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. IMPORTANCE 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.
Assembly of bacterial short-read whole-genome sequencing data frequently results in hundreds of contigs for which the origin, plasmid or chromosome, is unclear. Complete genomes resolved by long-read sequencing can be used to generate and label short-read contigs. These were used to train several popular machine learning methods to classify the origin of contigs from Enterococcus faecium, Klebsiella pneumoniae and Escherichia coli using pentamer frequencies. We selected support-vector machine (SVM) models as the best classifier for all three bacterial species (F1-score E. faecium=0.92, F1-score K. pneumoniae=0.90, F1-score E. coli=0.76), which outperformed other existing plasmid prediction tools using a benchmarking set of isolates. We demonstrated the scalability of our models by accurately predicting the plasmidome of a large collection of 1644 E. faecium isolates and illustrate its applicability by predicting the location of antibiotic-resistance genes in all three species. The SVM classifiers are publicly available as an R package and graphical-user interface called ‘mlplasmids’. We anticipate that this tool may significantly facilitate research on the dissemination of plasmids encoding antibiotic resistance and/or contributing to host adaptation.
BackgroundThe gut microbiota is a reservoir of opportunistic pathogens that can cause life-threatening infections in critically ill patients during their stay in an intensive care unit (ICU). To suppress gut colonization with opportunistic pathogens, a prophylactic antibiotic regimen, termed “selective decontamination of the digestive tract” (SDD), is used in some countries where it improves clinical outcome in ICU patients. Yet, the impact of ICU hospitalization and SDD on the gut microbiota remains largely unknown. Here, we characterize the composition of the gut microbiota and its antimicrobial resistance genes (“the resistome”) of ICU patients during SDD and of healthy subjects.ResultsFrom ten patients that were acutely admitted to the ICU, 30 fecal samples were collected during ICU stay. Additionally, feces were collected from five of these patients after transfer to a medium-care ward and cessation of SDD. Feces from ten healthy subjects were collected twice, with a 1-year interval. Gut microbiota and resistome composition were determined using 16S rRNA gene phylogenetic profiling and nanolitre-scale quantitative PCRs.The microbiota of the ICU patients differed from the microbiota of healthy subjects and was characterized by lower microbial diversity, decreased levels of Escherichia coli and of anaerobic Gram-positive, butyrate-producing bacteria of the Clostridium clusters IV and XIVa, and an increased abundance of Bacteroidetes and enterococci. Four resistance genes (aac(6′)-Ii, ermC, qacA, tetQ), providing resistance to aminoglycosides, macrolides, disinfectants, and tetracyclines, respectively, were significantly more abundant among ICU patients than in healthy subjects, while a chloramphenicol resistance gene (catA) and a tetracycline resistance gene (tetW) were more abundant in healthy subjects.ConclusionsThe gut microbiota of SDD-treated ICU patients deviated strongly from the gut microbiota of healthy subjects. The negative effects on the resistome were limited to selection for four resistance genes. While it was not possible to disentangle the effects of SDD from confounding variables in the patient cohort, our data suggest that the risks associated with ICU hospitalization and SDD on selection for antibiotic resistance are limited. However, we found evidence indicating that recolonization of the gut by antibiotic-resistant bacteria may occur upon ICU discharge and cessation of SDD.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0309-z) contains supplementary material, which is available to authorized users.
Assembly of bacterial short-read whole genome sequencing (WGS) data frequently results in hundreds of contigs for which the origin, plasmid or chromosome, is unclear. Long-read sequencing has emerged as a solution to resolve plasmid structures and to obtain complete genomes for most bacterial species. This information can be used to generate and label datasets from short-read based contigs as plasmid-or chromosome-derived. We investigated the use of several popular machine learning methods to classify short-read contigs with known plasmid-or chromosome-origin from Enterococcus faecium , Klebsiella pneumoniae and Escherichia coli using pentamer frequencies. Based on resulting F1-scores we selected support-vector machine (SVM) models as best classifier for all three bacterial species (F1-score E. faecium = 0.94, F1-score K. pneumoniae = 0.90, F1-score E. coli = 0.76) , which outperformed other existing plasmid tools using an independent set of isolates (precision E. faecium = 0.92, precision K. pneumoniae = 0.86, precision E. coli = 0.82). We demonstrated the scalability of our model by accurately predicting the plasmidome of a large collection of 1,644 E. faecium isolates with only short-read WGS available using a standard laptop with a single core. A low number of false positive predicted sequences suggests that the assignment of a particular gene of interest as plasmid-or chromosome-encoded by the models is plausible. The SVM classifiers are publicly available as a new R package called 'mlplasmids' at https://gitlab.com/sirarredondo/mlplasmids under the GNU General Public License v3.0. We additionally developed a graphical-user interface using the Shiny package which can be accessed at https://sarredondo.shinyapps.io/mlplasmids/ . Single genomes can easily be predicted by uploading genome assemblies. We anticipate that this tool may significantly facilitate research on the dissemination of plasmids encoding antibiotic resistance and/or contributing to host adaptation.
Hospital-acquired Enterococcus faecium isolates responsible for nosocomial outbreaks and invasive infections are enriched in the orf2351 and orf2430 genes, encoding the SgrA and EcbA LPXTG-like cell wallanchored proteins, respectively. These two surface proteins were characterized to gain insight into their function, since they may have favored the rapid emergence of this nosocomial pathogen. We are the first to identify a surface adhesin among bacteria (SgrA) that binds to the extracellular matrix molecules nidogen 1 and nidogen 2, which are constituents of the basal lamina. EcbA is a novel E. faecium MSCRAMM (microbial surface component recognizing adhesive matrix molecules) that binds to collagen type V. In addition, both SgrA and EcbA bound to fibrinogen; however, SgrA targeted the alpha and beta chains, whereas EcbA bound to the gamma chain of fibrinogen. An E. faecium sgrA insertion mutant displayed reduced binding to both nidogens and fibrinogen. SgrA did not mediate binding of E. faecium cells to biotic materials, such as human intestinal epithelial cells, human bladder cells, and kidney cells, while this LPXTG surface adhesin is implicated in E. faecium biofilm formation. The acm and scm genes, encoding two other E. faecium MSCRAMMs, were expressed at the mRNA level together with sgrA during all phases of growth, whereas ecbA was expressed only in exponential and late exponential phase, suggesting orchestrated expression of these adhesins. Expression of these surface proteins, which bind to extracellular matrix proteins and are involved in biofilm formation (SgrA), may contribute to the pathogenesis of hospital-acquired E. faecium infections.Enterococcus faecium has emerged as an important opportunistic gram-positive pathogen, responsible for nosocomial infections and hospital outbreaks worldwide (1,30,49). E. faecium can cause a variety of infections in immunocompromised patients, such as urinary tract infections, surgical site infections, bacteremia, and endocarditis. Hospital-acquired E. faecium isolates recovered from clinical sites and isolates associated with nosocomial outbreaks clearly differ from clinically nonrelevant E. faecium (25). These hospital-acquired E. faecium strains show high-level resistance to ampicillin and ciprofloxacin (24), are enriched in putative virulence genes, such as esp (23), which encodes the enterococcal surface protein and is implicated in biofilm formation (16,47), genes putatively encoding the PilA and PilB pilus-like structures (17), and three cell wall-anchored LPXTG surface proteins, designated Orf903, Orf2351, and Orf2430 (18). These three surface proteins have in common that they contain an N-terminal signal peptide, a nonrepetitive A domain, and a C-terminal cell wall sorting signal (CWS), which is comprised of a highly conserved LPXTG-like (Leu-Pro-X-Thr-Gly, where X denotes any amino acid) sortase substrate motif and a hydrophobic domain followed by positively charged amino acids (27,40). After translocation of the precursor protein, the LPXTG motif is clea...
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