Characterization of antimicrobial resistance and quinolone resistance factors in high-level ciprofloxacin-resistant Enterococcus faecalis and Enterococcus faecium isolates obtained from fresh produce and fecal samples of patients

Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes

MOTIVATION

The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph.

RESULTS

We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes.

AVAILABILITY AND IMPLEMENTATION

Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.

MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec

Introduction

Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes.

Methods

In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions.

Results and discussion

Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

Characteristics and Global Occurrence of Human Pathogens Harboring Antimicrobial Resistance in Food Crops: A Scoping Review

Background

The role of the crop environment as a conduit for antimicrobial resistance (AMR) through soil, water, and plants has received less attention than other sectors. Food crops may provide a link between the agro-environmental reservoir of AMR and acquisition by humans, adding to existing food safety hazards associated with microbial contamination of food crops.

Objectives

The objectives of this review were: (1) to use a systematic methodology to characterize AMR in food crop value chains globally, and (2) to identify knowledge gaps in understanding exposure risks to humans.

Methods

Four bibliographic databases were searched using synonyms of AMR in food crop value chains. Following two-stage screening, phenotypic results were extracted and categorized into primary and secondary combinations of acquired resistance in microbes of concern based on established prioritization. Occurrence of these pathogen-AMR phenotype combinations were summarized by sample group, value chain stage, and world region. Sub-analyses on antimicrobial resistance genes (ARG) focused on extended-spectrum beta-lactamase and tetracycline resistance genes.

Results

Screening of 4,455 citations yielded 196 studies originating from 49 countries, predominantly in Asia (89 studies) and Africa (38). Observations of pathogen-phenotype combinations of interest were reported in a subset of 133 studies (68%). Primary combinations, which include resistance to antimicrobials of critical importance to human medicine varied from 3% (carbapenem resistance) to 13% (fluoroquinolones), whereas secondary combinations, which include resistance to antimicrobials also used in agriculture ranged from 14% (aminoglycoside resistance) to 20% (aminopenicillins). Salad crops, vegetables, and culinary herbs were the most sampled crops with almost twice as many studies testing post-harvest samples. Sub-analysis of ARG found similar patterns corresponding to phenotypic results.

Discussion

These results suggest that acquired AMR in opportunistic and obligate human pathogens is disseminated throughout food crop value chains in multiple world regions. However, few longitudinal studies exist and substantial heterogeneity in sampling methods currently limit quantification of exposure risks to consumers. This review highlights the need to include agriculturally-derived AMR in monitoring food safety risks from plant-based foods, and the challenges facing its surveillance.

Molecular characterization of antimicrobial resistance and virulence factors of Enterococcus faecalis from ducks at slaughterhouses

This study investigated the prevalence of antimicrobial resistant Enterococcus faecalis (E. faecalis) from ducks at slaughterhouses, analyzed antimicrobial resistance genes and virulence-associated genes of the isolates. Multilocus sequence typing (MLST) was performed to characterize their molecular characteristics. A total of 227 E. faecalis isolates (67.8%) were obtained from cecum (n = 114), cloaca (n = 50), skin (n = 59), and rinsed water (n = 4). These E. faecalis exhibited high level of resistance against tetracycline (95.6%), doxycycline (94.3%), linezolid (75.8%), erythromycin (72.2%), followed by norfloxacin (56.8%), vancomycin (38.3%), penicillin (36.1%), teicoplanin (30.8%). Lower level of resistance was found to high-level streptomycin (19.8%), imipenem (15.9%) and high-level gentamicin (5.7%). The vast majority of isolates (90.3%) were multidrug resistant (MDR). Moreover, the commonly observed resistance genes were optrA (90.7%) and ermB (90.3%), followed by aph(3’)-Ⅲ (86.8%), tetM (84.6%), acc(6’)-aph(2) (77.5%), blaZ (76.7%) and aac(6’)-Ie-aph(2”)-Ia (75.8%). The less frequently observed genes were vanC (19.8%), blaTEM (4.8%), vanM (2.6%), and vanA (0.4%). None of the strains carried aph(2”)-Ic and vanB genes. Furthermore, a high prevalence of ten virulence determinants was identified, and efaA (99.1%) was predominant, followed by eep (97.4%), srtA (96.9%), asa1 (95.6%), fsrB (92.1%), sprE (89.9%), aggA (63.9%), gelE (56.4%), esp (33.9%), and cylL (15.4%). Eleven isolates (4.9%) co-carried all of the tested virulence-associated genes. MLST analysis demonstrated that, E. faecalis isolates consisted of 12 known STs and 5 new STs, among which 6 of the identified STs were associated with nosocomial infection. Our data indicated that retail ducks serve as an important source of MDR E. faecalis with high pathogenicity potential, and suggested that transmission to humans could not be excluded.

Antimicrobial Resistance and Virulence Factor Gene Profiles of Enterococcus spp. Isolated from Giant Panda Oral Cavities

Introduction

The objective of this study was to determine the prevalence and characteristics of antimicrobial-resistant Enterococcus faecalis and E. faecium isolated from the oral cavities of captive giant pandas in China.

Material and Methods

The virulence-associated determinant and antimicrobial resistance genes were detected and antimicrobial susceptibility tests were performed on 54 strains of each bacterium.

Results

All isolates showed 100% multidrug resistance. E. faecalis isolates showed a higher percentage of strains resistant to gentamicin (48.1%), vancomycin (55.6%), linezolid (100%), and streptomycin (33.3%) than E. faecium isolates. The resistance genes of Enterococcus spp. were present to highly varying extents according to antibiotic type, their presence breaking down for E. faecalis and E. faecium respectively as aac(6')/aph(2″) 5.56% and 5.56%; aph(3')-Ⅲ 0% and 14.81%; ant(6)-I 0% and 3.7%; ant(4')-Ia 0% and 64.81%; tetL 20.37% and 100%; vanA 92.59% and 46.3%; vanB 0% and 0%; cfr 0% and 90.74%; optrA 96.3% and 3.7%; blaZ 0% and 1.85%; blaTEM 0% and 0%; tetA 20.37% and 0%; tetC 24.07% and 100%; tetM 0% and 0%; ermA 12.96% and 100%; ermB 5.56% and 3.7%; and ermC 0% and 1.85%.Virulence-associated determinants were detected in this research, which typically include efaA, gelE, asa1, ace, cylA, esp and hyl; however, the latter three were not detected. High proportions of the isolates carried the efaA, gelE, asa1, and ace genes. Respectively for E. faecalis and E. faecium their detection was efaA 98.1% and 85.2%; gelE 98.1% and 87%; asa1 92.6% and 87%; and ace 87% and 85.2%.

Conclusion

This is the first study on the potential disease risk and antimicrobial-resistant characteristics of E. faecalis and E. faecium isolates in giant panda oral cavities. The results of this study show that the antimicrobial resistance rate of Enterococcus spp. isolated from the oral cavity of captive pandas is very high, and thus needs to be monitored.

Ensembling graph attention networks for human microbe-drug association prediction

MOTIVATION

Human microbes get closely involved in an extensive variety of complex human diseases and become new drug targets. In silico methods for identifying potential microbe-drug associations provide an effective complement to conventional experimental methods, which can not only benefit screening candidate compounds for drug development but also facilitate novel knowledge discovery for understanding microbe-drug interaction mechanisms. On the other hand, the recent increased availability of accumulated biomedical data for microbes and drugs provides a great opportunity for a machine learning approach to predict microbe-drug associations. We are thus highly motivated to integrate these data sources to improve prediction accuracy. In addition, it is extremely challenging to predict interactions for new drugs or new microbes, which have no existing microbe-drug associations.

RESULTS

In this work, we leverage various sources of biomedical information and construct multiple networks (graphs) for microbes and drugs. Then, we develop a novel ensemble framework of graph attention networks with a hierarchical attention mechanism for microbe-drug association prediction from the constructed multiple microbe-drug graphs, denoted as EGATMDA. In particular, for each input graph, we design a graph convolutional network with node-level attention to learn embeddings for nodes (i.e. microbes and drugs). To effectively aggregate node embeddings from multiple input graphs, we implement graph-level attention to learn the importance of different input graphs. Experimental results under different cross-validation settings (e.g. the setting for predicting associations for new drugs) showed that our proposed method outperformed seven state-of-the-art methods. Case studies on predicted microbe-drug associations further demonstrated the effectiveness of our proposed EGATMDA method.

AVAILABILITY

Source codes and supplementary materials are available at: https://github.com/longyahui/EGATMDA/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting human microbe-drug associations via graph convolutional network with conditional random field

Motivation

Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task.

Results

In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network, and a microbe-drug interaction network. We then proposed a novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e., microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart (RWR) based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e., Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations.

Availability

Python codes and dataset are available at: https://github.com/longyahui/GCNMDA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Antimicrobial Activity of Schinopsis brasiliensis Engler Extract-Loaded Chitosan Microparticles in Oral Infectious Disease

Enterococcus faecalis infections represent a health concern, mainly in oral diseases, in which treatments with chlorhexidine solution (0.2%) are often used; however, it presents high toxicity degree and several side effects. Based on this, the use of natural products as an alternative to treatment has been explored. Nonetheless, plant extracts have poor organoleptic characteristics that impair theirs in natura use. Therefore, this work aimed to evaluate the analytical profile, biological activity, and cytotoxicity in vitro of S. brasiliensis-loaded chitosan microparticles (CMSb) produced using different aspersion flow rates. The analytical fingerprint was obtained by FTIR and NIR spectra. Principal components analysis (PCA) was used to verify the similarity between the samples. The crystallinity degree was evaluated by X-ray diffraction (XRD). Phytochemical screening (PS) was performed to quantify phytocompounds. Antimicrobial activity was evaluated by minimum inhibitory concentration (MIC). Antibiofilm activity and bactericidal kinetics against E. faecalis (ATCC 29212 and MB 146-clinical isolated) were also assessed. The hemolytic potential was performed to evaluate the cytotoxicity. Data provided by FTIR, NIR, and PCA analyses revealed chemical similarity between all CMSb. Furthermore, the results from XRD analysis showed that the obtained CMSb present amorphous characteristic. Tannins and polyphenols were accurately quantified by the PS, but methodology limitations did not allow the flavonoid quantification. The low hemolytic potential assay indicates that all samples are safe. Antimicrobial assays revealed that CMSb were able to inhibit not only the E. faecalis ATCC growth but also the biofilm formation. Only one CMSb sample was able to inhibit the clinical strain. These results highlighted the CMSb antimicrobial potential and revealed this system as a promising product to treat infections caused by E. faecalis.

Co-Incidence of Type II Topoisomerase Mutations and Efflux Expression in High Fluoroquinolone Resistant Enterococcus faecalis Isolated from Urinary Tract Infections

Introduction

Enterococcus faecalis is one of the most common pathogens in urinary tract infections (UTIs). Fluoroquinolones have been frequently used to treat E. faecalis UTIs, and the emergence of fluoroquinolone-resistant E. faecalis strains has recently been reported in several countries. This study aimed to elucidate the mechanisms involved in fluoroquinolone resistance in clinical E. faecalis isolates by analyzing mutations in quinolone- resistance-determining regions (QRDRs) of gyrA and parC and investigating the role of some efflux pumps.

Methods

In total, 70 clinical E. faecalis isolates collected from UTIs were identified by phenotypic and genotypic methods. Antimicrobial susceptibility testing was performed and multidrug-resistant (including ciprofloxacin resistant) isolates were studied for minimum inhibitory concentrations to ciprofloxacin, levofloxacin, and ofloxacin. In the following, mutations in QRDRs of gyrA and parC and expression of EfrA, EfrB, and EmeA efflux pumps were investigated in 20 high-level ciprofloxacin resistant and two ciprofloxacin susceptible isolates.

Results

High-level resistance to ciprofloxacin was detected in 97.5% of isolates. Sequencing of QRDRs revealed that 65% and 75% of isolates carried mutations in gyrA and parC, respectively. The presence of efflux genes was detected in all studied isolates, but expression of efrA, emeA, and efrB was demonstrated in 50%, 40%, and 30% of resistant isolates, respectively. Neither QRDR mutation nor the expression of efflux genes showed any significant association with MIC.

Conclusion

Co-incidence of mutation and efflux gene expression in more than half of isolates (13/20) suggests that both mechanisms may play a role in fluoroquinolone resistance. The other unknown mechanisms including different efflux pumps and probably other QRDRs mutations may contribute to fluoroquinolone resistance in E. faecalis.

Molecular characterization of antimicrobial-resistant Enterococcus faecalis and Enterococcus faecium isolated from layer parent stock

Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) are ubiquitous intestinal bacteria in humans and animals that can easily acquire antimicrobial resistance, which allows them to have roles as antimicrobial resistance indicators. In addition, layer parent stock produces thousands of eggs for the production of commercial laying hens and can transfer a variety of viral and bacterial agents to chicks. The objective of this study was to determine the prevalence and characteristics of antimicrobial-resistant E. faecalis and E. faecium isolated in the layer parent stock level of the egg-layer operating system in South Korea. A total of 129 E. faecalis and 166 E. faecium isolates from 74 flocks of 30 layer parent stock were tested for resistance in this study. The prevalence of doxycycline- (51.9%), erythromycin- (53.5%), high-level gentamicin- (13.2%), high-level kanamycin- (31.0%), high-level streptomycin- (30.2%), and tetracycline- (64.3%) resistant E. faecalis isolates were higher than those for E. faecium isolates (P < 0.05). The ermB gene was detected in 66 (95.7%) erythromycin-resistant E. faecalis isolates, which was higher than that of 32 (71.7%) erythromycin-resistant E. faecium isolates. Twenty-one high-level gentamicin-resistant Enterococcus spp. (17 E. faecalis and 4 E. faecium) carried at least one aminoglycoside-modifying enzyme gene, aac(6')Ie-aph(2')-Ia or ant(6)-Ia. Fourteen isolates that harbored both aac(6')Ie-aph(2')-Ia and ant(6)-Ia exhibited pattern A with IS256 at both ends. Ten high-level ciprofloxacin-resistant Enterococcus spp. (8 E. faecalis and 2 E. faecium) showed amino acid changes from serine to isoleucine at codons 83 in gyrA, and 80 in parC. Also, the virulence genes ace, asa1, efaA, and gelE were detected in this study. To the best of our knowledge, this is the first study to examine the prevalence and characteristics of antimicrobial-resistant E. faecalis and E. faecium isolates in the layer parent stock. Our findings support the need for a surveillance program to monitor the emergence of antimicrobial-resistant E. faecalis and E. faecium in layer operating system.

Temporal and Geographic Variation in Antimicrobial Susceptibility and Resistance Patterns of Enterococci: Results From the SENTRY Antimicrobial Surveillance Program, 1997-2016

Background

The SENTRY Antimicrobial Surveillance Program was established in 1997 and presently encompasses more than 750 000 bacterial isolates from over 400 medical centers worldwide. Among these pathogens, enterococci represents a prominent cause of bloodstream (BSIs), intra-abdominal (IAIs), skin and skin structure, and urinary tract infections (UTIs). In the present study, we reviewed geographic and temporal trends in Enterococcus species and resistant phenotypes identified throughout the SENTRY Program.

Methods

From 1997 to 2016, a total of 49 491 clinically significant enterococci isolates (15 species) were submitted from 298 medical centers representing the Asia-Pacific (APAC), European, Latin American (LATAM), and North American (NA) regions. Bacteria were identified by standard algorithms and matrix-assisted laser desorption ionization–time of flight mass spectrometry. Susceptibility (S) testing was performed by reference broth microdilution methods and interpreted using Clinical and Laboratory Standards Institute/US Food and Drug Administration and European Committee on Antimicrobial Susceptibility Testing criteria.

Results

The most common Enterococcus species in all 4 regions were Enterococcus faecalis (64.7%) and E. faecium (EFM; 29.0%). Enterococci accounted for 10.7% of BSIs in NA and was most prominent as a cause of IAIs (24.0%) in APAC and of UTIs (19.8%) in LATAM. A steady decrease in the susceptibility to ampicillin and vancomycin was observed in all regions over the 20-year interval. Vancomycin-resistant enterococci (VRE) accounted for more than 8% of enterococcal isolates in all regions and was most common in NA (21.6%). Among the 7615 VRE isolates detected, 89.1% were the VanA phenotype (91.0% EFM) and 10.9% were VanB. Several newer antimicrobial agents demonstrated promising activity against VRE, including daptomycin (99.6–100.0% S), linezolid (98.0%–99.6% S), oritavancin (92.2%–98.3% S), tedizolid (99.5%–100.0% S), and tigecycline (99.4%–100.0% S).

Conclusions

Enterococci remained a prominent gram-positive pathogen in the SENTRY Program from 1997 through 2016. The overall frequency of VRE was 15.4% and increased over time in all monitored regions. Newly released agents with novel mechanisms of action show promising activity against VRE.

Resistance profile of clinically relevant bacterial isolates against fluoroquinolone in Ethiopia: a systematic review and meta-analysis

Background

Fluoroquinolones are among the most frequently utilized antibacterial agents in developing countries like Ethiopia. Ciprofloxacin has become the most prescribed drug within this class and remains as one of the top three antibacterial agents prescribed in Ethiopia. However, several studies indicated that there is a gradual increase of antibacterial resistance. Therefore, this meta-analysis aimed to quantitatively estimate the prevalence of ciprofloxacin resistance bacterial isolates in Ethiopia.

Methods

Literature search was conducted from electronic databases and indexing services including EMBASE (Ovid interface), PubMed/MEDLINE, Google Scholar, Science Direct and WorldCat. Data were extracted with structured format prepared in Microsoft Excel and exported to STATA 15.0 software for the analyses. Pooled estimation of outcomes was performed with DerSimonian-Laird random-effects model at 95% confidence level. Degree of heterogeneity of studies was presented with I² statistics. Publication bias was conducted with comprehensive meta-analysis version 3 software and presented with funnel plots of standard error supplemented by Begg’s and Egger’s tests. The study protocol has been registered on PROSPERO with reference number ID: CRD42018097047.

Results

A total of 37 studies were included for this study. The pooled prevalence of resistance in selected gram-positive bacterial isolates against ciprofloxacin was found to be 19.0% (95% confidence interval [CI]: 15.0, 23.0). The degree of resistance among Staphylococcus aureus, Coagulase negative Staphyloccoci (CoNS), Enterococcus faecalis and Group B Streptococci (GBS) was found to be 18.6, 21.6, 23.9, and 7.40%, respectively. The pooled prevalence of resistance in gram-negative bacteria was about 21.0% (95% CI: 17, 25). Higher estimates were observed in Neisseria gonorrhea (48.1%), Escherichia coli (24.3%) and Klebsiella pneumonia (23.2%). Subgroup analysis indicated that blood and urine were found to be a major source of resistant S. aureus isolates. Urine was also a major source of resistant strains for CoNS, Klebsiella and Proteus species.

Conclusion

Among gram-positive bacteria, high prevalence of resistance was observed in E. faecalis and CoNS whereas relatively low estimate of resistance was observed among GBS isolates. Within gram-negative bacteria, nearly half of isolates in N. gonorrhoea were found ciprofloxacin resistant. From enterobacteriaceae isolates, K. pneumonia and E. coli showed higher estimates of ciprofloxacin resistance.

Electronic supplementary material

The online version of this article (10.1186/s40360-018-0274-6) contains supplementary material, which is available to authorized users.

Characteristics of High-Level Ciprofloxacin-Resistant Enterococcus faecalis and Enterococcus faecium from Retail Chicken Meat in Korea

Genes encoding ciprofloxacin resistance in enterococci in animals may be transferred to bacteria in the animal gut and to zoonotic bacteria where they could pose a human health hazard. The objective of this study was to characterize antimicrobial resistance in high-level ciprofloxacin-resistant (HLCR) Enterococcus faecalis and Enterococcus faecium isolated from retail chicken meat. A total of 345 enterococci (335 E. faecalis and 10 E. faecium) were isolated from 200 chicken meat samples. Of these, 85 E. faecalis isolates and 1 E. faecium isolate were confirmed as HLCR enterococci. All 86 HLCR enterococci displayed gyrA- parC point mutations consisting of S83I-S80I (94.2%, 81 isolates), S83F-S80I (2.3%, 2 isolates), S83Y-S80I (2.3%, 2 isolates), and S83Y-S80F (1.2%, 1 isolate). Sixty-one (72.9%) of the 86 HLCR enterococci showed multidrug resistance to three to six classes of antimicrobial agents. Multilocus sequence typing revealed that E. faecalis had 17 different sequence types (ST) and E. faecium had 1 different ST, with ST256 observed most often (44 isolates, 51.8%). Although these results cannot exclude the possibility that pathotypes of enterococci isolated from chicken might represent transmission to or from humans, the foodborne HLCR E. faecalis indicated that the food chain is a potential route of enterococcal infection in humans.