ABSTRACT
Enterococcus faecalis is an important intestinal colonizing bacteria and can cause various tissue infections, including invasive blood infection (BI). The annual incidence of E. faecalis BI has been estimated to be ~4.5 per 100,000, with a fatality rate that can reach 20%. However, whether bacterial colonization or invasive infections are tissue based has not been thoroughly studied. In this study, we analyzed 537 clinical isolates from 7 different tissues to identify the key genomic elements that facilitate the colonization and invasive infection of E. faecalis. Comparative genomic analysis revealed that the BI E. faecalis isolates had the largest genome size but the lowest GC content, fsr quorum-sensing system genes were enriched in the BI E. faecalis, and the fsr gene cluster could enhance biofilm formation and serum resistance ability. Our findings also provide deep insight into the genomic differences between different tissue isolates, and the fsr quorum-sensing systems could be a key factor promoting E. faecalis invasion into the blood.
IMPORTANCE First, we conducted an advanced study on the genomic differences between colonizing and infecting E. faecalis, which provides support and evidence for early and accurate diagnoses. Second, we discovered that fsr was significantly associated with blood infections, which also provides additional information for studies exploring the invasiveness of E. faecalis. Most importantly, we found that fsr played an important role in both biofilm formation and serum resistance ability in E. faecalis.
KEYWORDS: Enterococcus faecalis, genomes, Fsr quorum-sensing system, colonization
INTRODUCTION
Enterococcus faecalis is a Gram-positive commensal bacterium common in humans, animals, and insects (1). In the past few decades, E. faecalis has emerged as an important health care-associated pathogen and the second leading cause of health care-associated bacteremia (2, 3). The incidence rate of invasive blood infections is 10.6% (4), resulting in an ~19 to 48% mortality rate (5–7).
Intraspecies variations are a critical factor influencing the efficiency of E. faecalis colonization and invasiveness (8–10). Multilocus sequence typing (MLST) revealed that clonal complexes (CCs) CC2, CC9, and CC87 and a widely prevalent sequence type 16 (ST16) are at high risk for E. faecalis infections (1). The predominant clones isolated from nonsterile tissues were ST16 and ST179 (11), while ST6, ST40, ST16, and ST179 E. faecalis clones were more likely to cause blood infections (12). On the contrary, ST30 and ST44 tended to colonize in the gut niche (13). Intraspecies variations were shaped by varied virulence or metabolism of E. faecalis, but specific genomic gaps between different E. faecalis have not been explored in depth.
The classic virulence factors in E. faecalis include five categories (adherence, antiphagocytosis, biofilm formation, exoenzyme, and exotoxin), which are associated with innate immune evasion, intercellular adhesion, and the release of toxic substances (14). However, studies on the genomic functional analysis of E. faecalis have not identified the roles of colonization pressure and host factors on the emergence of infections in clinical settings. Among the virulence factors, biofilm formation is the most important during colonization and invasion (15, 16). Biofilm formation consists of many steps, starting with attachment to a living or nonliving surface that will lead to the formation of a microcolony, giving rise to three-dimensional structures, and ending up, after maturation, with detachment. In general, bacterial biofilms show resistance against the human immune system, as well as against antibiotics (17). Recent studies indicate that bacterial biofilms in Streptococci, Staphylococci, and Pseudomonas aeruginosa contribute to the colonization of host tissue (18). However, the effects of biofilms on the tissue colonization and invasiveness of E. faecalis have not been adequately studied.
In this study, a total of 537 E. faecalis genomes (52 clinical isolated genomes and 485 public genomes) isolated from the gastrointestinal (GI) tract, urinary tract, blood, intra-abdominal region, wound, eye, and respiratory tract were subjected to comparative genomic analysis, and further experimental approaches were conducted. The results revealed that fsr quorum-sensing systems could contribute to the colonization and invasiveness of E. faecalis in the blood.
RESULTS
The different derived E. faecalis genomes displayed large genomic diversity.
A total of 537 E. faecalis genomes were analyzed in this study. Most were obtained from the gastrointestinal tract, followed by the urinary tract, blood, and intra-abdominal region (Fig. 1). The E. faecalis genomes displayed an average genome size of 2.98 Mb (range, 2.63 Mb to 3.45 Mb), with G+C contents ranging from 37.0 to 39.0%.
FIG 1.
Genome size (A) and GC content (B) of E. faecalis strains isolated from different niches. *, P < 0.05; **, P < 0.01 (one-way ANOVA test).
E. faecalis strains from sterile sites (urinary tract, blood, and intra-abdominal region) displayed significant differences from strains from nonsterile sites (GI tract) in genome size (from 2.63 Mb to 3.45 Mb), gene numbers (from 2466 to 3578), and GC content (from 36.9% to 38.7%) (Fig. 1). In particular, the invasive blood derived from E. faecalis had the largest genome size (3.10 ± 0.19 Mb) and the biggest number of genes (3,042 ± 240) but the lowest GC contents (37.37 ± 0.35%) (Fig. 1). In addition, the main strains from blood, enriched into two main clusters on the phylogenetic tree, were ST9, ST742, ST743, ST959, ST32, ST79, ST674, ST103, and ST6 (see Fig. S2 in the supplemental material).
Different tissue-derived E. faecalis genomes showed specific virulence characteristics.
In general, the accessory genome of a single pathogen could confer selective advantages, including adaptation to particular niches, antibiotic resistance, and colonization of different hosts. We then analyzed the accessory genome of 537 E. faecalis genomes composed of 17,243 genes and annotated these genes into clusters of orthologous groups (COGs) to further identify features prevalent within the colonization and invasiveness of E. faecalis. There were no obvious differences in 22 clusters of orthologous groups identified with COG analysis (Fig. 2A), indicating that the invasiveness of E. faecalis obtained from the GI tract or other sterile sites was not affected by intrinsic metabolism. Also, the distribution of plasmids, phages, mobile genetic elements, and repeated sequences in different tissue-derived E. faecalis genomes showed no significant differences (Table S1).
FIG 2.
(A) Presence and absence (with 1 and 0) matrix of 17,432 accessory genes of 537 E. faecalis genomes. (B) Distribution of virulence genes in different sources of 537 E. faecalis genomes. Box indicates the presence percentage of specific virulence genes.
Therefore, to investigate the different genetic contents of various E. faecalis genomes, we analyzed the external virulence factors in these isolates according to the VFDB database (Fig. 2B). Consistently, the presence percentages of cps genes (cpsC to cpsK) were higher in the sterile group (urinary tract, blood, and intra-abdominal region) than the nonsterile group (GI tract) in our study. Other virulence factors (srtC, ebpC, efaA, ebpA, ebpB, and bopD) that referred to adherence and biofilm formation were almost all identified in both nonsterile and sterile groups. In addition, the percentage of cytolysin (cyl) operon genes (cylI, cylL, cylM, cylR1, cylR2, and cylS) detected in the E. faecalis genomes almost reached 58% and was highest in strains from the urinary tract within all groups. The detection probability of the cyl gene in E. faecalis from the urinary tract ranged from ~40% to 70% in past studies, and our findings were consistent with these. Recent studies also have reported that cyl is enriched among clinical isolates and could be associated with colonization of the urinary tract (11, 19).
The fsr gene cluster was highly correlated with blood-derived E. faecalis.
We observed that the probability of a simultaneous presence of quorum-sensing system-associated genes (fsrA, fsrB, fsrC, gelE, and sprE) was the highest in strains obtained from blood in all groups, indicating that the fsr gene cluster could play a role in the colonization and invasiveness of E. faecalis in blood.
The fsr quorum-sensing system (fsrA to fsrC) is an important two-component regulator that regulates gelE and sprE expression, both of which are important for biofilm formation. Gelatinase and serine protease, encoded by gelE and sprE, respectively, are positively regulated by the fsr quorum-sensing system (20). Moreover, fsr is the only global regulator identified and the only quorum-sensing system controlling E. faecalis biofilm formation under different environmental conditions (21). To illustrate the relationship between the fsr quorum-sensing system and the colonization and invasiveness of E. faecalis in blood, we analyzed the genomic characteristics of the fsr-gelE region and the gene presence rates in all five source groups. We found that the fsr-gelE regions were classified into three types based on the presence or absence of these five genes (fsrA, fsrB, fsrC, gelE, and sprE), cluster positive (all five genes present), partial (one or more genes absent), or negative (all five genes absent) (Fig. 3A). In general, the detection rates of fsr-gelE-positive clusters in strains of blood, intra-abdominal region, urinary tract, and GI tract decreased in turn (Fig. 3B). The probability of an fsr-gelE-positive cluster showed a significant correlation (P < 0.001) with the strain from blood, indicating a specific fsr mechanism inducing E. faecalis to invade the blood.
FIG 3.
(A) Structure of the fsr-gelE region in E. faecalis genomes. The five genes (fsrA, fsrB, fsrC, gelE, and sprE) associated with the fsr quorum-sensing system and other genes are highlighted with red and gray arrows, respectively. The light blue portion means a BLASTN alignment identity of ≥97% between two genome regions. (B) Distribution of structure clusters of the fsr-gelE region in E. faecalis from different sources. The positive, partial, and negative clusters are colored by straw yellow, magenta, and blue, respectively.
The fsr gene cluster enhanced biofilm formation and serum resistance ability in E. faecalis.
To study the function of the fsr gene cluster, we selected six clinical strains for the virulence test, biofilm assay, and serum resistance assay, using ATCC 19433 as a control. After scanning the presence or absence of the fsr gene cluster, we divided the six clinical strains into two groups, fsr positive (fsr+) and fsr negative (fsr−) (Table 1). The growth curves of these six strains were generally consistent (Fig. S1A). There was no significant difference in the virulence test between the fsr+ group and the fsr− group (Fig. S1B). However, we found that strains from the fsr+ group had a higher biofilm formation ability than the fsr− group (Fig. 4A). When the E. faecalis was grown in normal human serum was cultured, the growth rate of fsr+ strains was significantly higher than that of fsr− strains at 3 h (P = 0.001), indicating the better serum resistance ability of fsr+ strains (Fig. 4B).
TABLE 1.
Distribution of genes associated with fsr quorum-sensing system of the seven strains used in the experiment
| Isolate | Isolation location | fsr group | Data for:a |
||||
|---|---|---|---|---|---|---|---|
| sprE | gelE | fsrC | fsrB | fsrA | |||
| DT-Efs3599 | Intra-abdominal | fsr− | 0 | 0 | 0 | 0 | 0 |
| DT-Efs5202 | Urinary tract | fsr− | 0 | 0 | 0 | 0 | 0 |
| DT-Efs4131 | Wound | fsr− | 0 | 0 | 0 | 0 | 0 |
| DT-Efs3350 | Blood | fsr + | 1 | 1 | 1 | 1 | 1 |
| DT-Efs4455 | Intra-abdominal | fsr + | 1 | 1 | 1 | 1 | 1 |
| DT-Efs4630 | Intra-abdominal | fsr + | 1 | 1 | 1 | 1 | 1 |
| ATCC 19433 | Unknown | Control | 1 | 1 | 1 | 0 | 0 |
1, gene was present in a genome; 0, gene was absent in a genome (cutoff, sequence identity ≥ 80% and coverage ≥ 80%).
FIG 4.
(A) Serum resistance ability of E. faecalis strains of fsr+ and fsr− groups at 0, 1, and 3 h. We took the colony number of E. faecalis ATCC 19433 at each time point as the control and divided the colony numbers of the other 6 strains (DT-Efs3350, DT-Efs4455, DT-Efs4630, DT-Efs3599, DT-Efs4131, and DT-Efs5202) by it to obtain the corresponding colonies growth multiples. Each experiment was replicated three times. **, P < 0.01 (one-way ANOVA test). (B) Biofilm formation assay of E. faecalis strains of fsr+ and fsr− groups. We took the blank well as the control and divided the OD value (590 nm) of the above-described 6 strains by it to obtain the multiples. Each experiment was replicated four times. **, P < 0.01 (Student’s t test).
DISCUSSION
The genomic characteristics of E. faecalis isolates obtained from different sources showed high diversity. The blood-derived E. faecalis had a larger genome size but a lower GC content. As expected from the more antibiotic resistance genes, the newly identified fsr cluster in this study revealed a key mechanism associated with E. faecalis invasiveness. Meanwhile, it provided a new method of exploring E. faecalis colonization during infection.
The catheter-related blood infection-isolated E. faecalis could produce more biofilm, indicating that biofilm formation is an important factor causing blood infection, but the mechanism has not been thoroughly explored (16). In this study, we identified strains containing fsr that could enhance the blood infectivity of colonized E. faecalis. First, the blood-isolated E. faecalis had higher fsr abundance; second, the fsr-positive strains could produce more biofilm than fsr-negative strains; finally and most importantly, the fsr-positive E. faecalis could better resist the attack of immune factors in the normal human serum.
The quorum-sensing system encoded by fsrA, fsrB, and fsrC could be an important toxicity determinant in E. faecalis contributing to its virulence, host tissue degradation, and biofilm formation (20). Gelatinase and serine protease, which are encoded by gelE and sprE, respectively, are positively regulated by the fsr quorum-sensing system. The interaction between the quorum-sensing system and its regulated molecules was subject to further analysis, especially during bacterial infections. In this study, we identified that the proportions of fsr-positive, -partial, and -negative clusters in blood-derived E. faecalis strains were 76%, 10%, and 14%, respectively. More importantly, the fsr-positive cluster exhibited the highest percentage of strains from blood among all groups. This suggests that the fsr quorum-sensing system and its regulated enzymes both play key roles in the colonization and invasiveness of E. faecalis in the blood.
Moreover, the interaction between microbes and the immune system is crucial for the prevention and treatment of multidrug-resistant pathogens. Previous studies revealed that human serum specifically killed commensal E. faecium strains isolated from normal gut microbiota but not clinical isolates, suggesting that some differences in gene expression and/or gene content were associated with serum resistance between commensal and clinical strains in Enterococcus (22). Our study reveals that the fsr gene could be the key factor for resistance to serum killing in clinical strains and could specifically induce E. faecalis to invade the bloodstream.
Conclusions.
In summary, our data demonstrated that there are significant differences in the genomic characteristics of E. faecalis strains from varied tissue niches, especially between strains from the blood and GI tract. Our results also suggested that the fsr quorum-sensing system had a larger proportion in blood-derived strains than others and could promote colonization through host immune evasion in E. faecalis. Future studies will provide further insights into both new and known pathogenic mechanisms of infections in E. faecalis.
MATERIALS AND METHODS
Bacterial strains.
We enrolled E. faecalis strains collected by the clinical laboratory of Beijing Ditan Hospital from 2011 to 2014. The 52 isolates were first identified by Vitek 2, after which the classification was validated by 16S rRNA amplification and sequencing. The 485 E. faecalis genomes available by September 2020 were downloaded from the GenBank database (www.ncbi.nlm.nih.gov/genbank/) at the National Center for Biotechnology Information (NCBI).
Total DNA extraction, sequencing, assembly, and annotation.
The strains were cultured, and the total DNA was extracted using a genomic DNA kit (Qiagen, USA) according to the manufacturer’s instructions. Sequencing libraries were generated using the NEBNext Ultra DNA library prep kit for Illumina (NEB, USA), and the whole genome of the 52 strains was sequenced using the Illumina NovaSeq platform. The clean read data were checked using FastQC software (23). The paired-end reads were assembled de novo using SPAdes v3.13.0 (24). The assembled genomes were annotated using Prokka software (25).
Phylogenetic analysis.
The assembly genomes of 537 strains were mapped to the complete genome sequence of E. faecalis strain V583 (GenBank accession number AE016830), and all single nucleotide polymorphisms (SNPs) were identified by KSNP3 software (26). The above-described variant sites were retained, and the sequence of these sites in each strain was concatenated and aligned and then used for phylogenetic analysis using FastTree v2.1.11 (27) with the maximum-likelihood method.
Multilocus sequence typing.
Multilocus sequence typing (MLST) was conducted by the reference method (28). The genome sequences were compared with the nucleotide sequences of housekeeping genes (gdh, gyd, pstS, gki, aroE, xpt, and yqiL) in the MLST database (https://pubmlst.org/organisms/enterococcus-faecalis) to determine the number of alleles and assigned STs. The genetic relationship among strains was analyzed by constructing a minimal spanning tree using Phyloviz software (29).
Construction of the accessory genome.
The pan-genome was analyzed using Roary software (30). The pan-genome was constructed by counting the total number of nonredundant gene families within the complete data set. The core genome was constructed by counting the total number of gene families commonly shared by all genomes (99% ≤ strains ≤ 100%). The accessory genome was the result of subtracting the core genome from the pan-genome.
Identification of virulence genes.
A BLASTN search was performed with all predicted genes from the 537 strains against the Virulence Factor Database (VFDB; http://www.mgc.ac.cn/VFs/main.htm) to identify potential virulence genes (sequence identity ≥ 80% and coverage ≥ 80%).
Identification of antibiotic resistance genes.
Antimicrobial resistance genes were identified by performing searches using ABRicate software (31) (https://github.com/tseemann/abricate) (sequence identity ≥ 80% and coverage ≥ 80%).
Identification of plasmids, phages, and mobile genetic elements.
Plasmids, phages, and mobile genetic elements were identified by performing searches using by PlasmidFinder (32), PhageMiner (33), and MobileElementFinder (34) (sequence identity ≥ 80% and coverage ≥ 80%).
Serum bactericidal assay.
The E. faecalis strains were cultured at 37°C in brain heart infusion (BHI) medium (Oxoid Ltd., United Kingdom); E. faecalis ATCC 19433 was selected as control. The overnight bacterial cultures were diluted with fresh BHI medium to adjust the concentration to 1 × 106 CFU/mL (optical density at 600 nm [OD600] = 1). For the serum bactericidal assay, 2 × 105 CFU (25 μL) of the E. faecalis strains was mixed with 75 μL normal human serum, and then the mixture was incubated at 37°C. This mixture suspension was made in triplicate. Next, 100-fold serial dilutions from each suspension were obtained at 0, 1, and 3 h, respectively, and then coated on BHI medium. Surviving bacteria were quantified by counting the CFU after incubation at 37°C for 24 h. Each experiment was replicated three times.
Biofilm formation assay.
The biofilm formation assay was performed on the above-described seven strains. The biofilm formation assay was measured using the method of Wilksch et al. (35, 36). Clinical isolates were grown to the logarithmic phase in BHI broth and diluted into phosphate-buffered saline (PBS) to adjust the concentration to 1 × 106 CFU/mL (OD600 = 1). The bacteria solution was diluted 1:20 into fresh BHI broth. A total of 200 μL of each dilution was added to a 96-well polystyrene microtiter plate, and blank controls were set. Three duplicate wells were set for each strain. Then, the plate was incubated at 37°C for 24 h. Planktonic cells were removed, and the wells were washed three times with sterile water, stained with 200 μL 0.1% crystal violet for 20 min, and rinsed three times with sterile water. Stained biofilms were solubilized with 95% (vol/vol) ethanol and quantified by measuring the OD590. Each sample was measured in triplicate, and the average absorbance values were used for analysis. The experiment was repeated four times for each strain.
Statistical analysis.
Parametric data were analyzed for significance by Student's t test and one-way analysis of variance (ANOVA) test, and P values of ≤0.05 were considered statistically significant. Nonparametric data were analyzed by the Kruskal-Wallis test or Wilcoxon test, depending on the number of variables. Correlations, chi-square, and Fisher’s exact tests were calculated in R (https://www.r-project.org/). In most instances, presence and absence values were converted into pseudonumeric variables (with 1 and 0) to compute Pearson correlation coefficients and create a correlation matrix.
Ethical statement.
All the investigation protocols in this study were approved by the Ethics Committee of the Affiliated Beijing Shijitan Hospital of Capital Medical University. Informed consent was waived because this study with an observational approach had mainly focused on bacteria and involved no interventions for the healthy donators.
Data availability.
All BioSample accession numbers related to data from 485 E. faecalis-assembled genomes from the GenBank database at the NCBI that were used in this study are listed in Table S1 in the supplemental material. FASTQ sequences of 52 clinical genomes sequenced in this study were deposited in the National Genomics Data Center (NGDC; https://ngdc.cncb.ac.cn/), under the BioProject accession number PRJCA006264. The BioSample accession numbers (SAMC447783 to SAMC447834) are also listed in Table S1.
ACKNOWLEDGMENTS
We give special thanks to the uploaders of public genome data from the NCBI database used in our study.
Chen Chen designed the study; Jinglin Yue and Mingxi Hua engaged in the acquisition, analysis, and interpretation of the data; and Nan Chen, Jiarui Li, Ang Duan, Xinzhe Liu, Huizhu Wang, Pengcheng Du, and Chengbo Rong participated in data acquisition and analysis. Jinglin Yue drafted the article, and all the authors had final approval of the version to be submitted.
We declare that there are no conflicts of interest.
This work was supported by grants from the National Natural Science Foundation of China (81902025), the National Key Research and Development Project of China (2018YFE9102500), and Beijing Municipal Science & Technology Commission nos. Z201100005520040 and Z201100005520035.
Footnotes
Supplemental material is available online only.
Contributor Information
Chen Chen, Email: chenchen1@ccmu.edu.cn.
Christopher A. Elkins, Centers for Disease Control and Prevention
REFERENCES
- 1.Garcia-Solache M, Rice LB. 2019. The Enterococcus: a model of adaptability to its environment. Clin Microbiol Rev 32:e00058-18. doi: 10.1128/CMR.00058-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Billington EO, Phang SH, Gregson DB, Pitout JD, Ross T, Church DL, Laupland KB, Parkins MD. 2014. Incidence, risk factors, and outcomes for Enterococcus spp. blood stream infections: a population-based study. Int J Infect Dis 26:76–82. doi: 10.1016/j.ijid.2014.02.012. [DOI] [PubMed] [Google Scholar]
- 3.Noskin GA, Peterson LR, Warren JR. 1995. Enterococcus faecium and Enterococcus faecalis bacteremia: acquisition and outcome. Clin Infect Dis 20:296–301. doi: 10.1093/clinids/20.2.296. [DOI] [PubMed] [Google Scholar]
- 4.Mendes RE, Sader HS, Castanheira M, Flamm RK. 2018. Distribution of main Gram-positive pathogens causing blood infections in United States and European hospitals during the SENTRY Antimicrobial Surveillance Program (2010–2016): concomitant analysis of oritavancin in vitro activity. J Chemother 30:280–289. doi: 10.1080/1120009X.2018.1516272. [DOI] [PubMed] [Google Scholar]
- 5.Suppli M, Aabenhus R, Harboe ZB, Andersen LP, Tvede M, Jensen J-US. 2011. Mortality in enterococcal blood infections increases with inappropriate antimicrobial therapy. Clin Microbiol Infect 17:1078–1083. doi: 10.1111/j.1469-0691.2010.03394.x. [DOI] [PubMed] [Google Scholar]
- 6.Bar K, Wisplinghoff H, Wenzel RP, Bearman GM, Edmond MB. 2006. Systemic inflammatory response syndrome in adult patients with nosocomial blood infections due to enterococci. BMC Infect Dis 6:145. doi: 10.1186/1471-2334-6-145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Patterson JE, Sweeney AH, Simms M, Carley N, Mangi R, Sabetta J, Lyons RW. 1995. An analysis of 110 serious enterococcal infections. Epidemiology, antibiotic susceptibility, and outcome. Medicine (Baltimore, MD) 74:191–200. doi: 10.1097/00005792-199507000-00003. [DOI] [PubMed] [Google Scholar]
- 8.Kao PHN, Kline KA. 2019. Dr. Jekyll and Mr. Hide: how Enterococcus faecalis subverts the host immune response to cause infection. J Mol Biol 431:2932–2945. doi: 10.1016/j.jmb.2019.05.030. [DOI] [PubMed] [Google Scholar]
- 9.Royer G, Roisin L, Demontant V, Lo S, Coutte L, Lim P, Pawlotsky JM, Jacquier H, Lepeule R, Rodriguez C, Woerther PL. 2021. Microdiversity of Enterococcus faecalis isolates in cases of infective endocarditis: selection of non-synonymous mutations and large deletions is associated with phenotypic modifications. Emerg Microbes Infect 10:929–938. doi: 10.1080/22221751.2021.1924865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pontinen AK, Top J, Arredondo-Alonso S, Tonkin-Hill G, Freitas AR, Novais C, Gladstone RA, Pesonen M, Meneses R, Pesonen H, Lees JA, Jamrozy D, Bentley SD, Lanza VF, Torres C, Peixe L, Coque TM, Parkhill J, Schurch AC, Willems RJL, Corander J. 2021. Apparent nosocomial adaptation of Enterococcus faecalis predates the modern hospital era. Nat Commun 12:1523. doi: 10.1038/s41467-021-21749-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Zheng JX, Bai B, Lin ZW, Pu ZY, Yao WM, Chen Z, Li DY, Deng XB, Deng QW, Yu ZJ. 2018. Characterization of biofilm formation by Enterococcus faecalis isolates derived from urinary tract infections in China. J Med Microbiol 67:60–67. doi: 10.1099/jmm.0.000647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zheng JX, Wu Y, Lin ZW, Pu ZY, Yao WM, Chen Z, Li DY, Deng QW, Qu D, Yu ZJ. 2017. Characteristics of and virulence factors associated with biofilm formation in clinical Enterococcus faecalis isolates in China. Front Microbiol 8:2338. doi: 10.3389/fmicb.2017.02338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Solheim M, Aakra A, Snipen LG, Brede DA, Nes IF. 2009. Comparative genomics of Enterococcus faecalis from healthy Norwegian infants. BMC Genomics 10:194. doi: 10.1186/1471-2164-10-194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jett BD, Huycke MM, Gilmore MS. 1994. Virulence of enterococci. Clin Microbiol Rev 7:462–478. doi: 10.1128/CMR.7.4.462. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Jamal M, Ahmad W, Andleeb S, Jalil F, Imran M, Nawaz MA, Hussain T, Ali M, Rafiq M, Kamil MA. 2018. Bacterial biofilm and associated infections. J Chin Med Assoc 81:7–11. doi: 10.1016/j.jcma.2017.07.012. [DOI] [PubMed] [Google Scholar]
- 16.Roilides E, Simitsopoulou M, Katragkou A, Walsh TJ. 2015. How biofilms evade host defenses. Microbiol Spectr 3. doi: 10.1128/microbiolspec.MB-0012-2014. [DOI] [PubMed] [Google Scholar]
- 17.Paganelli FL, Willems RJ, Leavis HL. 2012. Optimizing future treatment of enterococcal infections: attacking the biofilm? Trends Microbiol 20:40–49. doi: 10.1016/j.tim.2011.11.001. [DOI] [PubMed] [Google Scholar]
- 18.Hoiby N, Ciofu O, Johansen HK, Song ZJ, Moser C, Jensen PO, Molin S, Givskov M, Tolker-Nielsen T, Bjarnsholt T. 2011. The clinical impact of bacterial biofilms. Int J Oral Sci 3:55–65. doi: 10.4248/IJOS11026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Hashem YA, Abdelrahman KA, Aziz RK. 2021. Phenotype–genotype correlations and distribution of key virulence factors in Enterococcus faecalis isolated from patients with urinary tract infections. Infect Drug Resist 14:1713–1723. doi: 10.2147/IDR.S305167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ali L, Goraya MU, Arafat Y, Ajmal M, Chen J-L, Yu D. 2017. Molecular mechanism of quorum-sensing in Enterococcus faecalis: its role in virulence and therapeutic approaches. Int J Mol Sci 18:960. doi: 10.3390/ijms18050960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Shojima A, Nakayama J. 2014. Quorum sensing in Gram-positive bacteria: assay protocols for staphylococcal agr and enterococcal fsr systems. Methods Mol Biol 1147:33–41. doi: 10.1007/978-1-4939-0467-9_3. [DOI] [PubMed] [Google Scholar]
- 22.Paganelli FL, Leavis HL, He S, van Sorge NM, Payre C, Lambeau G, Willems RJL, Rooijakkers SHM. 2018. Group IIA-secreted phospholipase A2 in human serum kills commensal but not clinical Enterococcus faecium isolates. Infect Immun 86:e00180-18. doi: 10.1128/IAI.00180-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Leggett RM, Ramirez-Gonzalez RH, Clavijo BJ, Waite D, Davey RP. 2013. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Front Genet 4:288. doi: 10.3389/fgene.2013.00288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069. doi: 10.1093/bioinformatics/btu153. [DOI] [PubMed] [Google Scholar]
- 26.Gardner SN, Slezak T, Hall BG. 2015. kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome. Bioinformatics 31:2877–2878. doi: 10.1093/bioinformatics/btv271. [DOI] [PubMed] [Google Scholar]
- 27.Price MN, Dehal PS, Arkin AP. 2009. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26:1641–1650. doi: 10.1093/molbev/msp077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ruiz-Garbajosa P, Bonten MJ, Robinson DA, Top J, Nallapareddy SR, Torres C, Coque TM, Canton R, Baquero F, Murray BE, del Campo R, Willems RJ. 2006. Multilocus sequence typing scheme for Enterococcus faecalis reveals hospital-adapted genetic complexes in a background of high rates of recombination. J Clin Microbiol 44:2220–2228. doi: 10.1128/JCM.02596-05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Nascimento M, Sousa A, Ramirez M, Francisco AP, Carrico JA, Vaz C. 2017. PHYLOViZ 2.0: providing scalable data integration and visualization for multiple phylogenetic inference methods. Bioinformatics 33:128–129. doi: 10.1093/bioinformatics/btw582. [DOI] [PubMed] [Google Scholar]
- 30.Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, Fookes M, Falush D, Keane JA, Parkhill J. 2015. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31:3691–3693. doi: 10.1093/bioinformatics/btv421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Seemann TA. 2020. Github. https://github.com/tseemann/abricate.
- 32.Carattoli A, Hasman H. 2020. PlasmidFinder and in silico pMLST: identification and typing of plasmid replicons in whole-genome sequencing (WGS). Methods Mol Biol 2075:285–294. doi: 10.1007/978-1-4939-9877-7_20. [DOI] [PubMed] [Google Scholar]
- 33.Rezaei Javan R, Ramos-Sevillano E, Akter A, Brown J, Brueggemann AB. 2019. Prophages and satellite prophages are widespread in Streptococcus and may play a role in pneumococcal pathogenesis. Nat Commun 10:4852. doi: 10.1038/s41467-019-12825-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Johansson MHK, Bortolaia V, Tansirichaiya S, Aarestrup FM, Roberts AP, Petersen TN. 2021. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J Antimicrob Chemother 76:101–109. doi: 10.1093/jac/dkaa390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Wilksch JJ, Yang J, Clements A, Gabbe JL, Short KR, Cao H, Cavaliere R, James CE, Whitchurch CB, Schembri MA, Chuah ML, Liang ZX, Wijburg OL, Jenney AW, Lithgow T, Strugnell RA. 2011. MrkH, a novel c-di-GMP-dependent transcriptional activator, controls Klebsiella pneumoniae biofilm formation by regulating type 3 fimbriae expression. PLoS Pathog 7:e1002204. doi: 10.1371/journal.ppat.1002204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Singh KV, Nallapareddy SR, Nannini EC, Murray BE. 2005. Fsr-independent production of protease(s) may explain the lack of attenuation of an Enterococcus faecalis fsr mutant versus a gelE-sprE mutant in induction of endocarditis. Infect Immun 73:4888–4894. doi: 10.1128/IAI.73.8.4888-4894.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental methods, Fig. S1 and S2, and legend for Table S1. Download aem.01551-22-s0001.pdf, PDF file, 1.4 MB (1.4MB, pdf)
Table S1. Download aem.01551-22-s0002.xlsx, XLSX file, 0.5 MB (502.7KB, xlsx)
Data Availability Statement
All BioSample accession numbers related to data from 485 E. faecalis-assembled genomes from the GenBank database at the NCBI that were used in this study are listed in Table S1 in the supplemental material. FASTQ sequences of 52 clinical genomes sequenced in this study were deposited in the National Genomics Data Center (NGDC; https://ngdc.cncb.ac.cn/), under the BioProject accession number PRJCA006264. The BioSample accession numbers (SAMC447783 to SAMC447834) are also listed in Table S1.