Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies

Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies View ORCID Profile Vitor Luis Macena Leite , Adriana Rocha Faria , Clara Ferreira Guerra , View ORCID Profile Stephanie da Silva Rodrigues Souza , Andréa de Andrade Rangel Freitas , Jaqueline Martins Morais , Vânia Lúcia Carreira Merquior , View ORCID Profile Paul J. Planet , View ORCID Profile Lúcia Martins Teixeira doi: https://doi.org/10.1101/2025.05.05.652174 Vitor Luis Macena Leite a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vitor Luis Macena Leite Adriana Rocha Faria a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil b Faculdade de Medicina, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clara Ferreira Guerra a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil b Faculdade de Medicina, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephanie da Silva Rodrigues Souza d Department of Biological Sciences, University at Albany, State University of New York , Albany, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephanie da Silva Rodrigues Souza Andréa de Andrade Rangel Freitas a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jaqueline Martins Morais a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil c Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vânia Lúcia Carreira Merquior c Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul J. Planet e Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA f Pediatric Infectious Disease Division, Children’s Hospital of Philadelphia , Philadelphia, Pennsylvania, USA g Institute for Comparative Genomics, American Museum of Natural History , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul J. Planet Lúcia Martins Teixeira a Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro , Rio de Janeiro, Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lúcia Martins Teixeira For correspondence: lmt2{at}micro.ufrj.br Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Enterococcus faecalis is a commensal bacterium that colonizes the gut of humans and animals, and a major opportunistic pathogen, known for causing multidrug-resistant healthcare-associated infections (HAIs). Its ability to thrive in diverse environments and disseminate antimicrobial resistance genes (ARGs) across ecological niches highlights the importance of understanding its ecological, evolutionary, and epidemiological dynamics. The CRISPR2 locus has been used as a valuable marker for assessing clonality and phylogenetic relationships in E. faecalis . In this study, we identified a group of E. faecalis strains lacking CRISPR2, forming a distinct, well-supported clade. We demonstrate that this clade meets the genomic criteria for classification as a novel subspecies, here referred to as “subspecies B”. Through a comprehensive pangenome analysis and comparative genomics, we explored the adaptive ecological traits underlying this diversification process, identifying clade-specific features and their predicted functional roles. Our findings suggest that the frequent isolation of subspecies B from meat products and processing facilities may reflect dissemination routes involving environmental contamination (e.g., water, plants, soil) from avian species. The absence of key virulence traits required for pathogenicity in mammals, particularly in humans, and the lack of clinically relevant resistance determinants indicate that subspecies B may currently pose minimal threat to public health compared to the broadly disseminated “subspecies A”. Nevertheless, the unclear potential for genetic exchange between these subspecies, and the frequent association of subspecies B with food sources, calls for continued genomic surveillance of E. faecalis from a One Health perspective to detect and mitigate the emergence of high-risk variants in advance. INTRODUCTION Enterococcus faecalis is a Gram-positive commensal bacterium commonly found colonizing the gastrointestinal tract (GIT) of humans and other animals ( 1 ). Its broad distribution, extending to soil, water, and other natural sources ( 2 ), is attributed to its exceptional tolerance to adverse environmental conditions, including variations in temperature, pH, and exposure to antimicrobials ( 3 , 4 ). As an opportunistic pathogen, E. faecalis is a major cause of healthcare-associated infections (HAIs) globally, mainly due to its remarkable ability to acquire multiple antimicrobial resistance genes (ARGs) through horizontal gene transfer (HGT), hampering treatment outcomes ( 5 , 6 ). A key factor in the genomic plasticity of E. faecalis is its ability to accumulate mobile genetic elements (MGEs), a process counterbalanced by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and their associated Cas proteins (CRISPR-Cas) ( 5 ). The CRISPR-Cas system functions as a bacterial adaptive immune mechanism, protecting against bacteriophage infections and limiting the uptake of exogenous DNA, including plasmids and other MGEs ( 7 ). The system’s activity relies on two core components: (i) the CRISPR array, composed of alternating direct repeats and short spacer sequences derived from foreign DNA, and (ii) Cas proteins, which mediate the recognition and cleavage of invading genetic material ( 7 ). Three CRISPR loci have been described in E. faecalis : CRISPR1-Cas and CRISPR3-Cas, both predicted to be functional, and CRISPR2, an orphan locus lacking associated cas genes, rendering it incapable of defense against foreign DNA ( 5 ). Despite this, CRISPR2 has been widely regarded as a conserved feature of the E. faecalis genome, found in most isolates and considered part of the species’ core genome ( 8 ). This raises intriguing questions about its evolutionary maintenance, particularly given to evidence suggesting a potential regulatory role for CRISPR2 as a noncoding RNA ( 9 ). Indeed, transcriptomic analyses have confirmed CRISPR2 transcription, though its precise function remains unconfirmed ( 10 , 11 ). Previous studies have explored the discriminatory power of CRISPR2 arrays to assess E. faecalis genetic diversity ( 12 – 14 ). Given the system’s ubiquity and the variability of spacer sequences, CRISPR2 has been proposed as a useful marker for strain typing, especially in studies with limited resources where more comprehensive methods like whole-genome sequencing are not feasible. Notably, Hullahalli and colleagues (2015) demonstrated the value of CRISPR2 typing in providing additional phylogenetic resolution when coupled with multilocus sequence typing (MLST). However, while CRISPR2-based methods have proven valuable for investigating population diversity, studies have not yet explored potential associations between unique CRISPR2 signatures and ecological variation within closely related strains, especially in ubiquitous E. faecalis sequence-types (STs), such as STs 16, 21, and 40 ( 14 , 15 ). Given the generalist nature of E. faecalis and its exposure to diverse ecological pressures beyond healthcare settings, it is crucial to explore its population structure through molecular epidemiology approaches. Studies comprising isolates from a range of environments, host species, and geographical regions will deepen our understanding of the ecological and evolutionary dynamics shaping E. faecalis diversity ( 16 ). Thus, it is extremely worthy to explore inexpensive and feasible approaches providing complementary resolution to conventional typing methods (e.g., MLST), enabling the discrimination of subpopulations in early stages of ecological differentiation, which tend to be indistinguishable by multilocus sequence analyses targeting housekeeping genes ( 17 , 18 ). Initially, this study aimed to explore CRISPR2 sequence variability across a comprehensive dataset of E. faecalis genomes representing multiple STs and diverse isolation sources. However, an unexpected finding shifted the focus of our research. A subset of isolates from genetically related STs was found to completely lack CRISPR2. This prompted us to reframe the study with two main objectives. First, we aimed to validate whether this CRISPR2-negative group constitutes a cohesive unit of bacterial diversity, distinct both genetically and ecologically, while also clarifying its taxonomic and phylogenetic position in relation to well-characterized E. faecalis lineages. Second, through comparative pangenomic analysis, we sought to identify the differential genetic content between both groups and discuss potential selective pressures driving cladogenesis within E. faecalis . This genome-based approach offers novel insights into the complexity of E. faecalis population structure and niche breadth, identifying key areas for future studies on the ecological and evolutionary dynamics underlying genome divergence in this species. MATERIALS AND METHODS Enterococcus faecalis GENOMIC DATA Initially, this study included 71 whole genome sequences of E. faecalis strains from our local bacterial culture collection, selected based on data availability rather than specific inclusion criteria. These strains were originally isolated from diverse sources, primarily in the state of Rio de Janeiro, Brazil, including 60 from hospitalized patients, 8 from coastal waters, and 3 from wild birds admitted to wildlife rehabilitation centers, with isolation dates ranging from 2005 to 2017. At this preliminary stage, the goal was to explore CRISPR2 sequence variability across a broad representation of E. faecalis genomes, without pre-established hypotheses regarding the ecological or clinical origins of the isolates. Genomic DNA extraction, purification, and sequencing followed a previously described protocol ( 19 ), using the Illumina HiSeq 2500 platform (Illumina Inc., San Diego, CA, USA). Information on local bacterial strains and access details for their corresponding raw sequencing data are provided in the S1 Dataset . De novo genome assemblies were performed with the Unicycler v0.5.0 pipeline ( 20 ) on the BV-BRC suite ( 21 ), with default parameters and automatic read trimming by Trim Galore v0.6.5 ( 22 ). In addition, 1,464 complete and draft E. faecalis genomes, available in GenBank until April 1, 2020, were downloaded for further analysis. All genomes were assigned sequence types (STs) using in silico MLST based on PubMLST typing schemes ( 23 , 24 ). Targeted searches for genomes representing specific STs, flagged as relevant based on initial findings, were conducted in the updated GenBank database (up to May 30, 2022), resulting in the inclusion of three additional E. faecalis genomes isolated between 2015 and 2019. Throughout this study, specific subsets of genomes were analyzed to address emerging research questions as new patterns were identified. The rationale for these subsets is detailed alongside the corresponding results to provide contextual clarity and maintain a logical flow of information. All E. faecalis genomes analyzed in this study, including strain metadata and accession numbers are shown in S1 Dataset . Genome annotation was carried out using Prokka v1.14.5 ( 25 ). CRISPR SCREENING All E. faecalis genomes were screened for the presence of CRISPR2 loci using two complementary approaches: the CRISPRCasFinder online tool ( 26 ) and an in silico PCR program described in reference 27, employing previously described primer sets ( 28 ). CRISPRCasFinder detects candidate CRISPR- cas systems within a query genome, allowing to distinguish CRISPR2 loci from other E. faecalis CRISPR systems (CRISPR1- cas and CRISPR3- cas ) using two criteria: (i) the presence of signature direct repeats ( 28 ); and ii) the absence of adjacent cas genes. Functional CRISPR- cas loci (CRISPR1- cas and CRISPR3- cas ) identified by CRISPRCasFinder were also noted for subsequent analysis. For CRISPR2 detection via in silico PCR, any amplification product was considered indicative of the locus’s presence, regardless of size, as the primers were designed to target its conserved flanking regions in the chromosome ( 28 ). This differs from the detection of CRISPR1- cas and CRISPR3- cas systems, which relies on primers targeting the cas genes. The variation in product size across strains reflects the number of spacers within the CRISPR2 array. CRISPR2 absence was confirmed only when both screening methods (CRISPRCasFinder and in silico PCR) failed to detect the array. DETECTION OF ANTIMICROBIAL RESISTANCE AND VIRULENCE-ASSOCIATED GENES Genomes of interest were screened for ARGs and virulence genes against the Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca ) ( 29 ) and the Virulence Factor Database (VFDB; http://www.mgc.ac.cn/VFs/ ) ( 30 ), respectively, using ABRicate v1.0.0 ( 31 ) with default parameters. GENOME-BASED TAXONOMY Taxonomic confirmation of the genomes of interest was conducted using the Type (Strain) Genome Server (TYGS), a high-throughput platform designed for genome-based taxonomic analysis ( 32 , 33 ). This platform is interconnected with the List of Prokaryotic Names with Standing in Nomenclature (LPSN) database ( 34 ). The algorithm assigns each query genome to its corresponding species and subspecies based on established digital DNA:DNA hybridization (dDDH) thresholds — 70% for species delineation and 70-80% for subspecies — ( 35 , 36 ), using pairwise comparisons against the closest type strains in the TYGS database. Using FastME 2.1.6.1 ( 37 ), TYGS also provides a genome-scale phylogeny based on the Genome BLAST Distance Phylogeny method (GBDP), including the query strains and the automatically selected closest type strains, providing branch support values and a treelikeness indicator ( 32 , 33 ). ORTHOLOGY INFERENCE AND INTERSPECIFIC Enterococcus spp. PHYLOGENY To evaluate the population structure of E. faecalis within a broader evolutionary framework, we selected 118 genomes for phylogenetic analysis. This dataset included 55 E. faecalis genomes representing the species’ genetic diversity within the scope of this study, four well-characterized E. faecalis reference genomes (OG1RF, V583, T5), including the type strain (ATCC 19433 = NBRC 100480), 58 genomes representing other validly published Enterococcus species (as of June 13, 2024), and the Vagococcus fluvialis DSM 5731 genome as an outgroup. The selection of E. faecalis genomes was based on initial genomic and taxonomic analyses, which are detailed in the Results section. Accession numbers, strain names, and taxonomic information for all 59 non- E. faecalis genomes are provided in S2 Dataset . Of these, 54 were type strains available in GenBank as of the search date, with exceptions also noted in S2 Dataset. To identify orthologous gene groups (orthogroups), we used OrthoFinder (v2.5.5) with the Multiple Sequence Alignment (MSA) option (“-M msa”) across the 118 genomes ( 38 , 39 ). OrthoFinder’s relaxed approach, which allows for the inclusion of single-copy orthogroups present in the majority of genomes rather than strictly in all genomes, is particularly suited for highly divergent species ( 38 , 39 ). This relaxed data criterion improves the phylogenetic signal by incorporating genes that, while not universally present, are conserved in a large proportion of genomes [Further details on the basis for this approach are described in method’s paper ( 39 )]. Consequently, 751 orthogroups were selected, each containing single-copy genes present in at least 95.8% of the genomes. By default, the concatenated MSA of these orthogroups was then generated using MAFFT-linsi ( 40 ). The maximum-likelihood phylogenetic tree was constructed using IQ-TREE v2.3.5 ( 41 ). ModelFinder Plus ( 42 ) was employed to automatically determine the best-fit amino acid substitution model (LG+F+I+R9), via the “-m MFP” option in IQ-TREE. Branch support was assessed with 1,000 ultrafast bootstrap replicates with UFBoot2 ( 43 ). The resulting phylogeny was visualized and customized using iTOL ( 44 ). E. faecalis PANGENOME ANALYSIS AND INTRASPECIFIC PHYLOGENY Pangenome analysis of the 55 E. faecalis genomes, representing the species’ genetic diversity within the scope of this study (see S1 Dataset ), was conducted using Roary v3.13.0 ( 45 ). Out of the 9,950 orthologous gene clusters identified, 1,650 were predicted as core genes (i.e., present in all genomes), and these were then used to build a core gene alignment with MAFFT v7.477 ( 40 ). Single nucleotide polymorphisms (SNPs) were extracted from the alignment using SNP-sites v2.5.1 ( 46 ), and then used as input for maximum-likelihood tree inference by IQ-TREE ( 41 ) under the generalized time reversible (GTR) model of nucleotide substitution with the Gamma model of rate heterogeneity. Branch support was assessed with 10,000 ultrafast bootstrap replicates using UFBoot2 ( 43 ). The resulting phylogeny was rooted at the midpoint and customized using iTOL ( 44 ). ANALYSIS OF CLADE-LEVEL ENRICHED GENES Based on the E. faecalis pangenome analysis described in the previous topic, we used Scoary v1.6.16 ( 47 ) to identify genes whose frequencies differed significantly between two groups, hereafter referred to as “subspecies A” and “subspecies B”—arbitrary labels employed in this study solely to facilitate comparisons—indicating enrichment or depletion in either group. Genes were considered significantly associated with a given subspecies if they had a P-value of less than 0.05 after applying the Benjamini-Hochberg correction for multiple comparisons ( 47 ). The genes enriched in each subspecies were then classified into their respective COG functional categories ( 48 , 49 ) using COGClassifier v1.0.5 ( 50 ) for a broader functional comparison approach. To further assess differences in the distribution of functional categories between subspecies, we applied the chi-squared test, with statistical significance defined as P-value < 0.01. ANALYSIS OF NICHE-SPECIFYING GENES To identify candidate genes potentially involved in niche differentiation between E. faecalis subspecies, we filtered gene clusters that were consistently present in all genomes of one subspecies while absent in the other. These clade-specific genes likely encode functions central to the ecological adaptation of the subspecies, differentiating them from their nearest relatives ( 17 , 51 ). Due to the frequent occurrence of inaccurate or incomplete annotations in these genes, initially annotated using Prokka v1.14.5 ( 25 ), we performed a secondary functional analysis on representative protein sequences from each clade-specific gene cluster, as identified in the Roary output files. Representative protein sequences were re-annotated through BLASTp ( 52 ) searches against the UniProtKB database ( 53 ), including reference proteomes and entries from TrEMBL and Swiss-Prot (as of April 2023). Homology was inferred based on sequence similarity, with a cutoff of E-value < 1e-6 ( 54 ). Additionally, protein sequences were further characterized using the InterProScan tool ( 55 ), which facilitated the identification of conserved domains, active sites, and other protein signatures of known biological function, thus providing insights into potential functional roles of these niche-specifying genes. DATA AVAILABILITY The raw sequence reads for the local E. faecalis strains have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession numbers PRJNA1186978, PRJNA695567, and PRJNA503970. Genome assemblies analyzed in this study include these local strains and publicly available assemblies from GenBank, with accession numbers provided in the S1 and S2 Datasets. RESULTS DETECTION OF E. faecalis STRAINS LACKING CRISPR2: GENETIC DIVERSITY, ISOLATION SOURCES AND CRISPR SYSTEM PROFILES We detected the presence of CRISPR2 loci in the vast majority of E. faecalis strains analyzed in this study ( Fig. 1 ). All 71 draft genomes from our collection, representing isolates across 16 distinct STs, were CRISPR2-positive based on both in silico PCR screening and CRISPRCasFinder predictions. Similarly, CRISPR2 was identified by at least one of these methodologies in 98.3% (1439 out of 1464) of the E. faecalis genomes obtained from GenBank. These publicly available genomes corresponded to 208 distinct STs registered in the PubMLST database (see S1 Dataset ). Download figure Open in new tab Fig. 1 CRISPR2 status in E. faecalis genomic samples. The main pie chart categorizes the total genomes (n=1535) into CRISPR2-positive and CRISPR2-negative. The secondary pie chart further classifies CRISPR2-negative genomes into MLST allelic profile-related strains and others. * The category “Others” includes genomes with no evident close relationship within the group based on MLST allelic profiles; genomes with statistics indicating low-quality sequencing or assembly; and genomes removed from RefSeq due to various inconsistencies. Interestingly, out of the remaining 25 genomes where CRISPR2 was not detected by any method, 13 were genetically related, sharing MLST alleles for specific genes. The majority of these were classified as members of ST228, followed by ST624, ST1468, and one strain with an allelic profile not yet registered in the PubMLST database (as of October 2024), which we refer to as “STx” for the purpose of this study (see Table 1 ). View this table: View inline View popup Download powerpoint Table 1 Sampling details and MLST allelic profiles of the 16 Enterococcus faecalis strains lacking CRISPR2 The remaining 12 genomes presented several inconsistencies that cast doubt on the reliability of CRISPR2 absence. These genomes were genetically unrelated to each other and to the group of 13 closely related strains, as determined by their distinct MLST allelic profiles (see S1 Dataset). Moreover, many exhibited issues such as low sequencing or assembly quality, inconclusive CRISPR predictions, or had been flagged for removal from RefSeq due to various discrepancies. Given these concerns, it is highly likely that the absence of CRISPR2 in these genomes was an artifact of poor-quality data rather than a true biological feature. Consequently, we excluded these 12 genomes from further analysis and focused exclusively on the 13 genetically related strains that consistently lacked CRISPR2 based on results of both screening methods. To expand our dataset and investigate these CRISPR2-negative strains further, we conducted a targeted search for additional genomes deposited in GenBank after our initial analysis. This search focused on genomes from the same sequence types (ST228, ST624, ST1468, and STx) or closely related allelic profiles. As a result, three newly deposited genomes belonging to ST228 and ST624 were identified, which also lacked the CRISPR2 locus ( Table 1 ). Thus, our final dataset comprised 16 CRISPR2-negative genomes, which became the focus of our study. Sample details for this genetically related cluster of CRISPR2-negative strains are presented in Table 1 . All 16 strains shared the same allelic variants for the housekeeping genes gki ( 53 ) and yqiL ( 42 ), with 15 of them (except the ST1468 strain) also sharing the gyd ( 5 ) allele. Interestingly, most of these strains were isolated from meat products or surfaces in animal processing facilities. Additionally, one strain was isolated from the gastrointestinal tract of a mouse. The strains originated from diverse geographical locations, spanning three continents, and were isolated between 2006 and 2019. Notably, besides lacking the orphan CRISPR2 locus, all 16 strains also lacked the CRISPR1-Cas system but harbored the CRISPR3-Cas system. GENOMIC EVIDENCE FOR A DISTINCT E. faecalis SUBSPECIES: TAXONOMIC AND PHYLOGENETIC ASSESSMENT Motivated by the notable absence of CRISPR2 in this genetically related cluster of E. faecalis strains, we performed a comprehensive genome-wide evaluation. This analysis sought to clarify their taxonomic classification and determine their position within the broader evolutionary context of the species and its closely related taxa. For this purpose, we first used the Type (Strain) Genome Server (TYGS) ( 32 ), which automatically detects the closest related type strains for each user-submitted genome by default, performing pairwise whole-genome comparisons via the Genome BLAST Distance Phylogeny method (GBDP). GBDP distances are employed to infer phylogenetic trees and to calculate digital DNA:DNA hybridization (dDDH) values, enabling the taxonomic delineation of species and subspecies based on established thresholds (≥70% dDDH for species and ≥79% dDDH for subspecies) ( 32 ). As a result, we found that while all 16 CRISPR2-lacking genomes were confirmed as E. faecalis , they were classified as a unique subspecies, distinct from the one assigned to their closest type strain ( E. faecalis ATCC 19433 = NBRC 100480). The TYGS analysis job summary, including taxonomic identification results and pairwise dDDH values between the queried genomes and selected type strains, is provided as supplementary information ( Additional file S1 ). A simplified version of this finding is shown in Fig. 2A , in which redundant strains were excluded and strain 209EA1 was selected as the representative genome for the CRISPR2-negative cluster. Download figure Open in new tab Fig. 2 Species and subspecies delineation based on dDDH thresholds. Phylogenetic trees were inferred using FastME 2.1.6.1 from GBDP distances calculated from genome sequences. Branch lengths are scaled according to the GBDP distance formula d5. The numbers above branches represent GBDP pseudo-bootstrap support values > 60% from 100 replications. The trees were midpoint-rooted. The taxonomic classifications at the species and subspecies levels are represented by color codes on the right side of each tree, based on the dDDH values obtained and the established thresholds (≥70% dDDH for species and ≥79% dDDH for subspecies) ( A ) Whole-genome sequence-based phylogeny of Enterococcus faecalis 209EA1 (representative strain of the genetically related group of E. faecalis lacking CRISPR2) and its closest related type-strains, as determined by TYGS by default. ( B ) Phylogeny focused exclusively on query genomes, encompassing all 16 E. faecalis strains lacking CRISPR2 along with E. faecalis reference strains, including the type strain ATCC 19433. Sequence types (STs) of each strain are shown in parentheses. * Type strain of a proposed species with nomenclatural status not yet validly published. To gain novel insights into the unprecedented subspecies differentiation of Enterococcus faecalis , we conducted a new TYGS analysis. This analysis incorporated additional clonally distinct E. faecalis reference strains, including the well-studied model strains OG1RF [derived from the commensal and naturally antibiotic-sensitive human oral isolate OG1 ( 56 )] and V583 [a hospital-adapted VRE isolate ( 57 )], along with the species’ reference genome from NCBI datasets (strain T5). We also included the genome of E. faecalis ’ type strain. In this analysis, pairwise comparisons were confined to this set of genomes and the 16 CRISPR2-lacking strains. Notably, despite their genetic diversity, all four E. faecalis reference strains were grouped within the same subspecies and formed a clade separated from the one containing the 16 CRISPR2-lacking strains ( Fig. 2B ). For clarity and ease of comparison throughout this study, we will refer to this newly identified subspecies as “subspecies B”, while individuals classified within the most predominant and diverse subspecies (CRISPR2-positive) will be referred to as “subspecies A”. We expanded our TYGS analysis to investigate whether subspecies B was exclusively composed of genomes lacking CRISPR2, and if the inclusion of additional E. faecalis representatives from diverse lineages could reveal new taxonomic subdivisions at the subspecies level. For this purpose, in addition to subspecies B genomes, we incorporated 39 genomes representing 28 distinct STs, encompassing both the genetic diversity from our local collection and other lineages known to harbor CRISPR3- cas systems, a trait consistently found in subspecies B (see S1 Dataset ). The analysis revealed that all 39 genomes were classified within subspecies A (see Additional file S2 ), reinforcing the absence of CRISPR2 as a presumptive feature of subspecies B. Based on these results, we established this set of 39 genomes as the representative subset of subspecies A for subsequent comparative genomic analyses against subspecies B, providing a comprehensive framework for capturing the genetic diversity of E. faecalis . To further validate the reliability of this cladogenesis event and the associated taxonomic separation, we conducted additional phylogenomic reconstructions using different genome sets and approaches. These reconstructions spanned both broad and short evolutionary time scales, encompassing interspecific and intraspecific relationships, respectively (the latter will be presented in the subsequent section). By doing so, we were able to capture different levels of phylogenetic signal, inferred from the concatenated multiple sequence alignments (MSA) of the orthologous groups conserved within each genome set. Initially, to contextualize our findings within the broader evolutionary landscape of Enterococcus , we gathered a dataset of 117 genomes, including 59 E. faecalis strains (39 subspecies A, 16 subspecies B, and four additional reference genomes) and 58 unique representatives of other validly published Enterococcus species, primarily type strains. In cases where type strain genomes were unavailable or of lower quality than the species’ assigned reference genome in NCBI, we prioritized the highest quality genomes ( S2 Dataset ). Vagococcus fluvialis DSM 5731 was selected as the outgroup. Phylogenetic placement was based on the protein sequences of 751 orthologous groups, with a minimum of 95.8% of the genomes (out of the 118 included) containing single-copy genes in any orthologous group (for details, see Materials and Methods section). The resulting phylogenetic tree corroborated the classification of E. faecalis subspecies A and B as a single taxonomic species, forming a well-supported clade distinctly separated by a deep branch from the nearest related Enterococcus species ( Fig. 3 ). Despite the long-term evolutionary history depicted in the tree, the reconstruction effectively resolved the subspecies-level differentiation within E. faecalis , producing two well-supported subclades. Each subclade clusters strains classified in the same subspecies based on dDDH thresholds, demonstrating consistency between recent diversification events and taxonomic separation. Download figure Open in new tab Fig. 3 Comprehensive phylogeny of the Enterococcus genus illustrating subspecies-level cladogenesis of E. faecalis . The tree was estimated using IQ-TREE under the LG+F+I+R9 substitution model with 1,000 bootstrap replicates, based on the concatenation and alignment of 751 protein sequences corresponding to single-copy orthologs present in 95.8% of the 117 sampled enterococci and the outgroup strain Vagococcus fluvialis DSM 5731. The tree is presented at different scales. The global phylogeny on the left shows relationships between taxonomic species, with E. faecalis strains collapsed into their respective subspecies clades, which are highlighted by colored regions enclosed in a dashed rectangle. On the right, the E. faecalis clade is detailed at a fine scale, highlighting subspecies clusters (subspecies A in light orange and subspecies B in light teal). Red dots represent type strains; blue dots indicate E. faecalis model strains V583 and OG1RF; the black dot marks the E. faecalis reference genome from the NCBI datasets. Stars denote nodes with bootstrap support < 95%. PANGENOME AND REVERSE ECOLOGY ANALYSES: GENE CONTENT AND PREDICTED ECOLOGICAL DIFFERENCES BETWEEN SUBSPECIES A AND B The subset of 55 E. faecalis strains (39 subspecies A and 16 subspecies B), included in the previous phylogeny ( Fig. 3 ), was selected for a pangenome analysis. This analysis aimed to enhance the phylogenetic resolution of the subspecies-level cladogenesis and to identify genetic determinants potentially involved in their ecological differentiation. The pangenome consisted of 9,950 genes, while the core genome comprised 1,650 single-copy genes. Single nucleotide polymorphisms (SNPs) were extracted from the core gene alignment and used to construct the intraspecific phylogenetic tree presented in Fig. 4 . Additionally, CRISPR content, predicted antimicrobial resistance (AMR) and virulence-associated genes for each genome are indicated. Download figure Open in new tab Fig. 4 Enterococcus faecalis intraspecific phylogeny showcasing the distribution of CRISPR, antimicrobial resistance (AMR), and virulence genes across subspecies A and B. The tree was estimated using IQ-TREE under the GTR+G substitution model with 10,000 bootstrap replicates, based on SNPs extracted from the concatenated alignment of the 1,650 core genes present in all 55 sampled E. faecalis genomes. Rooted at the midpoint, the tree depicts E. faecalis subspecies A and B as the deepest divergences within the species, shown in colored ranges (subspecies A in light orange and subspecies B in light teal). Stars denote nodes with bootstrap support < 95%. Strain names are followed by their assigned sequence types (STs) in each leaf label. The colored strip adjacent to the strains indicates their respective isolation sources: human (red), animal (green), coastal water (blue), animal product (orange), beef processing environments (yellow), or unknown origin (gray). Screened genetic traits are shown as binary data in the next panels (filled shapes indicate trait presence and omitted shapes indicate absence), with CRISPR loci as checkmarks, antimicrobial resistance genes as black squares, and virulence genes as dark gray squares. Rooted at the midpoint, the phylogenetic tree reveals a clear subspecies-level divergence within E. faecalis , separating into two major clades that reflect the deepest divergences within the species. The larger clade comprising subspecies A genomes displays a more intricate topology with multiple subclades, interconnected by branches of varying lengths and generally lower statistical support. This clade includes most of the STs analyzed, encompassing strains isolated from diverse sources such as humans, animals, animal products, and environmental samples from coastal waters (see S1 Dataset for details). Notably, although CRISPR2 is universally present among genomes of subspecies A, the tree reveals a heterogeneous distribution of complete CRISPR-Cas systems (CRISPR1-Cas and CRISPR3-Cas) within this clade. The subspecies A clade also shows considerable variation in the number of AMR and virulence-associated genes, ranging from genomes with few such genes to strains with prominent multidrug resistance (MDR) profiles, including all vancomycin-resistant enterococci (VRE) isolates. The clade corresponding to subspecies B was supported by the maximum bootstrap value, corroborating the robustness of this separation. Most strains within this clade, except for ST624 DSM111623, were isolated either directly from animal meat or from surfaces and environments within meat processing facilities, suggesting a common ecological niche. Two well-supported branches are observed, with strains clustering according to their STs. One subclade includes strains from ST228 and its single-locus variant (SLV) STx, with branches exhibiting short lengths, making internal divergence indistinguishable at the current tree scale. The other subclade comprises genomes of the closely related ST624 and ST1468, which are also SLVs of each other. As previously mentioned, the CRISPR2 and CRISPR1- cas loci were consistently absent from the genomes of subspecies B, while CRISPR3- cas was universally present across all genomes in this group. In contrast to subspecies A, subspecies B exhibited a more uniform pattern of AMR and virulence genotypes, particularly within the ST228 cluster. The AMR profile of subspecies B was predominantly characterized by genes encoding efflux pumps, such as drfE, efrA/B, and lsaA , which are associated with MDR and biocide resistance ( 58 , 59 ). However, these genes were also detected in most subspecies A genomes. Notably, three strains of subspecies B harbored tetracycline resistance genes, specifically tetM , with one strain also containing tetL . Although not unique to subspecies B, a broad array of virulence genes was identified in most genomes of this group, especially within the ST228 cluster. These genes include homologues of the E. faecalis V583 ORF EF0818, which encodes a family 8 polysaccharide lyase ( 60 ); the bopD gene, encoding a sugar-binding transcriptional regulator essential for biofilm production ( 61 ); the capsule production-associated cps operon (except for the cpsF gene) ( 62 ); the ebpA/B/C genes, responsible for encoding endocarditis- and biofilm-associated pili ( 63 ); the efaA gene, which encodes the E. faecalis antigen A ( 64 ); the srtC gene (also known as bps ), encoding a biofilm- and pilus-associated sortase ( 65 ); the fss1 gene, encoding a fibrinogen-binding MSCRAMM (Microbial Surface Components Recognizing Adhesive Matrix Molecules) ( 66 ); and the gelE and sprE genes, which encode the secreted proteases gelatinase and serine protease, respectively. These proteases are regulated by the Fsr quorum-sensing system, encoded by the fsrA/B/C locus, which was also detected in these genomes ( 67 , 68 ). On the other hand, key differences in the resistome and virulome between subspecies A and B were observed. The ermB gene and homologues of EF3023 were statistically underrepresented in subspecies B ( P values < 0.005 after Benjamini-Hochberg correction). The ermB gene encodes an enzyme responsible for resistance to macrolides, lincosamides, and streptogramin B (MLS B ) ( 69 ), while EF3023 encodes HylA, a virulence-associated enzyme whose exact role remains to be elucidated ( 70 ). Still in the comparative epidemiological context, it is also worth noting that other AMR and virulence genes were exclusively detected in subspecies A. These include determinants involved in high-level aminoglycoside resistance (HLAR) ( aac(6′)-Ie-aph(2′)-Ia , ant ( 6 ) -Ia, aph(3’)-IIIa ), glycopeptide resistance ( van genes), oxazolidinone resistance ( cfr(B) and optrA ), as well as the operon encoding the pore-forming exotoxin cytolysin ( cylL L , cylL S , cylM , cylB , cylA , and cylI ) and its regulatory genes ( cylR1 and cylR2 ) ( 71 , 72 ). Interestingly, the detection of the ace gene, which encodes the virulence-associated adhesin to collagen of E. faecalis (Ace), revealed subspecies-specific variations in similarity to its corresponding reference sequence in VFDB (NP_814829). Specifically, genes from subspecies A showed higher similarity, as indicated by elevated coverage (100% in most cases) and identity percentages (>95% in all cases) (details are shown in S3 Dataset ). In contrast, sequences from subspecies B exhibited uniformly lower similarity, with reduced coverage and identity (both indices <85% in all cases) relative to the same reference sequence. Given the evolutionary distance between subspecies A and B, and considering the generalist lifestyle of E. faecalis ( 6 ), we aimed to infer the ecological properties that differentiate them as distinct ecotypes based on their genomes. For this purpose, we conducted a pangenome association analysis with the Scoary tool ( 47 ), which evaluates components of the pangenome for their associations with specific traits — here, the division between subspecies. This analysis identified 664 genes significantly associated with each subspecies, showing either positive or negative correlations. Of these, 59.3% (394/664) were overrepresented in subspecies A, while the remaining 40.7% (270/664) were more prevalent in subspecies B. The list of subspecies-enriched genes is provided in S4 Dataset . Functional analysis of the genes overrepresented in each subspecies was performed using the COGclassifier tool ( 50 ), which classifies prokaryote protein sequences into functional categories based on the COG (Cluster of Orthologous Genes) database. Significant similarity to known sequences was identified for 73.9% (291/394) of the subspecies A-enriched genes and 55.2% (149/270) of the subspecies B-enriched genes (see S4 Dataset for details). Although a considerable number of these genes, particularly those associated with subspecies B, could not be categorized into COG functional groups, some notable patterns emerged. Genes from both subspecies that were successfully classified were distributed across the same 20 COG categories ( Fig. 5A ). Remarkably, subspecies A exhibited a significantly higher proportion of genes likely involved in carbohydrate transport and metabolism (COG category G) compared to subspecies B ( P = 0.002785) ( Fig. 5B ). The proportions of genes within other COG categories did not show significant variation between the two subspecies. Download figure Open in new tab Fig. 5 COG functional classification of subspecies-enriched genes. ( A ) Heatmap showing the distribution of the 664 genes significantly associated with subspecies A and B, color-coded by their respective COG functional categories. Black-filled cells represent the presence of a gene in each strain. ( B ) Comparison of gene frequencies across COG functional categories between subspecies A and subspecies B. Each bar represents the proportion of genes annotated in a given functional category in relation to the total number of overrepresented genes in the respective subspecies. An asterisk (*) indicates a P-value <0.01 (chi-squared test). To identify candidate niche-specifying genes that marked the origin of each subspecies clade (i.e., genes potentially responsible for functional novelties that redefined the ecological niche of their most recent common ancestor) ( 17 , 51 , 73 ), we conducted an additional filtration of the subspecies-enriched genes. Specifically, we focused on those conserved in all members of one clade while absent in all members of the other. This approach led to the preliminary identification of 31 genes associated with subspecies A and 76 genes associated with subspecies B ( S5 Dataset ). Building on this, we sought to further elucidate the selective pressures that shaped the emergence, maintenance, and diversification of these subspecies by employing a reverse ecology approach ( 17 ). This allowed us to infer the likely ecological roles provided by these candidate niche-specifying genes based on their predicted functions. However, the automatic annotation performed by Prokka revealed that 74.2% (23/31) of the candidate niche-specifying genes in subspecies A and 81.6% (62/76) in subspecies B encoded ‘hypothetical proteins,’ indicating a lack of significant matches with known sequences in the databases included in Prokka. Recognizing these gaps, we conducted a more comprehensive manual annotation of all candidate niche-specifying genes. We utilized BLASTp to compare each sequence against the UniProtKB reference proteomes, Swiss-Prot database, and unreviewed TrEMBL entries, thereby expanding the search coverage. For the follow-up discussion, we focused on the best alignment matches exhibiting significant similarity indicative of homology (see Materials and Methods section for details). Additionally, we examined these sequences against the InterPro protein signature databases to identify protein family memberships, domains, conserved sites, and other features critical for functional characterization. We observed that 20 genes from subspecies A and 20 from subspecies B exhibited significant similarity to the same reference sequences from E. faecalis strain ATCC 700802/V583 in our BLASTp analysis against UniProtKB. Notably, the nuanced differences in sequence similarity between these genes and the reference sequences suggest that they are likely subspecies-specific allelic variants of the same 20 genes, rather than representing 40 distinct orthologous groups ( Table 2 ). View this table: View inline View popup Table 2. List of Enterococcus faecalis subspecies-specific core allelic variants and their putative biological roles based on similarity searches. The putative biological functions of the proteins encoded by these subspecies-specific allelic variants are summarized in Table 2 . These predictions highlight several proteins involved in fundamental cellular processes, including enzymes critical for biosynthesis (e.g., those required for protein synthesis), stress response mechanisms, regulation of gene expression, and energy production. Additionally, some proteins are potentially implicated in horizontal gene transfer, defense mechanisms, virulence, or possess currently unknown functions. Furthermore, additional 21 candidate niche-specifying genes from subspecies B also exhibited significant similarity to sequences from E. faecalis ATCC 700802/V583, a member of subspecies A, as illustrated in Fig. 2B (see S5 Dataset for further details on these genes and the best alignment matches for their encoded proteins). To mitigate potential sampling biases, we focused our analysis on candidate niche-specifying genes that did not exhibit significant similarity to sequences from the opposing subspecies (see S5 Dataset ). This approach ensures that the genes analyzed are more likely to be truly subspecies-specific orthologs. Following this stringent filtering process, we identified 11 orthologous groups unique to subspecies A strains and 35 unique to subspecies B strains. Detailed information on the proteins encoded by these niche-specifying genes is provided in Tables 3 and 4 , respectively. The implications of these findings are discussed in the following section. View this table: View inline View popup Table 3. List of Enterococcus faecalis subspecies A-specific proteins and their predicted biological roles based on similarity searches. View this table: View inline View popup Table 4. List of Enterococcus faecalis subspecies B-specific proteins and their predicted biological roles based on similarity searches. DISCUSSION The distinctive biological mechanism of CRISPR systems, particularly their intrinsic ability to sequentially incorporate fragments of foreign DNA, thereby generating a hypervariable region within the host chromosome, places these loci as true biological records. This property renders them valuable for reconstructing historical events, such as host-virus interactions in natural environments, as well as for assessing genetic diversity ( 74 , 75 ). The potential of CRISPR loci has already been explored for various applications, including tracking clinically relevant lineages and elucidating their origins and evolutionary paths ( 76 , 77 ). In E. faecalis , the orphan CRISPR2 locus has been explored as an alternative target for genetic diversity analysis and phylogenetic studies, enabling clonality predictions and the identification of recombination events among STs ( 12 – 14 ). Our original goal was to evaluate CRISPR2 array variability for genotyping and phylogenetic analysis of E. faecalis strains from diverse sources. However, initial analysis revealed an unexpected finding: a genetically related group of strains lacking CRISPR2. This observation challenges the prevailing assumption that CRISPR2 is ubiquitous, a component of the species’ core genome ( 8 , 12 ). Given this intriguing observation and the fact that the orphan CRISPR2 locus, in the absence of associated cas genes, has no clearly defined biological role, we redirected our focus to explore the implications of CRISPR2 absence in the context of the species’ evolutionary trajectory. Due to the fact that none of the CRISPR2-negative strains were part of our local bacterial culture collection, our investigation was limited to genomic approaches. Phylogenomic analyses revealed that the CRISPR2-negative strains form one of two E. faecalis major clades. Their most recent common ancestor (MRCA) diverged early in the evolutionary history of the species, leading to the independent evolution of these two lineages. These lineages can be classified into distinct subspecies based on dDDH thresholds for taxonomic delineation (referred to here as subspecies A and B, consisting exclusively of CRISPR2-positive and CRISPR2-negative genomes, respectively) ( 36 ). Objectively, this means that pairwise genomic comparisons between any representatives of each subspecies reveal hybridization values below 79%. Because no validly published E. faecalis child taxa were listed in the LPSN database at the time of writing, we believe this is the first study to describe a subspecies-level division for E. faecalis , based on the current criterion used for taxonomic classification of bacteria ( 33 , 34 , 78 ). Our findings challenge the previous understanding of E. faecalis population structure and its generalist nature. By revealing well-supported and distantly related subspecies clades within E. faecalis , our results question the assumption that this species lacks a multiclade structure and is devoid of clearly divergent groups, as suggested by earlier studies ( 6 , 15 ). Given that these prior studies did not include representative STs from subspecies B, it is plausible that their conclusions were influenced by a limited genomic sampling that lacked sufficient genetic diversity. In contrast, our study included a more comprehensive set of lineages, providing a broader perspective on the species’ population structure. The internal topology of the subspecies A clade reflects its extensive genetic and ecological diversity. This clade includes STs predominantly found in hospital environments, with multidrug resistance profiles and a strong association with HAIs— such as STs 6, 103, 525, and 778, including all VRE isolates in our study (e.g., the well-characterized clinical isolate V583). Additionally, the clade encompasses generalist STs like STs 4, 16, 21, and 40, which are widespread across various ecological niches and display variable CRISPR-Cas content, alongside strains such as the commensal-like OG1RF (ST1), the species type strain ATCC 19433 (ST25), and the NCBI reference genome T5 (ST68) ( 14 , 15 , 79 – 81 ). The low bootstrap support observed among these lineages suggests complex evolutionary dynamics, likely driven by diverse selective pressures and rapid diversification, which complicate the reconstruction of a clear evolutionary pathway. While this finding is consistent with the aforementioned studies focused on subspecies A, it does not fully capture the population structure of E. faecalis , particularly regarding the phylogenetic positioning and evolutionary significance of subspecies B. On the other hand, the inclusion of subspecies B in the phylogenetic reconstructions of E. faecalis revealed significantly divergent lineages, as evidenced by their deep, well-supported branches. Unlike subspecies A, subspecies B displays a more homogeneous genetic and ecological profile, even though it is further divided into two well-resolved subclades. All strains of subspecies B are related by their MLST allelic profiles (STs 228, 624, 1468, and their respective loci variants), with the majority sharing common isolation sources, such as animal meat or meat processing facilities. From an epidemiological standpoint, the absence of association between subspecies B and human clinical sources, along with the uniform lack of traits commonly found in high-risk lineages (e.g., acquired multidrug resistance and virulence genes linked to treatment failure and increased pathogenicity in enterococcal infections; see reviews ( 71 , 82 ) for comprehensive overviews) suggest that this subspecies may not pose a significant threat to human health, either as pathogens or as reservoirs of clinically relevant ARGs. However, it is also reasonable to consider that because subspecies B is not commonly found in humans and most molecular epidemiology studies on enterococci are centered around human clinical isolates, our understanding of its niche breadth and ecological roles within a one-health continuum is currently limited. Drawing from the existing literature and available metadata on strains classified as subspecies B, including the STs represented in our study as well as their single and double-locus variants cataloged in the PubMLST database ( https://pubmlst.org/ ), it appears that this subspecies is predominantly associated with animal-related sources. Notably, it has been isolated not only from meat and meat processing facilities but also directly from cloacal samples of chickens and wild birds such as common kingfishers ( Alcedo atthis , a species within the Coraciiformes order). These reports are predominantly linked to ST228 ( 83 – 85 ). The isolation of the ST624 strain from a mouse GIT, as documented in the NCBI under the identifier SAMEA13256522 (DSM111623 biosample), contributes to the understanding that subspecies B may also be associated with non-human mammals, in addition to livestock. Notably, we found no reports of subspecies B-associated STs being isolated from humans, with the sole exception of the ST228 SLV, ST247, found in a subgingival plaque sample from a patient with marginal periodontitis in Norway ( 86 ). This observation underscores the rarity of human association for subspecies B, reinforcing the notion of its distinct ecological profile. The consistent absence of critical genetic determinants associated with E. faecalis persistence in hospital environments, enhanced pathogenicity, and treatment failures in HAIs aligns with the universal presence of the CRISPR3-Cas system across all subspecies B genomes examined. Johnson et al. (2021) ( 5 ) have reviewed how the evolution of enterococci, particularly E. faecalis and E. faecium , into MDR hospital-acquired pathogens is largely driven by the accumulation of MGEs, including plasmids, transposable elements, and phages, which serve as vehicles for the spread of ARGs and virulence factors. Given that these traits are predominantly acquired through HGT, and the CRISPR3-Cas systems are known to restrict HGT in E. faecalis ( 87 ), it seems that subspecies B genomes have been shaped by selective pressures markedly different from those maintaining the high-risk lineages of subspecies A. An additional facet of this divergence can be seen in the variability of the ace gene between subspecies A and B. The Ace protein of E. faecalis mediates binding to host extracellular matrix proteins, such as collagen types I and IV and laminin ( 88 , 89 ), playing a crucial role in the colonization and infection of various tissues ( 90 , 91 ). Notably, the detection of ace genes in subspecies B genomes with significantly lower similarity to the VFDB reference sequence (NP_814829) compared to the ace sequences in subspecies A representatives may suggest tropism for different host environments, especially because this gene is known to be highly conserved among E. faecalis isolates ( 92 ). Moreover, individual gut bacteria are thought to exhibit divergence patterns aligned with host phylogeny, especially during allopatric speciation, which limits bacterial dispersal between hosts and gradually leads to reproductive isolation ( 93 ). Given that enterococci are hypothesized to have been core members of the gut microbiome in the last common ancestor of mammals, birds, reptiles, and insects ( 94 ), it is plausible that the divergence between subspecies A and B was driven by host-specific adaptations and reduced gene flow over time. The ubiquitous presence of the CRISPR3-Cas system among subspecies B strains could further support this reduced genetic exchange, offering an alternative or complementary explanation for the reproductive restriction and cladogenesis that culminated in the divergence of E. faecalis strains at the subspecies level. To further investigate the ecological adaptations that differentiate subspecies A and B, within the framework of reverse ecology, we conducted a pangenome analysis to identify genes enriched in each subspecies. This analysis revealed that both subspecies are enriched with genes distributed across the same 20 COG functional categories, suggesting that, in addition to the core genome, each subspecies has developed its own genetic repertoire supporting similar functional capacities. This finding is consistent with their independent evolutionary paths. Notably, subspecies A exhibited a significantly higher proportion of genes involved in carbohydrate transport and metabolism, supporting the hypothesis that subspecies A may be better adapted to environments with greater availability or diversity of carbohydrates, such as the human gut ( 95 – 97 ). In contrast, this suggests that subspecies B may be adapted to more restricted environments where carbohydrate variety is limited, or where alternative carbon sources are more prevalent, such as the guts of animals with more restrictive diets compared to humans, or ecosystems under distinct selective pressures ( 98 ). Carbohydrate availability in the host gut has been indicated as a major driver of enterococcal speciation since the emergence of the genus ( 99 ), which enhances the plausibility of our findings. Although these hypotheses align with our primary observations, it is important to note that nearly half of the genes enriched in subspecies B strains were not classified within any COG category. While this finding limits precise inferences regarding subspecies B’s ecological adaptations, it also provides compelling evidence of this lineage’s distinct evolutionary history and adaptive strategies. The significantly higher proportion of unclassified genes in subspecies B, compared to subspecies A, suggests that subspecies B may harbor novel genetic elements that have yet to be fully characterized. These unclassified genes could reflect adaptations to niche-specific pressures, possibly in environments that differ considerably from the well-studied human-associated habitats where subspecies A thrives. Moreover, the absence of functional annotation highlights the possibility that subspecies B is adapted to more specialized or less predictable environments, where distinct selective pressures —such as those found in non-human hosts or natural ecosystems— shape the evolution of unique genetic repertoires. Further research is required to elucidate the roles of these genes and their contributions to subspecies B’s fitness in specific conditions, which will enhance our understanding of E. faecalis niche breadth and its complex eco-evolutionary dynamics. In line with these considerations, it is also relevant to address the observed variability in alleles of MLST housekeeping genes in subspecies B, which may provide additional insights into the ecological divergence between the subspecies. All subspecies B isolates share the same allelic variants of the gki and yqiL genes, which are key housekeeping loci in the E. faecalis MLST scheme ( 80 ). Interestingly, Fertner et al. (2011) demonstrated that ST228 — the most frequent ST within subspecies B — had the most phylogenetically distant relationship compared to other E. faecalis lineages based on a concatenated phylogeny of the seven MLST housekeeping genes ( 85 ). While this study did not directly link the phylogenetic divergence to distinct subspecies, our findings suggest that such genetic distance reflects an ancient and consistent divergence between subspecies A and B, particularly considering that housekeeping genes tend to be highly conserved, with slower rates of evolution compared to other genomic regions ( 17 , 100 , 101 ). Building on the analysis of genes enriched in each subspecies, we applied a more stringent filter to identify genes present exclusively in one subspecies and absent in the other, across all representative genomes (i.e., clade/subspecies-specific genes, likely synapomorphic traits, and candidate niche-specifying genes) ( 17 , 73 ). This approach aimed to pinpoint genes potentially associated with the primary niche of each subspecies—those likely acquired by the common ancestor and maintained through periodic selection due to their adaptive value ( 17 , 73 ). Unlike the broader enrichment analysis, this method focused on genes that may have played a critical role in initiating ecological isolation and, thus, speciation, which is discussed here in light of the concept defined by Shapiro and Polz (2014) as “any stage of the dynamic process of ecological and genetic differentiation” ( 51 ). The prediction of subspecies-specific genes and allelic variants provides valuable targets for understanding the genetic basis of subspecies B’s emancipation and unique ecology. Initially, certain proteins were classified as distinct orthologous groups, thought to be exclusive to each subspecies. However, following reannotation, these proteins were reclassified as products of subspecies-specific allelic variants within the same orthologous group, rather than distinct genes. This finding highlights the potential roles of both gene acquisition or loss events, and sequence variation in pre-existing core genes, in delineating the ecological niches of each subspecies, as supported by Cohan (2002) ( 102 ). Although we cannot confirm whether such allelic variations correspond to actual adaptive mutations or to fixed neutral mutations solely based on these findings, the high degree of non-synonymous substitutions between subspecies A and B sequences suggests potential functional divergence. Even if these variants were neutral in the early stages of ecological speciation, they may have acquired adaptive significance over time, gradually shaped by distinct selective pressures and reflecting the niche specificity of each subspecies ( 103 ). It is important to note, however, that not all non-synonymous substitutions necessarily lead to functional changes ( 104 ). Therefore, further experimental validation is essential to determine the extent to which these variants contribute to bacterial adaptation ( 105 ). Investigating these potential functional changes could significantly enhance our understanding of subspecies-specific ecological adaptation in E. faecalis . Several proteins encoded by subspecies-specific gene variants are potentially linked to stress response and host colonization. One example is the EF1546-encoded protein, which contains a LysM domain, a well-characterized motif in autolysins responsible for binding bacterial cell wall peptidoglycan and facilitating precise catalytic cleavage during daughter cell separation ( 106 ). Controlled septum cleavage is critical for defining cell size and chain length in E. faecalis , factors directly impacting bacterial fitness ( 106 ). Mutations within the LysM domain have previously been associated with elongated cell chains, impaired immune evasion, and reduced virulence in E. faecalis ( 106 – 109 ). Given that such phenotypic alterations can profoundly affect bacterial morphology and its interaction with host defenses, the divergence in LysM-containing proteins between subspecies A and B may underlie their differential success in host colonization. Subspecies-specific sequence divergence was also observed in key transcriptional regulators, which may influence the adaptive responses of E. faecalis to varying environmental pressures. The EF1369 gene, encoding a Cro/CI family transcriptional regulator, has been linked to stress tolerance, including survival under high-salt conditions (8% NaCl), acidic environments (pH 2.8), and within mouse peritoneal macrophages ( 110 , 111 ). Similarly, the EF0876 gene, encoding a Mga helix-turn-helix protein, plays a crucial role in virulence regulation, particularly in host tissue adhesion in response to carbon metabolism ( 112 , 113 ). Notably, inactivation of EF0876 resulted in a 100-fold reduction in gut colonization in mice in a previous study ( 113 ), highlighting its importance for E. faecalis fitness in the GIT. Mga family regulators, including key E. faecalis regulons like EbpR ( 114 ), are activated by elevated CO 2 levels ( 115 , 116 ), typically found inside mammalian hosts (∼5-6%), as opposed to atmospheric levels in natural settings (∼0.036%) ( 117 , 118 ). Given the significant role of EF1369 and EF0876 in mediating E. faecalis colonization and survival under mammalian host conditions, and considering that these findings are based on subspecies A (i.e., strains derived from the V583 clinical isolate) ( 110 , 113 ), subspecies-specific sequence divergence in these regulators could impact the fitness of subspecies B under similar selective pressures. This is consistent with the lineage’s unusual isolation from human hosts, and might reflect the genetic basis supporting ecological niche distinctions between the subspecies. While the adaptive significance of subspecies-specific allelic variants of core genes is less predictable, the acquisition of entirely novel genes encoding new cellular functions is more likely to enable a strain to exploit previously inaccessible resources and establish itself within a new ecological niche (i.e., niche-specifying genes) ( 17 , 51 , 102 ). From this perspective, we highlight our main inferences and speculations regarding the gains and losses of key genetic determinants and the corresponding selective pressures driving the ecological separation of E. faecalis subspecies, as discussed below. Interestingly, the E. faecalis V583 EF2063 homologue, identified in the present study as subspecies A-specific, is one of the flanking regions of the CRISPR2 locus in E. faecalis genomes ( 28 ), whose absence we propose as a genomic marker for subspecies B. The absence of this region in subspecies B suggests that it may have been lost in a common ancestor through a single recombination event, akin to the mechanism of CRISPR1-Cas and CRISPR3-Cas variability described previously ( 28 ). While the precise functions of both the EF2063 gene (predicted to encode an AraC-family transcriptional regulator) and the orphan CRISPR2 locus remain unclear, we speculate that their combined loss in subspecies B could reflect a selective pressure favoring the genotype lacking this region, potentially dispensable or deleterious in its primary niche. This hypothesis is supported by the recent description of a wild-type E. faecalis strain harboring all three canonical CRISPR loci (CRISPR1-Cas, CRISPR2, and CRISPR3-Cas), leading the authors to conclude that CRISPR genotype variation in the species is likely due to CRISPR loss rather than locus acquisition by HGT ( 119 , 120 ). However, fitness trade-offs associated with CRISPR variability in E. faecalis have been primarily attributed to the presence or absence of functional CRISPR systems, not the orphan CRISPR2 ( 5 ). Thus, any further exploration of how CRISPR2 variation contributed to the ecological separation of the subspecies must also consider the role of EF2063 in this process. Similarly, building on the understanding that functionally related genes are often arranged contiguously in the genome, reflecting potential co-selection under specific environmental pressures ( 121 ), we observed that many of the subspecies A-specific genes exhibited synteny, (i.e., based on the gene arrangement of corresponding homologs in the V583 genome sequence) ( 57 ). This suggests that they may have been selected together in response to a specific ecological pressure, either gained by the most recent common ancestor of subspecies A or lost in subspecies B. For instance, the proteins encoded by EF1919 and EF1920 were annotated as a GNAT family acetyltransferase and a C4-dicarboxylate anaerobic carrier DcuC, respectively. While GNAT members are involved in various cellular processes, and this one has not been linked to any specific pathway so far, DcuC carriers are mainly responsible for succinate efflux produced during glucose fermentation ( 122 ). Interestingly, most bacteria containing dcuC homologs are pathogenic and all known DcuC family members are from enteric bacteria ( 122 ). This pattern supports the hypothesis that this locus may be integral to a key carbon and energy metabolic pathway in subspecies A strains, particularly under anaerobic conditions in the gut, the primary habitat of E. faecalis where it occurs as both a commensal and opportunistic pathogen ( 1 , 123 ). In contrast, the absence of these genes in subspecies B could indicate a competitive disadvantage for gut colonization, compared to subspecies A. Interestingly, many other subspecies A-specific genes seem to encode ecologically relevant determinants involved in E. faecalis ability to either colonize, persist or cause infection in a host environment. The EF1672 gene, encoding an ABC superfamily protein permease, was also exclusively and universally detected within subspecies A genomes. Notably, transcriptomic data have supported this gene’s crucial involvement in E. faecalis physiological adaptation during the course of infection in a mammalian host model, being hypothesized to be one of the core genes required for the species to thrive within a host, and thus a potential target for antimicrobial agents and vaccines to treat and prevent enterococcal infections ( 124 ). Another example implies that subspecies B strains might lack or have a reduced ability to utilize sucrose as a carbon and energy source. This is supported by the exclusive detection of determinants of two operons encoding a sucrose PTS (phosphoenolpyruvate: sugar phosphotransferase system) transporter (EF1602) and sucrose metabolism (EF1603, also named scrB-1 ) within subspecies A strains. Previous reports have shown that both loci likely contribute to virulence since they were up-regulated in E. faecalis strains grown in human urine ( 125 ), where sucrose levels can be increased in high-sugar diets ( 126 ). Knock-out mutants of EF1603-04 showed reduced virulence in a Caenorhabditis elegans infection model ( 127 ), which corroborates the differential role of these loci in E. faecalis pathogenesis associated with sucrose’s contribution to growth in infection. Additionally, studies have demonstrated that sucrose utilization enhances the expression of E. faecalis virulence-associated determinants and the production of biofilm matrix components (e.g., eDNA and EPS) in biofilms ( 128 ). Altogether, these findings support the genetic basis of subspecies A pathogenic potential in contrast to subspecies B by highlighting the contribution of sucrose uptake and metabolism in infections caused by E. faecalis , which seems consistent with this species (specifically, subspecies A) being a notorious agent of UTIs and aligned with the relevance of biofilm formation in the context of HAIs ( 82 ). Additionally, the subspecies A-specific EF1138 gene encodes an oxidoreductase of the aldo/keto reductase family, strongly related to E. faecalis V583 stress response induced by bovine bile exposure, being potentially implicated in bile salt modification ( 129 , 130 ). Bile tolerance mechanisms are critical for bacterial survival and colonization of the GIT, where bile’s emulsifying action poses a challenge ( 131 ). The absence of EF1138 in subspecies B may suggest that bile is not a strong selective pressure in its core ecological niche, or that subspecies B has evolved alternative mechanisms to tolerate bile exposure or to cope with different bile salt compositions, which includes the possibility of adaptation to a different host range ( 132 – 134 ). Considering that most studies on E. faecalis -host interactions have focused on its pathogenic potential in human infections and often rely on conventional animal models, primarily mammals (e.g., mice and rabbits), it is important to acknowledge that conclusions from these studies are inherently biased by the unique physiology of each host species ( 135 ). While the absence of subspecies A-specific genes in subspecies B suggests reduced fitness in humans and other mammalian hosts, it remains uncertain whether these genes are essential for colonization and survival across a broader host range, particularly in non-mammalian vertebrates ( 97 , 136 ). This knowledge gap limits our understanding of subspecies B’s ecological niche and adaptability. The isolation sources of subspecies B, which include wild birds, poultry, and meat processing environments, suggest a potential adaptation to hosts or environments beyond humans ( 83 – 85 ). Although the specific ecological role of birds in the lifecycle of subspecies B remains uncertain, previous studies support the hypothesis that wild migratory birds act as reservoirs for genetically diverse E. faecalis strains. Some of these strains are prevalent in poultry, indicating possible transmission routes from wild to domestic birds ( 16 , 137 ). These findings reinforce the idea that subspecies B may have evolved to thrive in non-mammalian hosts, particularly avian species, whose gut microbiota composition is consistently distinct from that of mammals ( 138 ). Furthermore, according to conclusions of a study by Chaillou and colleagues on meat microbiota ( 139 ), subspecies B’s common isolation from meat products and processing environments likely points to two primary sources of contamination: ( 1 ) animal-derived microbiota, particularly from the skin or gut, which may include Enterococcus , and ( 2 ) environmental psychrotrophic bacteria, largely represented by Firmicutes associated with water reservoirs, plants, or plant-derived animal feed ( 139 ). These hypotheses are not mutually exclusive in the context of E. faecalis , but rather suggest multiple potential dissemination pathways. This also implies that subspecies B may have undergone selective adaptations to thrive in such environments, where particular pressures could have driven and maintained its ecological and genetic differentiation over time ( 51 ). Our efforts to obtain further insights into the unique ecological niche of subspecies B focused on the functional annotation of its specific genes, a reverse ecology approach ( 17 , 73 ). While many of these genes encode proteins with uncharacterized functions, functional predictions were made based on putative homologs and conserved domains. Despite the significant homology between subspecies B sequences and their closest matches in the UniProtKB database, strongly supported by low E-values in most cases ( 54 ), notable differences in sequence identity were observed, making functional predictions less definitive. Nevertheless, the detection of conserved protein signatures provided valuable insights into potential ecological roles. Interestingly, most of the predicted homologs were derived from taxa commonly associated with meat microbiota and the contamination routes proposed by Chaillou et al., such as Vagococcus , Carnobacterium , Leuconostoc , various enterococcal species, and others ( 139 ). This correlation suggests that these genetic determinants may confer adaptations to shared ecological niches, potentially aiding subspecies B in coping with the selective pressures of these environments. One of our most compelling findings was the detection of sequences potentially encoding the toxin-antitoxin (TA) protein components HicA and HicB, along with a ClpP/crotonase-like domain-containing protein. These elements may play a coordinated role in stress responses linked to environmental conditions such as nutrient deprivation and heat shock ( 140 , 141 ). It has also been suggested that TA systems, including HicAB, are typically abundant in free-living bacteria but tend to be lost in host-associated prokaryotes ( 142 , 143 ). This raises the possibility that subspecies B’s recurrent isolation from meat products could be influenced by environmental contamination routes, potentially involving water, plants, or soil. Additionally, the ubiquitous detection of the CRISPR3-cas system within subspecies B further supports the contribution of environmental pressures, as functional CRISPR-Cas systems are known to provide defense against lytic phages, which are particularly abundant in water and soil environments ( 144 – 146 ). These environments could impose strong selection, contributing to the maintenance of CRISPR-mediated defense mechanisms. Although the CRISPR3-cas system was not exclusive to subspecies B, we observed that its Cas proteins (Cas1, Cas2, and Csn2) clustered distinctly from those of subspecies A, showing greater similarity to those found in Vagococcus elongatus , a species originally isolated from a swine-manure storage pit ( 147 ), which aligns with the transmission routes involving either animal or environmental reservoirs to meat microbiota, as proposed by Chaillou and colleagues ( 139 ). This divergence could reflect adaptations to different ecological pressures, though further investigation is needed to clarify the specific ecological role of these systems. We also identified subspecies B-specific traits that may be involved in the uptake and metabolism of alternative carbohydrates when glucose is scarce, which could suggest an adaptive advantage in diverse environments. Notably, we detected protein clusters resembling the IIABC component (BglF-like permease) of a PTS beta-glucoside-specific system and a PRD (PTS regulation domains)-containing protein (LicT-like transcriptional antiterminator) ( 148 , 149 ). These elements likely participate in the transport and positive regulation of genes linked to beta-glucoside metabolism, responding to substrates like salicin and arbutin found primarily in plant material but rarely in mammals ( 150 ). Moreover, we observed the exclusive presence of putative carbohydrate-binding proteins in subspecies B, such as the GlcNAc-binding protein A (GbpA) and a WxL domain-containing protein. The GbpA homolog contains domains frequently associated with cellulose- and chitin-binding capabilities, polysaccharides predominantly found in plants and arthropods, or fungi, respectively ( 151 , 152 ). In Vibrio cholerae , GbpA facilitates adhesion to zooplankton chitinous surfaces in aquatic habitats and to GlcNAc moieties on human intestinal epithelium ( 153 ). This dual functionality strongly suggests an exaptive process where a protein initially evolved for environmental persistence (e.g., attachment to chitin) later adapted to facilitate host colonization ( 154 ). By analogy, a similar exaptation in E. faecalis subspecies B could enable these bacteria to persist in environmental reservoirs (potentially associating with arthropods or adhering to plant cell walls) and also to colonize animal gut surfaces via GlcNAc binding, supporting its ecological adaptability across diverse environments ( 153 , 154 ). The WxL domain-containing proteins, commonly associated with adhesion to cellulose and xylan in other Gram-positive bacteria, such as Enterococcus faecium ( 155 ), reinforce this notion. These polysaccharides, abundant in plant cell walls, are often present in animal diets as dietary fibers. Interestingly, the ability of gut bacteria to adhere to these fibers confers a competitive advantage, especially in environments where plant-based feeds are supplemented with xylanase, facilitating the growth of beneficial microbes like lactobacilli ( 155 , 156 ). Thus, the exclusive presence of a WxL domain-containing protein in subspecies B suggests that these bacteria might have adapted to adhere to dietary fibers in the gastrointestinal tract of animals, potentially contributing to their isolation from meat products. Collectively, the subspecies B-specific GlcNAc-binding and WxL domain-containing proteins exemplify how exaptation might facilitate bacterial adaptation to shifting habitats, supporting the two potential transmission routes proposed by Chaillou et al. (2015). These traits could either enable direct environmental persistence, leading to contamination of meat products, or facilitate colonization of animal hosts, with subsequent transmission through the food production chain. However, it is important to note that identifying these proteins as exaptive traits remains speculative and requires further investigation for confirmation. Nevertheless, considering such possibilities underscores the adaptive potential of subspecies B and highlights the value of genomic surveillance across diverse settings to understand the evolutionary mechanisms driving E. faecalis adaptation, especially from a One Health perspective. Our findings also revealed specific subspecies B traits that suggest the existence of an unique repertoire of surface proteins, including several containing the LPxTG cell wall anchor motif, a feature typically associated with cell surface adhesion in Gram-positive bacteria ( 157 ). This repertoire appears to include proteins that may mediate adhesion to various substrates, as inferred from other conserved domains identified in their sequences. For example, one protein exhibits the fimbrial isopeptide formation D2 domain, which may facilitate pili subunit cross-linking and mediate surface adhesion to host structures ( 158 , 159 ). Another predicted protein contains a T-Q ester bond domain, an unusual bond type thought to stabilize pilin subunits and commonly found in cell surface adhesion proteins of Gram-positive bacteria ( 160 ), similar to a known collagen adhesin from Bacillus cereus . Moreover, we identified three proteins with MucBP domains, matching putative adhesins encoded by other enterococcal species. Notably, two of these proteins showed the highest similarity to a sequence from Enterococcus phoeniculicola ATCC BAA-412, a strain isolated from the uropygial gland of wild Red-billed Woodhoopoes ( Phoeniculus purpureus ) ( 161 ). This finding supports our earlier suggestion that subspecies B may be adapted to avian hosts, as the presence of proteins homologous to those in a bird-associated E. phoeniculicola highlights the potential role of birds as hosts or reservoirs for subspecies B. MucBP domain-containing proteins are generally linked to adherence in the mucosal environment of the GIT, providing a potential mechanism for subspecies B to colonize this niche. Notably, previous studies have identified these proteins as common in other commensal and environmental Gram-positive species, such as Lactobacillus plantarum ( 162 – 164 ). This suggests that subspecies B might exhibit similar ecological flexibility, capable of thriving in a range of environments, including the GIT of animals. Finally, two subspecies B-specific protein clusters were similar to predicted ABI family (Rce-like) proteins, both classified as type II CAAX prenyl endopeptidases. ABI proteins in enterococci have been previously associated with defense against phage infections ( 165 ), but there is also evidence suggesting their involvement in bacteriocin self-immunity mechanisms ( 166 ). This suggests that subspecies B might be able to produce additional bacteriocins. Unlike antibiotics, bacteriocins exhibit a narrow antimicrobial spectrum, targeting closely related bacterial strains ( 167 ). Such specificity implies that bacteriocin production might confer a competitive advantage by selectively inhibiting competitors within the same ecological niche. Additionally, bacteriocin production in Gram-positive bacteria is often linked to the transition from the logarithmic to stationary growth phase ( 168 ), highlighting their role as potent killing agents in resource-limited environments. Thus, the presence of subspecies B-specific proteins potentially involved in bacteriocin self-immunity hints at the possibility that this clade may possess a distinct competitive advantage in microbial communities, either in host-associated microbiomes or in environmental settings ( 169 , 170 ). Our findings suggest that the niche breadth of E. faecalis is likely underestimated, potentially encompassing a range of unexplored or insufficiently studied hosts and environments, which may explain the infrequent reporting of subspecies B-associated STs. By employing a reverse ecology approach, we have provided several insights into the hypothetical ecological properties and potential niches of subspecies B, aligning with current guidelines for the taxonomic description of prokaryotes from genome data ( 78 ). Nonetheless, the limitations inherent to our methods must be acknowledged. Specifically, the focus on clade-specific genes to identify past adaptations conserved through periodic selection inevitably overlooks other genetic determinants that may have contributed to the lineage’s evolution and differentiation, as highlighted by Lassalle et al. (2015). Additionally, experimental validation is essential to confirm the speculated roles of clade-specific genes or alleles and corresponding selective pressures in driving the ecological differentiation between E. faecalis subspecies A and B, following similar approaches used in related studies ( 73 , 99 , 171 , 172 ). Despite these limitations, our study highlights how genome-based exploratory approaches, utilizing publicly available data, can provide novel insights into the eco-evolutionary dynamics of bacterial species while simultaneously revealing important knowledge gaps. In the present study, analyzing CRISPR system genetic patterns across diverse genomes (spanning a broad range of STs, isolation sources, etc.) within the operational definition of a single species enabled us to identify and partially characterize a distinct cluster of E. faecalis strains, corresponding to a phylogenetically, ecologically, and taxonomically cohesive unit —subspecies B, using approaches proposed by previous studies ( 17 , 51 , 73 , 78 ). Although previous works have depicted the phylogenetic prominence of subspecies B members, particularly ST228 strains, compared to other E. faecalis clusters ( 83 , 85 ), neither the subspecies-level classification of this lineage, nor its positioning within the broader landscape of E. faecalis ’s population structure have been acknowledged. Addressing this gap, our findings reinforce the need to reassess E. faecalis ’s population structure in light of hidden genetic diversity in under-sampled environments and hosts (e.g., nonhuman isolates), as suggested by other studies ( 16 , 123 ). Given the role of generalist species in microbial dispersal and their propensity to give rise to specialists confined to a narrower range of habitats ( 173 , 174 ), it is plausible that the current view of E. faecalis as a species with little phylogenetic divergence and lacking a multiclade structure ( 15 , 175 ) stems from an underrepresentation of its true biological diversity. This underrepresentation likely obscures the existence of host- or environment-specific clades in advanced stages of speciation, such as subspecies B. Expanding intraspecific genomic surveillance to include isolates from nonhuman hosts and nonclinical environments will foster a more realistic understanding of the species’ population structure. Such efforts are critical from a One Health perspective, especially given previous evidence that E. faecalis ’s adaptation to the hospital environment may be a byproduct of its evolution in a wider range of ecological niches ( 6 , 99 , 175 ). Investigating the existence of niche-specific E. faecalis clades can help predict and monitor potential adaptive novelties that may precede the emergence of high-risk lineages, allowing for more targeted prevention and control strategies aimed at curbing the spread of these genetic determinants and associated lineages ( 176 , 177 ). While opportunities for genetic exchange between subspecies A and B are likely provided in overlapping compartments of their multidimensional niche breadths ( 178 ), it remains to be elucidated whether and how gene flow between individuals of these clades is maintained. CONCLUSIONS In this study, we unexpectedly identified a genetically related cluster of E. faecalis strains that lack the CRISPR2 locus, previously considered universally present in the species. Remarkably, this cluster represents a distinct subspecies, which we designate as subspecies B - an unprecedented finding, as no subspecies supported by formal genome-based taxonomic criteria has been previously described in E. faecalis . Our pangenome and reverse ecology analyses suggest that differences in carbohydrate availability in the host gut are likely key selective pressures driving the ecological separation between subspecies A and B. While subspecies B appears to have limited fitness in the mammalian gut and lacks genetic determinants associated with HAIs and multidrug resistance, it may have evolved under distinct host physiologies, potentially adapting to avian species. Furthermore, subspecies B harbors a unique genetic repertoire that likely enables its persistence in natural environments, such as water reservoirs, plants, or plant-derived animal feed, which may explain its frequent isolation from meat products and processing facilities. These findings challenge the previously established conception of the population structure of E. faecalis , revealing hidden phylogenetic diversity present in less-studied environments and hosts. From a One Health perspective, exploring intraspecific genomic heterogeneity, including nonhuman isolates, is essential for unraveling the genetic basis and selective pressures that drive the emergence of bacterial variants. Understanding whether these variants pose imminent public health threats, as well as identifying potential dissemination routes and environmental and host reservoirs, is crucial for developing targeted strategies to prevent and control the spread of emerging resistant and virulent genotypes. ACKNOWLEDGMENTS This work was supported in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq: Projects PQ/CNPq 312801/2020-3 and RAM/CNPq 408725/2022-2), Instituto Nacional de Pesquisa em Resistência Antimicrobiana (INPRA Project: INCT/CNPq 465718/2014-0); Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ: Projects E-26/211.554/2019, E-26/201.084/2021 and E-26/210.064/2020), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)–Finance Code 001 REFERENCES 1. ↵ Lebreton F , Willems RJL , Gilmore MS . 2014 . Enterococcus diversity, origins in nature, and gut colonization , p. 1 – 56 . In Enterococci: from commensals to leading causes of drug resistant infection . 2. ↵ Fisher K , Phillips C . 2009 . The ecology, epidemiology and virulence of Enterococcus . Microbiology (N Y ) 155 : 1749 – 1757 . OpenUrl CrossRef PubMed Web of Science 3. ↵ Morandi S , Brasca M , Alfieri P , Lodi R , Tamburini A . 2005 . Influence of pH and temperature on the growth of Enterococcus faecium and Enterococcus faecalis . Lait 85 . 4. ↵ Gaca AO , Lemos JA . 2019 . Adaptation to adversity: the intermingling of stress tolerance and pathogenesis in enterococci . Microbiology and Molecular Biology Reviews 83 . 5. ↵ Johnson CN , Sheriff EK , Duerkop BA , Chatterjee A . 2021 . Let me upgrade you: impact of mobile genetic elements on enterococcal adaptation and evolution . J Bacteriol 203 . 6. ↵ Pöntinen 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 , Schürch AC , Willems RJL , Corander J . 2021 . Apparent nosocomial adaptation of Enterococcus faecalis predates the modern hospital era . Nat Commun 12 . 7. ↵ Hille F , Charpentier E . 2016 . CRISPR-cas: Biology, mechanisms and relevance . Philosophical Transactions of the Royal Society B: Biological Sciences 371 . 8. ↵ Weaver KE . 2019 . Enterococcal Genetics . Microbiol Spectr 7 . 9. ↵ Varahan S , Hancock LE . 2016 . To defend or not to defend: that’s the question . mSphere 1 . 10. ↵ Fouquier D’Hérouel A , Wessner F , Halpern D , Ly-Vu J , Kennedy SP , Serror P , Aurell E , Repoila F . 2011 . A simple and efficient method to search for selected primary transcripts: non-coding and antisense RNAs in the human pathogen Enterococcus faecalis . Nucleic Acids Res 39 : e46 . OpenUrl CrossRef PubMed Web of Science 11. ↵ Innocenti N , Golumbeanu M , D’Hérouël AF , Lacoux C , Bonnin RA , Kennedy SP , Wessner F , Serror P , Bouloc P , Repoila F , Aurell E . 2015 . Whole-genome mapping of 5′ RNA ends in bacteria by tagged sequencing: a comprehensive view in Enterococcus faecalis . RNA 21 : 1018 – 1030 . OpenUrl Abstract / FREE Full Text 12. ↵ Hullahalli K , Rodrigues M , Schmidt BD , Li X , Bhardwaj P , Palmer KL . 2015 . Comparative analysis of the orphan CRISPR2 locus in 242 Enterococcus faecalis strains . PLoS One 10 . 13. Ping S , Mayorga-Reyes N , Price VJ , Onuoha M , Bhardwaj P , Rodrigues M , Owen J , Araya DP , Akins RL , Palmer KL . 2021 . Characterization of presumptive vancomycin-resistant enterococci recovered during infection control surveillance in Dallas, Texas , USA. Access Microbiol 3 : 000214 . OpenUrl PubMed 14. ↵ Gawryszewska I , Malinowska K , Kuch A , Chrobak-Chmiel D , Łaniewska-Trokenheim Ł , Hryniewicz W , Sadowy E . 2017 . Distribution of antimicrobial resistance determinants, virulence-associated factors and clustered regularly interspaced palindromic repeats loci in isolates of Enterococcus faecalis from various settings and genetic lineages . Pathog Dis 75 . 15. ↵ Palmer KL , Godfrey P , Griggs A , Kos VN , Zucker J , Desjardins C , Cerqueira G , Gevers D , Walker S , Wortman J , Feldgarden M , Haas B , Birren B , Gilmore MS . 2012 . Comparative genomics of enterococci: Variation in Enterococcus faecalis , clade structure in E. faecium , and defining characteristics of E. gallinarum and E. casseliflavus . mBio 3 : 1 – 11 . OpenUrl CrossRef PubMed 16. ↵ León-Sampedro R , del Campo R , Rodriguez-Baños M , Lanza VF , Pozuelo MJ , Francés-Cuesta C , Tedim AP , Freitas AR , Novais C , Peixe L , Willems RJL , Corander J , González Candelas F , Baquero F , Coque TM . 2019 . Phylogenomics of Enterococcus faecalis from wild birds: new insights into host-associated differences in core and accessory genomes of the species . Environ Microbiol 21 : 3046 – 3062 . OpenUrl CrossRef 17. ↵ Lassalle F , Muller D , Nesme X . 2015 . Ecological speciation in bacteria: reverse ecology approaches reveal the adaptive part of bacterial cladogenesis . Res Microbiol 166 : 729 – 741 . OpenUrl CrossRef 18. ↵ Shapiro BJ , Friedman J , Cordero OX , Preheim SP , Timberlake SC , Szabó G , Polz MF , Alm EJ . 2012 . Population genomics of early events in the ecological differentiation of bacteria . Science (1979) 335 : 48 – 51 . OpenUrl CrossRef 19. ↵ Freitas A de AR , Souza S da SR , Faria AR , Planet PJ , Merquior VLC , Teixeira LM . 2022 . Draft genome sequences of two commensal Enterococcus faecalis strains isolated from american black vultures (Coragyps atratus) in Brazil . Microbiol Resour Announc 11 . 20. ↵ Wick RR , Judd LM , Gorrie CL , Holt KE . 2017 . Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads . PLoS Comput Biol 13 : e1005595 . OpenUrl CrossRef PubMed 21. ↵ Olson RD , Assaf R , Brettin T , Conrad N , Cucinell C , Davis JJ , Dempsey DM , Dickerman A , Dietrich EM , Kenyon RW , Kuscuoglu M , Lefkowitz EJ , Lu J , Machi D , Macken C , Mao C , Niewiadomska A , Nguyen M , Olsen GJ , Overbeek JC , Parrello B , Parrello V , Porter JS , Pusch GD , Shukla M , Singh I , Stewart L , Tan G , Thomas C , VanOeffelen M , Vonstein V , Wallace ZS , Warren AS , Wattam AR , Xia F , Yoo H , Zhang Y , Zmasek CM , Scheuermann RH , Stevens RL . 2023 . Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR . Nucleic Acids Res 51 : D678 – D689 . OpenUrl CrossRef PubMed 22. ↵ Krueger F . 2012 . Trim Galore: a wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files, with some extra functionality for MspI-digested RRBS-type (Reduced Representation Bisufite-Seq) libraries . https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ . Retrieved 13 May 2024. 23. ↵ Seemann T . 2014 . MLST: Scan contig files against PubMLST typing schemes . https://github.com/tseemann/mlst . Retrieved 7 May 2024. 24. ↵ Jolley KA , Bray JE , Maiden MCJ . 2018 . Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications . Wellcome Open Res 3 . 25. ↵ Seemann T . 2014 . Prokka: rapid prokaryotic genome annotation . Bioinformatics 30 : 2068 – 2069 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Couvin D , Bernheim A , Toffano-nioche C , Touchon M , Rocha EPC , Vergnaud G , Michalik J , Bertrand N , Gautheret D , Pourcel C , Roux D . 2018 . CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins 46 : 246 – 251 . OpenUrl 27. Bikandi J , Millán RS , Rementeria A , Garaizar J . 2004 . In silico analysis of complete bacterial genomes: PCR, AFLP-PCR and endonuclease restriction . Bioinformatics 20 : 798 – 799 . OpenUrl CrossRef PubMed Web of Science 28. ↵ Palmer KL , Gilmore MS . 2010 . Multidrug-resistant enterococci lack CRISPR-cas . mBio 1 : e00227 – 10 . OpenUrl CrossRef PubMed 29. ↵ McArthur AG , Waglechner N , Nizam F , Yan A , Azad MA , Baylay AJ , Bhullar K , Canova MJ , De Pascale G , Ejim L , Kalan L , King AM , Koteva K , Morar M , Mulvey MR , O’Brien JS , Pawlowski AC , Piddock LJV , Spanogiannopoulos P , Sutherland AD , Tang I , Taylor PL , Thaker M , Wang W , Yan M , Yu T , Wright GD . 2013 . The comprehensive antibiotic resistance database . Antimicrob Agents Chemother 57 : 3348 – 3357 . OpenUrl Abstract / FREE Full Text 30. ↵ Chen L , Yang J , Yu J , Yao Z , Sun L , Shen Y , Jin Q . 2005 . VFDB: a reference database for bacterial virulence factors . Nucleic Acids Res 33 : D325 – D328 . OpenUrl CrossRef PubMed Web of Science 31. ↵ Seemann T . 2016 . ABRicate: Mass screening of contigs for antimicrobial and virulence genes . https://github.com/tseemann/abricate . Retrieved 14 May 2024. 32. ↵ Meier-Kolthoff JP , Göker M . 2019 . TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy . Nat Commun 10 . 33. ↵ Meier-Kolthoff JP , Carbasse JS , Peinado-Olarte RL , Göker M . 2022 . TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes . Nucleic Acids Res 50 : D801 – D807 . OpenUrl CrossRef PubMed 34. ↵ Parte AC . 2014 . LPSN—List of Prokaryotic Names with Standing in Nomenclature . Nucleic Acids Res 42 : D613 – D616 . OpenUrl CrossRef PubMed Web of Science 35. ↵ Meier-Kolthoff JP , Auch AF , Klenk HP , Göker M . 2013 . Genome sequence-based species delimitation with confidence intervals and improved distance functions . BMC Bioinformatics 14 : 1 – 14 . OpenUrl CrossRef PubMed 36. ↵ Meier-Kolthoff JP , Hahnke RL , Petersen J , Scheuner C , Michael V , Fiebig A , Rohde C , Rohde M , Fartmann B , Goodwin LA , Chertkov O , Reddy TBK , Pati A , Ivanova NN , Markowitz V , Kyrpides NC , Woyke T , Göker M , Klenk HP . 2014 . Complete genome sequence of DSM 30083T, the type strain (U5/41T) of Escherichia coli , and a proposal for delineating subspecies in microbial taxonomy . Stand Genomic Sci 9 : 1 – 19 . OpenUrl PubMed 37. ↵ Lefort V , Desper R , Gascuel O . 2015 . FastME 2.0: A comprehensive, accurate, and fast distance-based phylogeny inference program . Mol Biol Evol 32 . 38. ↵ Emms DM , Kelly S . 2019 . OrthoFinder: Phylogenetic orthology inference for comparative genomics . Genome Biol 20 . 39. ↵ Emms DM , Kelly, Affiliations. 2018 . STAG: Species Tree Inference from All Genes . bioRxiv . 40. ↵ Katoh K , Standley DM . 2013 . MAFFT multiple sequence alignment software version 7: Improvements in performance and usability . Mol Biol Evol 30 . 41. ↵ Minh BQ , Schmidt HA , Chernomor O , Schrempf D , Woodhams MD , Von Haeseler A , Lanfear R , Teeling E . 2020 . IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era . Mol Biol Evol 37 . 42. ↵ Kalyaanamoorthy S , Minh BQ , Wong TKF , Von Haeseler A , Jermiin LS . 2017 . ModelFinder: fast model selection for accurate phylogenetic estimates . Nat Methods 14 . 43. ↵ Hoang DT , Chernomor O , Von Haeseler A , Minh BQ , Vinh LS . 2018 . UFBoot2: improving the ultrafast bootstrap approximation . Mol Biol Evol 35 . 44. ↵ Letunic I , Bork P . 2024 . Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool . Nucleic Acids Res 2024 : 1 – 5 . OpenUrl CrossRef 45. ↵ 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 . OpenUrl CrossRef PubMed 46. ↵ Page AJ , Taylor B , Delaney AJ , Soares J , Seemann T , Keane JA , Harris SR . 2016 . SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments . Microb Genom 2 : e000056 . OpenUrl 47. ↵ Brynildsrud O , Bohlin J , Scheffer L , Eldholm V . 2016 . Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary . Genome Biol 17 : 1 – 9 . OpenUrl CrossRef PubMed 48. ↵ Galperin MY , Wolf YI , Makarova KS , Alvarez RV , Landsman D , Koonin E V . 2021 . COG database update: focus on microbial diversity, model organisms, and widespread pathogens . Nucleic Acids Res 49 : D274 – D281 . OpenUrl CrossRef PubMed 49. ↵ Tatusov RL , Galperin MY , Natale DA , Koonin E V . 2000 . The COG database: a tool for genome-scale analysis of protein functions and evolution . Nucleic Acids Res 28 : 33 – 36 . OpenUrl CrossRef PubMed Web of Science 50. ↵ Shimoyama Y . 2022 . COGclassifier: A tool for classifying prokaryote protein sequences into COG functional category . https://github.com/moshi4/COGclassifier . Retrieved 25 May 2024. 51. ↵ Shapiro BJ , Polz MF . 2014 . Ordering microbial diversity into ecologically and genetically cohesive units . Trends Microbiol 22 : 235 – 247 . OpenUrl CrossRef PubMed Web of Science 52. ↵ Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ . 1990 . Basic local alignment search tool . J Mol Biol 215 : 403 – 410 . OpenUrl CrossRef PubMed Web of Science 53. ↵ Bateman A , Martin MJ , Orchard S , Magrane M , Ahmad S , Alpi E , Bowler-Barnett EH , et al. 2023 . UniProt: the Universal Protein Knowledgebase in 2023 . Nucleic Acids Res 51 . 54. ↵ Pearson WR . 2013 . An introduction to sequence similarity (“homology”) searching . Curr Protoc Bioinformatics doi: 10.1002/0471250953.bi0301s42 . OpenUrl CrossRef PubMed 55. ↵ Paysan-Lafosse T , Blum M , Chuguransky S , Grego T , Pinto BL , Salazar GA , Bileschi ML , Bork P , Bridge A , Colwell L , Gough J , Haft DH , Letunić I , Marchler-Bauer A , Mi H , Natale DA , Orengo CA , Pandurangan AP , Rivoire C , Sigrist CJA , Sillitoe I , Thanki N , Thomas PD , Tosatto SCE , Wu CH , Bateman A . 2023 . InterPro in 2022 . Nucleic Acids Res 51 . 56. ↵ Bourgogne A , Garsin DA , Qin X , Singh K V. , Sillanpaa J , Yerrapragada S , Ding Y , Dugan-Rocha S , Buhay C , Shen H , Chen G , Williams G , Muzny D , Maadani A , Fox KA , Gioia J , Chen L , Shang Y , Arias CA , Nallapareddy SR , Zhao M , Prakash VP , Chowdhury S , Jiang H , Gibbs RA , Murray BE , Highlander SK , Weinstock GM . 2008 . Large scale variation in Enterococcus faecalis illustrated by the genome analysis of strain OG1RF . Genome Biol 9 : R110 . OpenUrl CrossRef PubMed 57. ↵ Paulsen IT , Banerjei L , Hyers GSA , Nelson KE , Seshadri R , Read TD , Fouts DE , Eisen JA , Gill SR , Heidelberg JF , Tettelin H , Dodson RJ , Umayam L , Brinkac L , Beanan M , Daugherty S , DeBoy RT , Durkin S , Kolonay J , Madupu R , Nelson W , Vamathevan J , Tran B , Upton J , Hansen T , Shetty J , Khouri H , Utterback T , Radune D , Ketchum KA , Dougherty BA , Fraser CM . 2003 . Role of mobile DNA in the evolution of vancomycin-resistant Enterococcus faecalis . Science (1979) 299 : 2071 – 2074 . OpenUrl Abstract / FREE Full Text 58. ↵ Jonas BM , Murray BE , Weinstock GM . 2001 . Characterization of emeA , a norA homolog and multidrug resistance efflux pump, in Enterococcus faecalis . Antimicrob Agents Chemother 45 . 59. ↵ Fernández-Fuentes MA , Abriouel H , Ortega Morente E , Pérez Pulido R , Gálvez A . 2014 . Genetic determinants of antimicrobial resistance in Gram positive bacteria from organic foods . Int J Food Microbiol 172 . 60. ↵ Lepage E , Brinster S , Caron C , Ducroix-Crepy C , Rigottier-Gois L , Dunny G , Hennequet-Antier C , Serror P . 2006 . Comparative genomic hybridization analysis of Enterococcus faecalis : identification of genes absent from food strains . J Bacteriol 188 . 61. ↵ Hufnagel M , Koch S , Creti R , Baldassarri L , Huebner J . 2004 . A putative sugar-binding transcriptional regulator in a novel gene locus in Enterococcus faecalis contributes to production of biofilm and prolonged bacteremia in mice . Journal of Infectious Diseases 189 . 62. ↵ Thurlow LR , Thomas VC , Hancock LE . 2009 . Capsular polysaccharide production in Enterococcus faecalis and contribution of CpsF to capsule serospecificity . J Bacteriol 191 : 6203 – 6210 . OpenUrl Abstract / FREE Full Text 63. ↵ Nallapareddy SR , Singh K V. , Sillanpää J , Garsin DA , Höök M , Erlandsen SL , Murray BE . 2006 . Endocarditis and biofilm-associated pili of Enterococcus faecalis . Journal of Clinical Investigation 116 : 2799 – 2807 . OpenUrl CrossRef PubMed Web of Science 64. ↵ Low YL , Jakubovics NS , Flatman JC , Jenkinson HF , Smith AW . 2003 . Manganese-dependent regulation of the endocarditis-associated virulence factor EfaA of Enterococcus faecalis . J Med Microbiol 52 . 65. ↵ Kemp KD , Singh K V. , Nallapareddy SR , Murray BE . 2007 . Relative contributions of Enterococcus faecalis OG1RF sortase-encoding genes, srtA and bps ( srtC ), to biofilm formation and a murine model of urinary tract infection . Infect Immun 75 . 66. ↵ Sillanpää J , Nallapareddy SR , Houston J , Ganesh VK , Bourgkogne A , Singh K V ., Murray BE , Höök M . 2009 . A family of fibrinogen-binding MSCRAMMs from Enterococcus faecalis . Microbiology (N Y) 155 . 67. ↵ Qin X , Singh K V. , Weinstock GM , Murray BE . 2001 . Characterization of fsr, a regulator controlling expression of gelatinase and serine protease in Enterococcus faecalis OG1RF . J Bacteriol 183 : 3372 – 3382 . OpenUrl Abstract / FREE Full Text 68. ↵ Qin X , Singh K V. , Weinstock GM , Murray BE . 2000 . Effects of Enterococcus faecalis fsr genes on production of gelatinase and a serine protease and virulence . Infect Immun 68 : 2579 – 2586 . OpenUrl Abstract / FREE Full Text 69. ↵ Portillo A , Ruiz-Larrea F , Zarazaga M , Alonso A , Martinez JL , Torres C . 2000 . Macrolide resistance genes in Enterococcus spp . Antimicrob Agents Chemother 44 : 967 – 971 . OpenUrl Abstract / FREE Full Text 70. ↵ Johnson AO , Shipman BM , Hunt BC , Learman BS , Brauer AL , Zhou SP , Blair RH , De Nisco NJ , Armbruster CE . 2024 . Function and contribution of two putative Enterococcus faecalis glycosaminoglycan degrading enzymes to bacteremia and catheter-associated urinary tract infection . Infect Immun 92 . 71. ↵ Geraldes C , Tavares L , Gil S , Oliveira M . 2022 . Enterococcus virulence and resistant traits associated with its permanence in the hospital environment . Antibiotics 11 . 72. ↵ Van Tyne D , Martin MJ , Gilmore MS . 2013 . Structure, function, and biology of the Enterococcus faecalis cytolysin . Toxins (Basel ) 5 : 895 – 911 . OpenUrl CrossRef PubMed 73. ↵ Lassalle F , Campillo T , Vial L , Baude J , Costechareyre D , Chapulliot D , Shams M , Abrouk D , Lavire C , Oger-Desfeux C , Hommais F , Guéguen L , Daubin V , Muller D , Nesme X . 2011 . Genomic species are ecological species as revealed by comparative genomics in Agrobacterium tumefaciens . Genome Biol Evol 3 : 762 – 781 . OpenUrl CrossRef PubMed 74. ↵ Held NL , Herrera A , Quiroz HC , Whitaker RJ . 2010 . CRISPR associated diversity within a population of Sulfolobus islandicus . PLoS One 5 . 75. ↵ Anderson RE , Brazelton WJ , Baross JA . 2011 . Using CRISPRs as a metagenomic tool to identify microbial hosts of a diffuse flow hydrothermal vent viral assemblage . FEMS Microbiol Ecol 77 : 120 – 133 . OpenUrl CrossRef PubMed Web of Science 76. ↵ Beauruelle C , Treluyer L , Pastuszka A , Cochard T , Lier C , Mereghetti L , Glaser P , Poyart C , Lanotte P . 2021 . CRISPR typing increases the discriminatory power of Streptococcus agalactiae typing methods . Front Microbiol 12 : 675597 . OpenUrl CrossRef PubMed 77. ↵ Fabre L , Zhang J , Guigon G , Le Hello S , Guibert V , Accou-Demartin M , de Romans S , Lim C , Roux C , Passet V , Diancourt L , Guibourdenche M , Issenhuth-Jeanjean S , Achtman M , Brisse S , Sola C , Weill FX . 2012 . CRISPR typing and subtyping for improved laboratory surveillance of Salmonella Infections . PLoS One 7 : e36995 . OpenUrl CrossRef PubMed 78. ↵ Riesco R , Trujillo ME . 2024 . Update on the proposed minimal standards for the use of genome data for the taxonomy of prokaryotes . Int J Syst Evol Microbiol 74 . 79. ↵ Raven KE , Reuter S , Gouliouris T , Reynolds R , Russell JE , Brown NM , Török ME , Parkhill J , Peacock SJ . 2016 . Genome-based characterization of hospital-adapted Enterococcus faecalis lineages . Nat Microbiol 1 : 1 – 7 . OpenUrl CrossRef 80. ↵ Ruiz-Garbajosa P , Bonten MJM , Robinson DA , Top J , Nallapareddy SR , Torres C , Coque TM , Cantón R , Baquero F , Murray BE , Del Campo R , Willems RJL . 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 . OpenUrl Abstract / FREE Full Text 81. ↵ McBride SM , Fischetti VA , LeBlanc DJ , Moellering RC , Gilmore MS . 2007 . Genetic diversity among Enterococcus faecalis . PLoS One 2 : e582 . OpenUrl CrossRef PubMed 82. ↵ Fiore E , Van Tyne D , Gilmore MS . 2019 . Pathogenicity of enterococci . Microbiol Spectr 7 . 83. ↵ Holman DB , Klima CL , Gzyl KE , Zaheer R , Service C , Jones TH , Mcallister TA . 2021 . Antimicrobial resistance in Enterococcus spp. isolated from a beef processing plant and retail ground beef . 84. Splichalova P , Svec P , Ghosh A , Zurek L , Oravcova V , Radimersky T , Bohus M , Literak I . 2015 . Prevalence, diversity and characterization of enterococci from three coraciiform birds. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology 107 : 1281 – 1289 . OpenUrl 85. ↵ Fertner ME , Olsen RH , Bisgaard M , Christensen H . 2011 . Transmission and genetic diversity of Enterococcus faecalis among layer chickens during hatch . Acta Vet Scand 53 : 56 . OpenUrl CrossRef PubMed 86. ↵ Sun J , Song X , Kristiansen BE , Kjæreng A , Willems RJL , Eriksen HM , Sundsfjord A , Sollid JE . 2009 . Occurrence, population structure, and antimicrobial resistance of enterococci in marginal and apical periodontitis . J Clin Microbiol 47 : 2218 – 2225 . OpenUrl Abstract / FREE Full Text 87. ↵ Price VJ , Huo W , Sharifi A , Palmer KL . 2016 . CRISPR-Cas and restriction-modification act additively against conjugative antibiotic resistance plasmid transfer in Enterococcus faecalis . mSphere 1 . 88. ↵ Tomita H , Ike Y . 2004 . Tissue-specific adherent Enterococcus faecalis strains that show highly efficient adhesion to human bladder carcinoma T24 cells also adhere to extracellular matrix proteins . Infect Immun 72 . 89. ↵ Nallapareddy SR , Qin X , Weinstock GM , Hook M , Murray BE . 2000 . Enterococcus faecalis adhesin, Ace, mediates attachment to extracellular matrix proteins collagen type IV and laminin as well as collagen type I . Infect Immun 68 . 90. ↵ Singh K V. , Nallapareddy SR , Sillanpää J , Murray BE . 2010 . Importance of the collagen adhesin ace in pathogenesis and protection against Enterococcus faecalis experimental endocarditis . PLoS Pathog 6 : e1000716 . OpenUrl CrossRef PubMed 91. ↵ Lebreton F , Riboulet-Bisson E , Serror P , Sanguinetti M , Posteraro B , Torelli R , Hartke A , Auffray Y , Giard JC . 2009 . ace , which encodes an adhesin in Enterococcus faecalis , is regulated by Ers and is involved in virulence . Infect Immun 77 : 2832 – 2839 . OpenUrl Abstract / FREE Full Text 92. ↵ Nallapareddy SR , Singh K V. , Duh RW , Weinstock GM , Murray BE . 2000 . Diversity of ace , a gene encoding a microbial surface component recognizing adhesive matrix molecules, from different strains of Enterococcus faecalis and evidence for production of ace during human infections . Infect Immun 68 . 93. ↵ Groussin M , Mazel F , Alm EJ . 2020 . Co-evolution and co-speciation of host-gut bacteria systems . Cell Host Microbe 28 : 12 – 22 . OpenUrl CrossRef PubMed 94. ↵ Van Tyne D , Gilmore MS . 2014 . Friend turned foe: evolution of enterococcal virulence and antibiotic resistance . Annu Rev Microbiol 68 : 337 – 356 . OpenUrl CrossRef PubMed 95. ↵ Payling L , Fraser K , Loveday SM , Sims I , Roy N , McNabb W . 2020 . The effects of carbohydrate structure on the composition and functionality of the human gut microbiota . Trends Food Sci Technol 97 : 233 – 248 . OpenUrl CrossRef 96. Mora-Flores LP , Moreno-Terrazas Casildo R , Fuentes-Cabrera J , Pérez-Vicente HA , de Anda-Jáuregui G , Neri-Torres EE . 2023 . The role of carbohydrate intake on the gut microbiome: a weight of evidence systematic review . Microorganisms 11 . 97. ↵ Ley RE , Lozupone CA , Hamady M , Knight R , Gordon JI . 2008 . Worlds within worlds: evolution of the vertebrate gut microbiota . Nat Rev Microbiol 6 . 98. ↵ Adebowale TO , Yao K , Oso AO . 2019 . Major cereal carbohydrates in relation to intestinal health of monogastric animals: A review . Animal Nutrition 5 : 331 – 339 . OpenUrl CrossRef PubMed 99. ↵ Lebreton F , Manson AL , Saavedra JT , Straub TJ , Earl AM , Gilmore MS . 2017 . Tracing the enterococci from paleozoic origins to the hospital . Cell 169 : 849 – 861 .e13. OpenUrl CrossRef PubMed 100. ↵ Joshi CJ , Ke W , Drangowska-Way A , O’Rourke EJ , Lewis NE . 2022 . What are housekeeping genes? PLoS Comput Biol 18 . 101. ↵ Shibai A , Kotani H , Sakata N , Furusawa C , Tsuru S . 2022 . Purifying selection enduringly acts on the sequence evolution of highly expressed proteins in Escherichia coli . G3: Genes, Genomes, Genetics 12 . 102. ↵ Cohan FM . 2002 . What are bacterial species? 101146/annurev.micro56012302160634 56 : 457 – 487 . OpenUrl 103. ↵ Savolainen O , Lascoux M , Merilä J . 2013 . Ecological genomics of local adaptation . Nat Rev Genet 14 . 104. ↵ Ng PC , Henikoff S . 2006 . Predicting the effects of amino acid substitutions on protein function . Annu Rev Genomics Hum Genet 7 . 105. ↵ Barrick JE , Yu DS , Yoon SH , Jeong H , Oh TK , Schneider D , Lenski RE , Kim JF . 2009 . Genome evolution and adaptation in a long-term experiment with Escherichia coli . Nature 461 . 106. ↵ Salamaga B , Turner RD , Elsarmane F , Galley NF , Kulakauskas S , Mesnage S . 2023 . A moonlighting role for LysM peptidoglycan binding domains underpins Enterococcus faecalis daughter cell separation . Commun Biol 6 . 107. Weiser JN . 2013 . The battle with the host over microbial size . Curr Opin Microbiol 16 . 108. Dalia AB , Weiser JN . 2011 . Minimization of bacterial size allows for complement evasion and is overcome by the agglutinating effect of antibody . Cell Host Microbe 10 . 109. ↵ Salamaga B , Prajsnar TK , Jareño-Martinez A , Willemse J , Bewley MA , Chau F , Ben Belkacem T , Meijer AH , Dockrell DH , Renshaw SA , Mesnage S . 2017 . Bacterial size matters: multiple mechanisms controlling septum cleavage and diplococcus formation are critical for the virulence of the opportunistic pathogen Enterococcus faecalis . PLoS Pathog 13 . 110. ↵ Michaux C , Hartke A , Martini C , Reiss S , Albrecht D , Budin-Verneuil A , Sanguinetti M , Engelmann S , Hain T , Verneuil N , Giard JC . 2014 . Involvement of Enterococcus faecalis small RNAs in stress response and virulence . Infect Immun 82 . 111. ↵ Shioya K , Michaux C , Kuenne C , Hain T , Verneuil N , Budin-Verneuil A , Hartsch T , Hartke A , Giard JC . 2011 . Genome-wide identification of small RNAs in the opportunistic pathogen Enterococcus faecalis V583 . PLoS One 6 . 112. ↵ Hondorp ER , McIver KS . 2007 . The Mga virulence regulon: infection where the grass is greener . Mol Microbiol 66 . 113. ↵ Rigottier-Gois L , Madec C , Navickas A , Matos RC , Akary-Lepage E , Mistou MY , Serror P . 2015 . The surface rhamnopolysaccharide epa of Enterococcus faecalis is a key determinant of intestinal colonization . Journal of Infectious Diseases 211 : 62 – 71 . OpenUrl CrossRef PubMed 114. ↵ Bourgogne A , Thomson LC , Murray BE . 2010 . Bicarbonate enhances expression of the endocarditis and biofilm associated pilus locus, ebpR-ebpABC, in Enterococcus faecalis . BMC Microbiol 10 . 115. ↵ Dai Z , Koehler TM . 1997 . Regulation of anthrax toxin activator gene ( atx A) expression in Bacillus anthracis : temperature, not CO2/bicarbonate, affects atxA synthesis . Infect Immun 65 . 116. ↵ Caparon MG , Geist RT , Perez-Casal J , Scott JR . 1992 . Environmental regulation of virulence in group A streptococci: transcription of the gene encoding M protein is stimulated by carbon dioxide . J Bacteriol 174 . 117. ↵ Cummins EP , Selfridge AC , Sporn PH , Sznajder JI , Taylor CT . 2014 . Carbon dioxide-sensing in organisms and its implications for human disease . Cellular and Molecular Life Sciences 71 . 118. ↵ Bahn YS , Cox GM , Perfect JR , Heitman J . 2005 . Carbonic anhydrase and CO2 sensing during Cryptococcus neoformans growth, differentiation, and virulence . Current Biology 15 . 119. ↵ Mulkerrins KB . 2023 . CRISPR locus combination affects the ecology of Enterococcus faecalis . MSc thesis. University of Massachusetts Boston . 120. ↵ Mulkerrins KB , Lyons C , Shiaris MP . 2021 . Draft genome sequence of Enterococcus faecalis AS003, a strain possessing all three type II-A CRISPR loci . Microbiol Resour Announc 10 . 121. ↵ Tamames J . 2001 . Evolution of gene order conservation in prokaryotes . 122. ↵ Janausch IG , Zientz E , Tran QH , Kröger A , Unden G . 2002 . C4-dicarboxylate carriers and sensors in bacteria . Biochim Biophys Acta Bioenerg 1553 . 123. ↵ He Q , Hou Q , Wang Y , Li J , Li W , Kwok LY , Sun Z , Zhang H , Zhong Z . 2018 . Comparative genomic analysis of Enterococcus faecalis : insights into their environmental adaptations . BMC Genomics 19 . 124. ↵ Frank KL , Colomer-Winter C , Grindle SM , Lemos JA , Schlievert PM , Dunny GM . 2014 . Transcriptome analysis of Enterococcus faecalis during mammalian infection shows cells undergo adaptation and exist in a stringent response state . PLoS One 9 . 125. ↵ Vebø HC , Solheim M , Snipen L , Nes IF , Brede DA . 2010 . Comparative genomic analysis of pathogenic and probiotic Enterococcus faecalis isolates, and their transcriptional responses to growth in human urine . PLoS One 5 : e12489 . OpenUrl CrossRef PubMed 126. ↵ Tasevska N , Runswick SA , McTaggart A , Bingham SA . 2005 . Urinary sucrose and fructose as biomarkers for sugar consumption . Cancer Epidemiology Biomarkers and Prevention 14 . 127. ↵ Maadani A , Fox KA , Mylonakis E , Garsin DA . 2007 . Enterococcus faecalis mutations affecting virulence in the Caenorhabditis elegans model host . Infect Immun 75 . 128. ↵ Kim MA , Rosa V , Min KS . 2020 . Characterization of Enterococcus faecalis in different culture conditions . Sci Rep 10 . 129. ↵ Bøhle LA , Færgestad EM , Veiseth-Kent E , Steinmoen H , Nes IF , Eijsink VGH , Mathiesen G . 2010 . Identification of proteins related to the stress response in Enterococcus faecalis V583 caused by bovine bile . Proteome Sci 8 . 130. ↵ Pfeiler EA , Azcarate-Peril MA , Klaenhammer TR . 2007 . Characterization of a novel bile-inducible operon encoding a two-component regulatory system in Lactobacillus acidophilus . J Bacteriol 189 . 131. ↵ Begley M , Gahan CGM , Hill C . 2005 . The interaction between bacteria and bile . FEMS Microbiol Rev 29 . 132. ↵ Une M , Hoshita T . 1994 . Natural occurrence and chemical synthesis of bile alcohols, higher bile acids, and short side chain bile acids . Hiroshima J Med Sci 43 . 133. Hofmann AF , Hagey LR , Krasowski MD . 2010 . Bile salts of vertebrates: structural variation and possible evolutionary significance . J Lipid Res 51 . 134. ↵ Haslewood GA . 1967 . Bile salt evolution . J Lipid Res 8 . 135. ↵ Garsin DA , Frank KL , Silanpää J , Ausubel FM , Hartke A , Shankar N , Murray BE . 2014 . Pathogenesis and models of enterococcal infection in enterococci: from commensals to leading causes of drug resistant infection . Massachusetts Eye and Ear Infirmary . 136. ↵ Muegge BD , Kuczynski J , Knights D , Clemente JC , González A , Fontana L , Henrissat B , Knight R , Gordon JI . 2011 . Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans . Science (1979) 332 . 137. ↵ Petersen A , Chadfield MS , Christensen JP , Christensen H , Bisgaard M . 2008 . Characterization of small-colony variants of Enterococcus faecalis isolated from chickens with amyloid arthropathy . J Clin Microbiol 46 . 138. ↵ Waite DW , Taylor MW . 2015 . Exploring the avian gut microbiota: current trends and future directions . Front Microbiol 6 . 139. ↵ Chaillou S , Chaulot-Talmon A , Caekebeke H , Cardinal M , Christieans S , Denis C , Hélène Desmonts M , Dousset X , Feurer C , Hamon E , Joffraud JJ , La Carbona S , Leroi F , Leroy S , Lorre S , Macé S , Pilet MF , Prévost H , Rivollier M , Roux D , Talon R , Zagorec M , Champomier-Vergès MC . 2015 . Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage . ISME Journal 9 . 140. ↵ Jørgensen MG , Pandey DP , Jaskolska M , Gerdes K . 2009 . HicA of Escherichia coli defines a novel family of translation-independent mRNA interferases in bacteria and archaea . J Bacteriol 191 . 141. ↵ Winter AJ , Williams C , Isupov MN , Crocker H , Gromova M , Marsh P , Wilkinson OJ , Dillingham MS , Harmer NJ , Titball RW , Crump MP . 2018 . The molecular basis of protein toxin HicA– dependent binding of the protein antitoxin HicB to DNA . Journal of Biological Chemistry 293 . 142. ↵ Makarova KS , Grishin N V. , Koonin E V . 2006 . The HicAB cassette, a putative novel, RNA-targeting toxin-antitoxin system in archaea and bacteria . Bioinformatics 22 . 143. ↵ Pandey DP , Gerdes K . 2005 . Toxin-antitoxin loci are highly abundant in free-living but lost from host-associated prokaryotes . Nucleic Acids Res 33 . 144. ↵ Srinivasiah S , Bhavsar J , Thapar K , Liles M , Schoenfeld T , Wommack KE . 2008 . Phages across the biosphere: contrasts of viruses in soil and aquatic environments . Res Microbiol 159 . 145. Suttle CA . 2007 . Marine viruses - Major players in the global ecosystem . Nat Rev Microbiol 5 . 146. ↵ Bhaya D , Davison M , Barrangou R . 2011 . CRISPR-cas systems in bacteria and archaea: versatile small RNAs for adaptive defense and regulation . Annu Rev Genet 45 . 147. ↵ Lawson PA , Falsen E , Cotta MA , Whitehead TR . 2007 . Vagococcus elongatus sp. nov., isolated from a swine-manure storage pit . Int J Syst Evol Microbiol 57 . 148. ↵ Graille M , Zhou CZ , Receveur-Bréchot V , Collinet B , Declerck N , Van Tilbeurgh H . 2005 . Activation of the LicT transcriptional antiterminator involves a domain swing/lock mechanism provoking massive structural changes . Journal of Biological Chemistry 280 . 149. ↵ Le Coq D , Lindner C , Kruger S , Steinmetz M , Stulke J . 1995 . New β-glucoside ( bgl ) genes in Bacillus subtilis : the bgl P gene product has both transport and regulatory functions similar to those of BglF, its Escherichia coli homolog . J Bacteriol 177 . 150. ↵ Kiliç AO , Tao L , Zhang Y , Lei Y , Khammanivong A , Herzberg MC . 2004 . Involvement of Streptococcus gordonii beta-glucoside metabolism systems in adhesion, biofilm formation, and in vivo gene expression . J Bacteriol 186 . 151. ↵ Folders J , Tommassen J , Van Loon LC , Bitter W . 2000 . Identification of a chitin-binding protein secreted by Pseudomonas aeruginosa . J Bacteriol 182 . 152. ↵ Schnellmann J , Zeltins A , Blaak H , Schrempf H . 1994 . The novel lectin-like protein CHB1 is encoded by a chitin-inducible Streptomyces olivaceoviridis gene and binds specifically to crystalline α- chitin of fungi and other organisms . Mol Microbiol 13 . 153. ↵ Kirn TJ , Jude BA , Taylor RK . 2005 . A colonization factor links Vibrio cholerae environmental survival and human infection . Nature 438 . 154. ↵ Martínez JL . 2018 . Ecology and evolution of chromosomal gene transfer between environmental microorganisms and pathogens . Microbiol Spectr 6 . 155. ↵ Hao Z , Zhang W , Wang X , Wang Y , Qin X , Luo H , Huang H , Su X . 2022 . Identification of WxL and S-layer proteins from Lactobacillus brevis with the ability to bind cellulose and xylan . Int J Mol Sci 23 . 156. ↵ Shaw CN , Kim M , Eastridge ML , Yu Z . 2015 . Effects of different sources of physically effective fiber on rumen microbial populations . Animal 10 . 157. ↵ Järvå MA , Hirt H , Dunny GM , Berntsson RPA . 2020 . Polymer adhesin domains in Gram-Positive cell surface proteins . Front Microbiol 11 . 158. ↵ Yeung MK , Cisar JO . 1988 . Cloning and nucleotide sequence of a gene for Actinomyces naeslundii WVU45 type 2 fimbriae . J Bacteriol 170 . 159. ↵ Spraggon G , Koesema E , Scarselli M , Malito E , Biagini M , Norais N , Emolo C , Barocchi MA , Giusti F , Hilleringmann M , Rappuoli R , Lesley S , Covacci A , Masignani V , Ferlenghi I . 2010 . Supramolecular organization of the repetitive backbone unit of the Streptococcus pneumoniae pilus . PLoS One 5 . 160. ↵ Kwon H , Squire CJ , Young PG , Baker EN . 2014 . Autocatalytically generated Thr-Gln ester bond cross-links stabilize the repetitive Ig-domain shaft of a bacterial cell surface adhesin . Proc Natl Acad Sci USA 111 . 161. ↵ Law-Brown J , Meyers PR . 2003 . Enterococcus phoeniculicola sp. nov., a novel member of the enterococci isolated from the uropygial gland of the Red-billed Woodhoopoe, Phoeniculus purpureus . Int J Syst Evol Microbiol 53 . 162. ↵ Etzold S , Kober OI , Mackenzie DA , Tailford LE , Gunning AP , Walshaw J , Hemmings AM , Juge N . 2014 . Structural basis for adaptation of lactobacilli to gastrointestinal mucus . Environ Microbiol 16 . 163. Devi SM , Halami PM . 2017 . Diversity and evolutionary aspects of mucin binding (MucBP) domain repeats among Lactobacillus plantarum group strains through comparative genetic analysis . Syst Appl Microbiol 40 . 164. ↵ Siezen RJ , Tzeneva VA , Castioni A , Wels M , Phan HTK , Rademaker JLW , Starrenburg MJC , Kleerebezem M , van Hylckama Vlieg JET . 2010 . Phenotypic and genomic diversity of Lactobacillus plantarum strains isolated from various environmental niches . Environ Microbiol 12 . 165. ↵ Canfield GS , Duerkop BA . 2020 . Molecular mechanisms of enterococcal-bacteriophage interactions and implications for human health . Curr Opin Microbiol 56 . 166. ↵ Kjos M , Snipen L , Salehian Z , Nes IF , Diep DB . 2010 . The Abi proteins and their involvement in bacteriocin self-immunity . J Bacteriol 192 . 167. ↵ Riley MA , Wertz JE . 2002 . Bacteriocins: evolution, ecology, and application . Annu Rev Microbiol 56 . 168. ↵ Buchman GW , Banerjee S , Hansen JN . 1988 . Structure, expression, and evolution of a gene encoding the precursor of nisin, a small protein antibiotic . Journal of Biological Chemistry 263 . 169. ↵ Hawlena H , Bashey F , Lively CM . 2012 . Bacteriocin-mediated interactions within and between coexisting species . Ecol Evol 2 . 170. ↵ Heilbronner S , Krismer B , Brötz-Oesterhelt H , Peschel A . 2021 . The microbiome-shaping roles of bacteriocins . Nat Rev Microbiol 19 . 171. ↵ Campillo T , Renoud S , Kerzaon I , Vial L , Baude J , Gaillard V , Bellvert F , Chamignon C , Comte G , Nesme X , Lavire C , Hommais F . 2014 . Analysis of hydroxycinnamic acid degradation in Agrobacterium fabrum reveals a coenzyme a-dependent, beta-oxidative deacetylation pathway . Appl Environ Microbiol 80 . 172. ↵ Materna AC , Friedman J , Bauer C , David C , Chen S , Huang IB , Gillens A , Clarke SA , Polz MF , Alm EJ . 2012 . Shape and evolution of the fundamental niche in marine Vibrio . ISME Journal 6 . 173. ↵ Sriswasdi S , Yang CC , Iwasaki W . 2017 . Generalist species drive microbial dispersion and evolution . Nat Commun 8 . 174. ↵ Xu Q , Luo G , Guo J , Xiao Y , Zhang F , Guo S , Ling N , Shen Q . 2022 . Microbial generalist or specialist: intraspecific variation and dormancy potential matter . Mol Ecol 31 : 161 – 173 . OpenUrl CrossRef 175. ↵ Hourigan D , Stefanovic E , Hill C , Ross RP . 2024 . Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy . BMC Microbiol 24 . 176. ↵ Andam CP . 2019 . Clonal yet different: understanding the causes of genomic heterogeneity in microbial species and impacts on public health . mSystems 4 . 177. ↵ Croucher NJ , Klugman KP . 2014 . The emergence of bacterial “hopeful monsters.” mBio 5 . 178. ↵ Baquero F , Coque TM , Galán JC , Martinez JL . 2021 . The origin of niches and species in the bacterial world . Front Microbiol 12 . View the discussion thread. Back to top Previous Next Posted May 07, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies Vitor Luis Macena Leite , Adriana Rocha Faria , Clara Ferreira Guerra , Stephanie da Silva Rodrigues Souza , Andréa de Andrade Rangel Freitas , Jaqueline Martins Morais , Vânia Lúcia Carreira Merquior , Paul J. Planet , Lúcia Martins Teixeira bioRxiv 2025.05.05.652174; doi: https://doi.org/10.1101/2025.05.05.652174 Share This Article: Copy Citation Tools Hidden Diversity in Enterococcus faecalis Revealed by CRISPR2 Screening: Eco-evolutionary Insights into a Novel Subspecies Vitor Luis Macena Leite , Adriana Rocha Faria , Clara Ferreira Guerra , Stephanie da Silva Rodrigues Souza , Andréa de Andrade Rangel Freitas , Jaqueline Martins Morais , Vânia Lúcia Carreira Merquior , Paul J. Planet , Lúcia Martins Teixeira bioRxiv 2025.05.05.652174; doi: https://doi.org/10.1101/2025.05.05.652174 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17636) Bioengineering (13860) Bioinformatics (41847) Biophysics (21401) Cancer Biology (18536) Cell Biology (25424) Clinical Trials (138) Developmental Biology (13353) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15583) Genomics (22463) Immunology (17701) Microbiology (40300) Molecular Biology (17141) Neuroscience (88434) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9808) Zoology (2268)