGT). This study demonstrates the feasibility of distinguishing between modes of dissemination with short-read data and provides one of the first quantitative assessments to estimate the relative contribution of nested genomic elements in the dissemination of vanA-type vancomycin resistance cluster.

Background The nosocomial pathogen Enterococcus faecium can survive for prolonged periods of time on surfaces in the absence of nutrients. This trait is thought to contribute to the ability of E. faecium to spread among patients in hospitals. There is currently a lack of data on the mechanisms that are responsible for the ability of E. faecium to survive in the absence of nutrients. Results We performed a high-throughput transposon mutant library screening (Tn-seq) to identify genes that have a role in long-term survival during incubation in phosphate-buffered saline (PBS) at 20 °C. A total of 24 genes were identified by Tn-seq to contribute to survival in PBS, with functions associated with the general stress response, DNA repair, metabolism, and membrane homeostasis. The gene which was quantitatively most important for survival in PBS was usp (locus tag: EfmE745_02439), which is predicted to encode a 17.4 kDa universal stress protein. After generating a targeted deletion mutant in usp, we were able to confirm that usp significantly contributes to survival in PBS and this defect was restored by in trans complementation. The usp gene is present in 99% of a set of 1644 E. faecium genomes that collectively span the diversity of the species. Conclusions We postulate that usp is a key determinant for the remarkable environmental robustness of E. faecium. Further mechanistic studies into usp and other genes identified in this study may shed further light on the mechanisms by which E. faecium can survive in the absence of nutrients for prolonged periods of time.

Enterococcus 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., 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. faecium.

Enterococcus faecium is a gut commensal of many mammals but is also recognized as a major nosocomial human pathogen, as it is listed on the WHO global priority list of multi-drug resistant organisms. Previous research has suggested that nosocomial strains have multiple zoonotic origins and are only distantly related to those involved in human commensal colonization. Here we present the first comprehensive population-wide joint genomic analysis of hospital, commensal and animal isolates using both short- and long-read sequencing techniques. This enabled us to investigate the population plasmidome, core genome variation and genome architecture in detail, using a combination of machine learning, population genomics and genome-wide co-evolution analysis. We observed a high level of genome plasticity with large-scale inversions and heterogeneous chromosome sizes, collectively painting a high-resolution picture of the adaptive landscape of E. faecium, and identified plasmids as the main indicator for host-specificity. Given the increasing availability of long-read sequencing technologies, our approach could be widely applied to other human and animal pathogen populations to unravel fine-scale mechanisms of their evolution.

Plasmids are autonomous extra-chromosomal elements in bacterial cells that can carry genes that are important for bacterial survival. To benchmark algorithms for automated plasmid sequence reconstruction from short read sequencing data, we selected 42 publicly available complete bacterial genome sequences which were assembled by a combination of long- and short-read data. The selected bacterial genome sequence projects span 12 genera, containing 148 plasmids. We predicted plasmids from short-read data with four different programs (PlasmidSPAdes, Recycler, cBar and PlasmidFinder) and compared the outcome to the reference sequences.PlasmidSPAdes reconstructs plasmids based on coverage differences in the assembly graph. It reconstructed most of the reference plasmids (recall = 0.82) but approximately a quarter of the predicted plasmid contigs were false positives (precision = 0.76). PlasmidSPAdes merged 83 % of the predictions from genomes with multiple plasmids in a single bin. Recycler searches the assembly graph for sub-graphs corresponding to circular sequences and correctly predicted small plasmids but failed with long plasmids (recall = 0.12, precision = 0.30). cBar, which applies pentamer frequency composition analysis to detect plasmid-derived contigs, showed an overall recall and precision of 0.78 and 0.64. However, cBar only categorizes contigs as plasmid-derived and does not bin the different plasmids correctly within a bacterial isolate. PlasmidFinder, which searches for matches in a replicon database, had the highest precision (1.0) but was restricted by the contents of its database and the contig length obtained from de novo assembly (recall = 0.36).Surprisingly, PlasmidSPAdes and Recycler detected single isolated components corresponding to putative novel small plasmids (This study shows that it is possible to automatically predict plasmid sequences, but only for small plasmids. The reconstruction of large plasmids (>50 kbp) containing repeated sequences remains challenging and limits the high-throughput analysis of WGS data.Author SummaryShort read sequencing of the DNA of bacteria is often used to understand characteristics such as antibiotic resistance. However the assembly of short read sequencing data with the goal of reconstructing a complete genome is often fragmented and leaves gaps. Therefore independently replicating DNA fragments called plasmids cannot easily be identified from an assembly. Lately a number of programs have been developed to enable the automated prediction of the sequences of plasmids. Here we tested these programs by comparing their outcomes with complete genome sequences. None of the tested programs were able to fully and unambiguously predict distinct plasmid sequences. All programs performed best with the prediction of plasmids smaller than 50 kbp. Larger plasmids were only correctly predicted if they were present as a single contig in the assembly. While predictions by PlasmidSPAdes and cBar contained most of the plasmids, they were merged with or indistinguishable from other plasmids and sometimes chromosome sequences. PlasmidFinder missed most plasmids but all its predictions were correct. Without manual steps or long-read sequencing information, plasmid reconstruction from short read sequencing data remains challenging.