Pangenomics insights of enterococcus faecium human isolates and identification of novel therapeutic targets by in silico subtractive genomics.

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Learn more: PMC Disclaimer | PMC Copyright Notice Braz J Microbiol . 2026 Jun 3;57(1):166. doi: 10.1007/s42770-026-01983-z Pangenomics insights of enterococcus faecium human isolates and identification of novel therapeutic targets by in silico subtractive genomics Yngrid Victória Cassiano Mascarenhas 1, # , Andrei Giacchetto Felice 2, # , Felipe Lucas Zen 2 , Victor Augusto Sallum Ceballos 2 , Siomar de Castro Soares 2, 3, ✉ Author information Article notes Copyright and License information 1 Laboratory of Neurobiology, Department of Physiology, Federal University of São Paulo, São Paulo, Brazil 2 Laboratory of Immunology and Bioinformatics, Department of Microbiology, Immunology and Parasitology, Federal University of Triângulo Mineiro, Uberaba, MG Brazil 3 Laboratory of Immunology and Bioinformatics, Department of Microbiology, Immunology and Parasitology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, MG CEP: 38025- 180 Brazil Communicated by Guilherme Sgobbi Zagui. ✉ Corresponding author. # Contributed equally. Received 2025 Dec 9; Accepted 2026 May 25; Collection date 2026 Dec. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . PMC Copyright notice PMCID: PMC13234099  PMID: 42234326 Abstract Enterococcus faecium is a Gram-positive bacteria that infects the human gastrointestinal tract and it is a leading cause of hospital-acquired infections, due to its ability to cause various types of infections, such as endocarditis, bacteremia, urinary tract infections, and others, exacerbated by its multidrug resistance, notably to vancomycin. Because they are linked to major infections that are difficult to manage, and also due to the widespread acquisition of resistance genes, management remains difficult. This study employs pangenomic analyses and subtractive genomics to explore genetic diversity and identify novel therapeutic targets across 20 human-derived E. faecium genomes. Phylogenomic analyses revealed four distinct clades, with genomic rearrangements and horizontal gene transfer events underscoring adaptive evolution. Comparative genomics identified 20 pathogenicity islands and 12 resistance islands, alongside pan-resistome profiling highlighting prevalent resistance to aminoglycosides, elfamycins, and glycopeptides (e.g., vancomycin in 14/20 strains). Core genome analyses, filtered for non-human homologs, prioritized cytoplasmic proteins critical for survival. Subtractive genomics predicted five high-confidence drug targets: phosphocarrier protein HPr (metabolic regulation), GNAT family N-acetyltransferase (antibiotic resistance), translation initiation factor IF-1 (protein synthesis), HU family DNA-binding protein (genome stability), and a sugar-binding domain protein (nutrient uptake). Structural modeling identified these targets as druggable with conserved roles in bacterial viability. This integrative approach elucidates E. faecium’s genomic plasticity and resistance mechanisms while proposing candidates for targeted therapies, addressing the urgent need for novel interventions against this resilient pathogen. Supplementary Information The online version contains supplementary material available at 10.1007/s42770-026-01983-z. Keywords: Enterococcus faecium , Pangenomics, Drug targets Introduction Enterococcus and enterococcus faecium genus The Enterococcus genus is a group of Gram-positive bacteria belonging to the Enterococcaceae family, which can be alpha-hemolytic or non-hemolytic, and may present themselves in pairs or short chains. This genus includes several species, with Enterococcus faecalis and Enterococcus faecium being the most common and clinically relevant. The gastrointestinal tract is the main human reservoir for Enterococcus; although they are also found in the oral cavity, gallbladder, urethra and vagina, less frequently. In feces of healthy adults, it is common to find high concentrations of E. faecalis , 90 to 95% of the species are found, while E. faecium is detected in lower concentrations, ranging from 5 to 10% [ 1 ]. Bacteria belonging to the Enterococcus genus were initially classified as Gram-positive enteric cocci and, with the implementation of the Lancefield serological screening system in the 1930 s, they were included in the genus Streptococcus of group D. Later, in 1984, the classification of these species, now in the genus Enterococcus , was established based on DNA-rRNA and 16 S rRNA homology studies, which revealed significant differences between Streptococcus and Enterococcus , leading to the reclassification of these microorganisms [ 2 ]. Since then, two distinct species of this genus, E. faecalis and E. faecium , have gained greater notoriety as some of the most frequently found nosocomial pathogens in healthcare settings worldwide [ 3 ]. Currently, E. faecium , for example, corresponds to the third largest cause of hospital infection, in addition to emerging as an important opportunistic pathogen that causes infections in the community, especially in immunocompromised individuals or with associated comorbidities [ 4 ]. In hospital settings, this genus can cause medical device-related infections, such as catheter-associated urinary tract infections or intravenous catheter-related bloodstream infections. They may also be responsible for respiratory tract infections, surgical wound infections, and other nosocomial infections. In the community, the infection caused by this pathogen is more related to people with compromised health conditions, and can lead to urinary tract infections, skin and soft tissue infections, and intra-abdominal infections. Enterococci are linked to a considerable number of difficult-to-manage infections, due to the acquisition of genes that confer resistance to current treatments, which results in the deterioration of the patient’s condition and, consequently, in the worsening of the clinical condition [ 5 ]. In recent decades, there has been a significant increase in the rates of acquired antimicrobial resistance in E. faecium , including resistance to vancomycin, an antibiotic of critical importance for the treatment of severe bacterial infections [ 6 ]. Serious infections caused by vancomycin-resistant E. faecium (VRE) have consistently presented clinical challenges, requiring combined therapeutic approaches and management of treatment-associated toxicity. Despite the introduction of new antibiotics with efficacy against VRE in the therapeutic arsenal, significant challenges remain. An understanding of the drivers of resistance development in VRE, the dynamics of gastrointestinal colonization and resistance to microbiota-mediated colonization, as well as the mechanisms of resistance to currently available therapies, will allow healthcare professionals to be better prepared to address these challenges associated with hospital-acquired pathogens [ 7 ]. To achieve effective prevention and reduction of nosocomial infections caused by VRE, it is crucial to also direct efforts to decrease the transport and spread of VRE. Effective control of E. faecium and other resistant bacteria requires the implementation of appropriate infection prevention and control measures, such as hand hygiene, proper hygiene practices, isolation of infected patients, prudent use of antibiotics, and education of healthcare providers on the appropriate use of antimicrobials [ 8 ]. In addition, continued research and development of new antimicrobials and alternative therapies, along with efforts to improve awareness of antimicrobial resistance, are essential to combat the spread of resistant E. faecium and improve the treatment of these infections. In this context, it is important to analyze the pan-genomics of this species, an area introduced by Tettelin et al. in 2005 [ 9 ], which presents a more comprehensive view of the genome of a species or genus. This approach involves the comparative analyses of multiple genomes of different isolates of the same species in order to identify the complete and non-redundant repertoire of genes present in the compared genomes. The availability of a large number of genomes of different isolates of the same pathogen has provided the opportunity to investigate genomic characteristics intrinsic to the species [ 10 ]. In this way, subtractive genomics becomes an interesting area to analyze these genes found in pan-genomics and identify those responsible for survival mechanisms that may become possible new drug targets [ 11 ]. Materials and methods Pan-genomic analysis Genome selection In order to perform pan-genomic analyses with strains isolated from the human host, the genomes of different strains of the species E. faecium that are deposited in a public database, the National Center for Biotechnology Information (NCBI - https://www.ncbi.nlm.nih.gov/ ) , were used. Since there are more than 30 thousand genomes deposited in this database, we initially filtered to those that were annotated and are present in the Refseq database and excluded atypical genomes, leaving 4083 genomes. This step was also necessary to warrant the quality of the data and homogeneity of the annotation. Also due to computational and software limitations, it was necessary to reduce the dataset even more; then, we used the phylogenetic tree provided by the NCBI taxonomy to select 20 genomes isolated from human hosts that would be representative of the clades found. Some information on these genomes is available in Table 1 . Table 1. Enterococcus faecium genome information Organism/ Name Strain Size (Mb) GC (%) Genes Proteins Geographical location Host Assembly ID Enterococcus faecium E8377 3.253 37.5 3.237 3.049 Netherlands Human hospitalized patient GCF_900639595.1 Enterococcus faecium Aus0004 3.020 38 3.118 2.919 Australia Human GCF_000250945.1 Enterococcus faecium E745 3.168 37.5 3.149 2.943 Netherlands Human hospitalized patient GCF_001750885.1 Enterococcus faecium DO 3.053 37.5 3.007 2.779 United States of América Human GCF_000174395.2 Enterococcus faecium E6055 3.040 37.5 2.996 2.798 - - GCF_900639345.1 Enterococcus faecium E8423 2.998 37.5 2.959 2.754 Netherlands Human GCF_900639725.1 Enterococcus faecium Aus0085 3.239 37.5 3.271 2.960 - - GCF_000444405.1 Enterococcus faecium AALTL 3.112 37.5 3.058 2.854 United States of América Human GCF_002880635.1 Enterococcus faecium Efaecium_ ER04526.5 A 3.126 37.5 3.087 2.882 United States of América Human GCF_002848725.1 Enterococcus faecium E243 3.141 37.5 3.111 2.905 United States of América Human GCF_002761275.1 Enterococcus faecium Dallas 131_2 3.186 37.5 3.117 2.921 United States of América Human GCF_016415465.1 Enterococcus faecium 17–508 2.874 38 2.849 2.644 France Human GCF_018517045.1 Enterococcus faecium NCTC7174 2.450 38 2.364 2.176 - - GCF_900637035.1 Enterococcus faecium NRRL B-2354 2.850 37.5 2.773 2.615 - Milk and dairy utensils GCF_000336405.1 Enterococcus faecium EFE10021 2.615 38 2.495 2.368 - Human GCF_900066025.1 Enterococcus faecium VB6521 3.248 37.5 3.193 3.009 India Human GCF_017898005.1 Enterococcus faecium VRE1 3.098 37.5 3.053 2.860 China Human GCF_006007925.1 Enterococcus faecium SRR24 2.919 37.5 2.887 2.714 China Human hospitalized patient GCF_009734005.1 Enterococcus faecium A7214 3.109 37.5 3.080 2.866 India Human GCF_012933165.2 Enterococcus faecium T110 2.738 38 2.647 2.515 - - GCF_000737555.1 Open in a new tab In addition, 1 genome of the species Enterococcus casseliflavus EC20 (GCF_000157355.2) was chosen as an outgroup for phylogenomic analyses. In order to perform the prediction of the pathogenicity islands (PAIs) and resistance islands (RIs), another outgroup was used, Enterococcus gallinarum FDAARGOS-163 (GCF_001558875.2), totaling 24 genomes in the analyses. These species used as outgroups are not commonly pathogenic, but the literature mentions that they may be indirectly associated with some pathology, for this reason it was decided to use two species commonly known to be non-pathogenic in healthy individuals (opportunistic pathogens) to reduce the odds of false negative predictions of PAIs. These genomes were obtained through the ftp protocol, in faa and fna formats, using in-house scripts . All genomes used in this work were downloaded in December, 20, 2023. Phylogenomic and phylogenetic analysis The phylogenomic analyses were carried out considering the Gegenees software [ 12 ] strategy, in which, through BLAST (Basic Local Alignment Search Tool), it produces a distance matrix based on the percentage of similarity between the genomic fragments of the analyzed species. The 21 genomes (20 E. faecium + 1 E. casseliflavus lineages) were deposited for analyses in this software, using the custom method of comparison, with 200 bp for the fragment size, 100 bp for the step size and BLASTn; and, the result was plotted on a heat map. The resulting distance matrix could be exported in nexus format to be used in the SplitsTree4 software [ 13 ], which aimed to infer the phylogenetic tree. For this, phylogenetic inference was used by the Neighbor-Joining method. Average nucleotide identity Given the results of the previous software, we carried out a more in-depth analysis of the E. faecium T110 lineage, due to the atypical behavior found. For this purpose, the Average Nucleotide Identity Analysis (ANI calculator) program from the Kostas lab website ( http://enve-omics.ce.gatech.edu/ani/index ) was used, which serves as a widely used measure in comparative genomics to assess the similarity between two genomes at the nucleotide level [ 14 ]. The program was used to perform two comparisons against the T110 lineage, the first against the reference species of the genus of interest, the E. faecium SRR24 lineage; and the second against the Streptococcus pyogenes lineage NCTC12064 (GCF_900475035.1). The program was used in its default configurations, and for the alignment options, 700 bp was used as the minimum size, 70% as the minimum identity and 50 as the minimum alignment; and, for fragment options, 1000 bp for window size and 200 bp as step size. Gene synteny For the gene synteny analysis, the Mauve software [ 15 ] was used, where 2 groups of analyses were performed, the first one using all 21 genomes (20 E. faecium + 1 E. casseliflavus ) and a second one using only the clusters found in the SplitsTree4 software. Mauve was used to identify genomic rearrangements, inversions, duplications, and deletions, in addition to facilitating the visualization of similarities and differences between the genomes analyzed. The program was used in its default settings, being: the matrix resulting by the HOXD method, gap open score of −400, gap extend score of −30 and a match seed weight of 15. Genome plasticity analysis and genomic island prediction Genomic Island Prediction Software (GIPSy) is a software developed for the identification and analysis of genomic islands in DNA sequences, which are segments often associated with horizontal gene transfer [ 16 ]. These segments have a GC content different from the organism analyzed, a deviation in the codon usage, have the presence of transposases and are usually larger than 6 kb. These regions can be further classified as presence of antibiotic resistance genes, and PAIs, with high concentrations of virulence factors. Two analyses were carried out with 2 opportunistic outgroups, being: E. faecium SRR24 x E. casseliflavus EC20 ; and, E. faecium SRR24 x E. gallinarum FDAARGOS-163 . The results from GIPSy were plotted using the BLAST Ring Image Generator (BRIG), a bioinformatics visualization software that enables the comparative analyses of genomes, creating images of rings that represent the genetic similarity between different sequences identified through the use of BLAST algorithm. In this analysis, the central genome for comparison was the reference genome of E. faecium , pre-established by the NCBI ( E. faecium SRR24 ), with all formed rings being compared to this central sequence [ 17 ]. The same 21 genomes were used in this analysis and the program was used with its default settings. Pan resistome prediction For the prediction of antibiotic resistance genes (ARGs), the Pan Resistome Analysis Pipeline (PRAP) was used, a platform-independent tool developed in Python 3, which is based on the Comprehensive Antibiotic Resistance Database (CARD) and ResFinder databases. This software enables the identification of resistance-related genes in whole genomes [ 18 ]. In addition, the generated annotations are used in the characterization of the pan-resistomes, which makes it possible to analyze the distribution of ARGs among the genomes analyzed. For pan-resistome analyses, the CARD database was used exclusively, as it offers a comprehensive catalog of ARGs. The program was used with the -m N1 option, to select the CARD database, and following the rest of the default settings, running locally. Reverse vaccinology and subtractive genomics Orthofinder software [ 19 ] was used to carry out the search for orthologous proteins among the E. faecium strains. Through the Diamond and MCL algorithm, the program identifies homology regions between the genes of the species and separates these groups of orthologous proteins. After separation, in-house scripts were applied on the orthofinder result to separate these proteins into the pangenome subgroups: core genome (proteins present in all lineages), accessory genome (proteins present in more than one, but not all lineages), and singletons (present in only one lineage). In the core genome there are usually proteins that are involved in important mechanisms for the survival of the microorganism [ 11 ], so it was chosen to continue the analyses. As a way to avoid possible adverse reactions, the core genome was compared with the human proteome through the NCBI’s BLASTp tool [ 20 ] and then only the proteins of the microorganisms that did not have homology in this analysis were selected for the next step. Prediction of subcellular localization of proteins Protein subcellular localization prediction was performed using SurfG+ software [ 21 ], which allows for the identification of specific locations of proteins within the cell based on their sequences. The software separates proteins into membrane, secreted or PSE (typically used for vaccine candidate research), and cytoplasmic (typically used for drug target research). Proteins categorized as cytoplasmic were then targeted for detailed analysis to identify potential drug targets, given that they are often associated with metabolic pathways critical to the organism’s survival. This approach allows a deeper understanding of the functions of these proteins and their implications for therapeutic strategies. Selection of potential drug targets For drug target prediction, the MHOlline software was used, which constitutes a pipeline designed for the analysis of protein structures in structural genomics projects [ 22 ]. This software integrates a variety of specific programs and methods that allow performing analyses such as comparative modeling, structural quality (ranging from very high to very low), and stereochemical quality alignment (Smith & Doe, 2021; Zhang, 2020). The final modeling was separated into G0, G1, G2 and G3, as follows: G0: non-aligned sequence; G1: E-value > 10 × 10-5 or identity < 15%; G2: E-value 10 × 10-5, identity ≥ 25% and LVI (Length Variation Index) ≤ 0.7; G3: E-value ≤ 10 × 10-5, identity ≤ 15% to < 25% or LVI > 0.7. In the result, proteins with ‘very high’, ‘high’, and/or ‘medium to good’ quality can be considered good candidates and are within the G2 quality group. Functional annotation of proteins The final candidates were characterized using different software and databases, such as UniProt [ 23 ], NCBI, MHOLline, SurfG+. Table 2 summarizes this information. Table 2. Candidate proteins for drug targets in the human host against Enterococcus faecium Protein ID Name Gene MHOLline (G2 Filters Quality) Length Subcellular Location WP_002287602.1 Phosphocarrier protein HPr HPr Very high 88 aa Citoplasmatic WP_002288353.1 GNAT family N-acetyltransferase Eis Very high 406 aa Citoplasmatic WP_002288695.1 Translation initiation factor IF-1 infA Very high 72 aa Citoplasmatic WP_002290178.1 HU family DNA-binding protein Hu Very high 91 aa Citoplasmatic WP_002295134.1 Sugar-binding domain-containing protein SorC Very high 345 aa Citoplasmatic Open in a new tab ID: Identity; aa: Aminoacids Docking analyses Molecular docking analysis is a widely used computational method to investigate molecular interactions and to support drug discovery or drug repositioning [ 24 ]. For this approach, the 3D structures of each identified protein, generated using the MHOLline server, were evaluated for structural quality. The Ramachandran plots [ 25 ] for each protein are presented in Figure S1 . Subsequently, the structures were prepared using AutoDock Tools [ 26 ] to assign charges to the protein targets and to set up the docking grid boxes. DogSiteScorer, a Proteins Plus tool [ 27 ], was used to identify potential binding pockets within each 3D structure. A ligand library containing 5,008 drug-like molecules (i.e., natural products) was prepared according to the methodology described by Vilela-Rodrigues et al. [ 28 ] and used as input for molecular docking. AutoDock Vina [ 29 ] was employed to perform the docking simulations between the protein targets and each ligand. An in-house script was used to select the top 10 compounds based on binding affinity, and UCSF Chimera [ 30 ] was used to visualize and generate 3D representations of the docking interactions. Results and discussion The distance matrix reflects a high similarity between the lineages The heat map generated by the Gegenees software presents a distance matrix that reflects the similarity between genomes, ranging from 0% to 100%. In the context of this project, the heat map demonstrates a similarity range that oscillates between 60% and 100% (Fig. 1 ). Fig. 1. Open in a new tab Similarity heatmap generated with Gegenees software. Red color represents alow degree of similarity; and, green/greenish color a high degree of similarity Interestingly, when analyzing comparisons between strains of the same species ( E. faecium ), some, such as those corresponding to numbers 14, 15, 16 and 21 ( E. faecium strain NCTC7174; E. faecium strain ATCC 8459; E. faecium strain EFE10021 ; and E. faecium T110, respectively), exhibit a lower similarity, ranging from 60% to 83%, when contrasted with the other intraspecies comparisons. In addition to this observation, the E. faecium T110 strain presents the lowest similarity, even within this small clade. It is also worth noting that the addition of a species as an outgroup, E. casseliflavus EC20, obtained a variation of 2 to 4% of similarity, which was already expected since it is different from the others in the group. Given this somewhat reduced similarity in this small group, we can raise some hypotheses, such as genetic variation between lineages. These lineages may have undergone mutations or specific genomic rearrangements (Single Nucleotide Polymorphism - SNP) that differentiate them from the others, resulting in discrepancies in gene sequences and, consequently, in their similarity [ 31 ]. In addition, the adaptability of specific lineages to different environments or selective conditions can lead to changes in genes that are not present or that become divergent in other lineages [ 32 ]. This adaptability may include the acquisition of antibiotic-resistance genes or the loss of non-essential genes, impacting genetic similarity. Another relevant factor is the effect of natural selection, which may influence the divergence of lineages over time. Lineages that inhabit distinct ecological niches may have evolved to present different genetic characteristics, leading to less similarity between them [ 33 ]. Additionally, horizontal gene transfer (HGT) between bacteria may play a significant role. HGT allows lineages to acquire new functions or characteristics, and if some lineages acquired genes through this mechanism, this could explain the variation in similarity compared to other lineages [ 34 ]. Finally, limitations in the databases used for the analysis may also influence the results. If less similar strains contain genes that are not well represented in reference databases, this may result in a lower similarity score. In the phylogenetic analysis, the tree constructed by the SplitsTree4 software shows the ancestral relationships between lineages of the same species when compared with the outgroup of the species E. casseliflavus EC20 (Fig. 2 ). SplitsTree4 uses phylogenetic networks instead of traditional trees and represents the evolutionary relationships between different lineages. This type of representation is especially useful when there are conflicting signals in the data, as in the result previously analyzed (Gegenees), which can occur in recombination events, hybridization, or horizontal genetic exchanges, where it is possible to visualize these inconsistencies in a reticulated structure by this second analysis. Fig. 2. Open in a new tab Taxonomic analysis of Enterococcus faecium species generated with SplisTree4 software. The branches represent a greater proximity between the species (fraction corresponding to the aligned positions). Formation of 4 clades demarcated by a gray rectangle. The outgroup is rooting the tree ( E. casseliflavus EC20) There is the formation of four main clades with high genetic similarity between strains of the same clade compared to strains of distinct clades, which then suggests a genetic proximity. The results follow the same line found in the previous result, and even the same group found previously, and which had a slightly lower similarity than the others ( E. faecium strain NCTC7174; E. faecium strain ATCC 8459; E. faecium strain EFE10021 ) was also formed in a separate clade. This information from these clades follows the same explanation we had previously described, due to the large genetic changes/adaptations that may have occurred throughout evolution [ 35 ]. Another result observed through the use of the SplitsTree4 software is the exclusion of the E. casseliflavus EC20 and E. faecium T110 strains. In the first case, it was expected that E. casseliflavus EC20 would not be part of any clade, as it was placed as an outgroup [ 36 ]. The same serves to distinguish ancestral characteristics from characteristics derived from a group of organisms under study, known as ingroup, in this case the lineages of E. faecium . In the case of the E. faecium T110 lineage, it was something atypical deserving to be better analyzed, for this reason we chose to use the ANI program and deepen the analysis. The average nucleotide identity corroborates the findings Using the ANI program, it was possible to observe in the analysis the percentage of similarity between the E. faecium T110 and E. faecium SRR24 strains, which resulted in 94.72% (Fig. 3 A). And for the percentage of similarity between the E. faecium T110 and Streptococcus pyogenes lineage, NCTC12064 which resulted in 78.51% (Fig. 3 B). The results obtained by the ANI program highlight significant differences in the evolutionary proximity between the analyzed lineages. The 94.72% similarity between E. faecium T110 and E. faecium SRR24 indicates a close phylogenetic relationship, possibly between lineages of the same species or subspecies that share a high percentage of homologous genes. On the other hand, the similarity of only 78.51% between E. faecium T110 and S. pyogenes NCTC12064 suggests a more distant phylogenetic relationship, reflecting that they are distinct species with considerable genomic differences [ 37 ]. This lower percentage indicates that these lineages have a reduced number of genes in common, which is expected among different bacterial genera. Fig. 3. Open in a new tab Average Nucleotide Identity of Enterococcus faecium T110. In A, the percentage of similarity between the species E faecium T110 and E faecium SRR24; and, in B, the percentage of similarity between the species E faecium T110 and Streptococcus pyogenes NCTC12064 These results also allow discussions about the degree of genetic variation between lineages of the same species compared to different species and may have practical implications, such as in the identification of virulence factors, antibiotic resistance, or pathogenic potential traits shared by genetically closer lineages [ 38 ]. Data in the literature supports these arguments above. In 1990, Murray [ 2 ] described that biochemical and genetic analyses caused a group of species belonging to the genus Streptococcus to change their nomenclature to Enterococcus in 1984. An ANI of 95% corresponds to a similarity of 70% in DNA-DNA hybridization, being used to distinguish between different species [ 14 ], so this result may suggest the reason why there was a change in the nomenclature of this species. Despite their similarities, several translocation, deletion, inversion, and duplication events have occurred Using the MAUVE software, the first analysis of all the 20 genomes of E. faecium studied here (Fig. 4 ) revealed a set of genomic rearrangements, including translocations, deletions, and inversions, which reflect the evolutionary process of the species over time. This evolutionary dynamic is further corroborated by subsequent analyses (Fig. 5 ), performed with the clades identified in SplitsTree4, offering a complementary and detailed view of the phylogenetic relationships between the genomes. Fig. 4. Open in a new tab Gene synteny analyses of all 20 genomes from E. faecium . Each color bar represents blocks of neighbouring genes shared between each genome, called collinear regions. Each line plot represents each of the strains present in the work. The first line represents the reference strain used in the work Fig. 5. Open in a new tab Gene synteny analysis based on clades found in the taxonomy. Construction of multiple genome alignments performed by the Mauve software on clades previously found by the SplitsTree4 analysis. ( A ) represents the 1 st clade ( E. faecium Aus0004; E. faecium DO; E. faecium E6055; E. faecium E745; E. faecium E8377 and E. faecium E8477); ( B ) the 2nd clade ( E. faecium Aus0085; E. faecium AALTL; E. faecium Dallas 131-2; E. faecium E243; E. faecium ER04526_5A); ( C ) the 3rd clade ( E. faecium ATCC 8459 NRRL-B2354; E. faecium 17–508; E. faecium EFE10021 ; E. faecium NCTC7174); and ( D ) the 4th clade ( E. faecium A7214; E. faecium SRR24; E. faecium VB6521; E. faecium VRE1) The clades resulting from the SplitsTree4 software are organized as follows: 1 st clade containing E. faecium Aus0004 , E. faecium DO , E. faecium E6055 , E. faecium E745 , E. faecium E8377 and E. faecium E8477 ; 2nd clade with E. faecium Aus0085 , E. faecium AALTL , E. faecium Dallas 131-2 , E. faecium E243 and E. faecium ER04526_5A; 3rd clade composed of E. faecium ATCC 8459 NRRL-B2354 , E. faecium 17–508 , E. faecium EFE10021 and E. faecium NCTC7174; and 4rth clade harboring E. faecium A7214 , E. faecium SRR24 , E. faecium VB6521 and E. faecium VRE1. In Fig. 4 , there are very small and highly fragmented blocks, suggesting the strains have undergone many evolutionary events. In this case, the ones that differ the most are the E. faecium E8423 and E. faecium AALTL lineages, which underwent an inversion process in almost the entire genome, approximately 2.8 Mb in comparison to the reference genome. The other genomes presented translocation events of small portions of genes. In Fig. 5 , in general, all clusters present larger block sizes and fewer evolutionary processes, corroborating with the previous steps that these species are closer/more clonal. The 1 st clade (Fig. 5 A) and the 2nd clade (Fig. 5 B) were the ones that suffered the most from these processes, with some inversions and translocations, compared to the 3rd clade (Fig. 5 C) which had deletions and only one inversion in the pink block; and to the 4th clade (Fig. 5 D) that had deletions and a few translocations. The identification of genomic rearrangements, such as inversions and translocations, allows us to explore how these events contributed to the diversification of lineages over time, impacting the adaptation and survival of variants in different environments. Furthermore, the greater similarity between variants of the same clade, compared to the similarity between distinct lineages, suggests a stronger evolutionary proximity within clades, possibly indicating that these lineages evolved from a more recent common ancestor. This finding opens the possibility of discussing how distinct selective forces acted on each group over time. The presence of conserved regions between genomes, in turn, highlights the functional importance of these regions for the viability of lineages. Analysis of the functions associated with these regions can reveal which genes are essential and which present adaptive variations, offering clues about the biology and specific adaptations of lineages. Finally, depending on the organism studied, the analysis of conserved regions and genomic rearrangements can also provide valuable information on antibiotic resistance or other environmental factors, which is particularly relevant for strains associated with pathogenic traits or hospital environments, where genomic adaptation is essential for persistence [ 39 ]. Genome plasticity, genomic islands and horizontal gene transfer Each of the rings is representative of the strains used in the study and we can therefore observe a high clonality between the strains, with some deletion regions (in white). The results of the GIPSy analysis were obtained using two opportunistic outgroups: E. faecium SRR24 x E. casseliflavus EC20, which presented 8 PAIs and 6 RIs, and E. faecium SRR24 x E. gallinarum FDAARGOS-163, with 12 PAIs and 6 RIs. In total, 20 PAIs and 12 RIs were identified among the genomes analyzed (Fig. 6 ). Fig. 6. Open in a new tab Genomic plasticity of Enterococcus faecium strains. Rings created by the BRIG software. Each of the colored rings represents a strain used in the work. White regions on the map represent gene deletion zones compared to the reference genome, E. faecium SRR24. The four outermost rings represent the genomic islands When analyzing the circular map image, a very homogeneous group of organisms is found, sharing many gene regions, though some deletion regions (blank spaces on the map) can be found. The detection of deletion regions by using BRIG can be important for many reasons, such as analyzing the loss of adaptive genes that can signal evolution and adaptation to different environments. These regions can contribute to unique characteristics, such as antibiotic resistance or interactions with hosts, and in genetic variants, they can serve as markers to differentiate genetic lineages [ 17 ]. Lineages with distinct genetic content (such as the presence of genomic islands) may be due to horizontal acquisition of mobile genetic elements or gene duplication events. Through comparative genomics, a probable evolutionary trajectory of Enterococci has been outlined, and from the literature, it is possible to demonstrate that both E. faecalis and E. faecium arose independently, largely due to gene acquisitions via mobile elements. Enterococci have a coevolution with their hosts that extends over hundreds of millions of years and, as shown by the result of the Mauve software, there is an evolution between the E. faecium lineages. The 20 PAIs contain virulence genes that include, among others, those related to host adhesion, a crucial factor for colonization and invasion of target tissues. On the other hand, the 11 RIs reveal genes associated with antibiotic resistance, which highlights an adaptation of the strains to environments with antimicrobial selective pressure (Table S1 ). There are suggestions that hospital Enterococci have an enhanced ability to cope with extreme conditions compared to other members of the genus, which have maintained an ancestral state [ 40 ]. This concept is corroborated by the fact that E. faecium , in particular, exhibits a remarkable phenotype of tolerance to nutrient deprivation, indicating specific adaptations in this species that contribute to its environmental persistence in healthcare settings [ 3 ]. Thus, this genomic profile may indicate an increased risk to public health, highlighting the need for constant surveillance and new approaches to control and treat infections associated with these genetically diverse and adaptable lineages. Important genes were founded The PRAP analysis used the power law to calculate the pan-resistome and the core resistome. For the pan- and core-resistome, the final formulas (P = (15.317) * x (0.2810) (R 2 = 0.996) e C = (8.278) * x (−0.340 ) (R 2 = 0.996), respectively) presented R2 values lower than 1, which suggests that this set of studied species have an open pan- and core-resistome, almost close to closing, revealing that they still can add foreign ARGs through the addition of newly sequenced genomes (Fig 7 ). Fig. 7. Open in a new tab Development trends of pan-resistome and core-resistome of Enterococcus faecium genomes. Graph representing the pan- and core-resistome calculation by the power law. The graph presents the number of genomes on the X-axis and the number of antibiotic resistance genes on the Y-axis. The blue box plots represent the pan-resistome curve; the orange box plots represent the core-resistome curve; the blue dotted line represents the mean of the pan-resistome curve; the dotted line represents the mean of the core-resistome curve; the purple straight line represents the fitted pan-resistome curve; and the red straight line represents the fitted core-resistome curve The following image refers to the general analysis of the pan-resistome genes, showing a range of antibiotics to which these strains are resistant (Fig. 8 ). There is a range of antibiotics that can be used to prevent and/or treat infections caused by this pathogen, however, the emergence and proliferation of antibiotic-resistant bacterial strains have rendered a significant number of antibiotics ineffective or only marginally effective. For this reason, constant study, as well as putting this project data on the bench, can help in a more efficient search for the resistance genes demonstrated in the project. Vertical transmission of ARGs occurs from one generation to the next, while horizontal transmission can occur between different bacterial species or strains through mobile genetic elements, such as plasmids, insertion sequences, and integrative conjugative elements [ 41 ]. Fig. 8. Open in a new tab Overview of pan-resistome antibiotics. Each color in the bar graph represents the number of genes found to be resistant to the antibiotics in the legend. The genomes are represented on the X-axis and the resistance gene count for each genome is represented on the Y-axis All representative strains presented resistance genes to antibiotics of the Aminoglycoside, Elfamycin, and Peptide classes. Enterococci present intrinsic resistance to moderate concentrations of aminoglycosides, a characteristic observed in all species due to the limited penetration of these antimicrobials through the bacterial wall [ 40 ]. Acquired resistance to high concentrations of aminoglycoside antibiotics can result from mutations that decrease the affinity of the agent for the ribosome, as occurs with streptomycin (known as ribosomal resistance), or, more frequently, by the acquisition of new genes that encode enzymes capable of modifying aminoglycosides, a phenomenon called acquired resistance [ 42 ]. Elfamycins are a class of antibiotics that target the protein synthesis elongation factor, EF-Tu. Very important for the catalysis of bonds, they have important functions in the bacterial translation process [ 43 ]. About 20 years ago, it was an important antibiotic for differentiating between E. faecium and E. faecalis , since the former was normally susceptible and the latter, with the presence of considerable resistance [ 43 ]. In recent years, new approaches have been used to search for bacteria that carry self-resistance genes within biosynthetic clusters [ 44 ], which may be one of the explanations for why our genomes (recently sequenced) present such resistance, in line with what is described in the literature. Peptides are typically found in a myriad of microorganisms and often exhibit broad-spectrum antimicrobial activity against many. They have become a promising alternative, given the growing resistance to traditional antibiotics [ 45 ]. In the case of Enterococci , we see that these microorganisms have undergone modifications to the cell wall and membrane, for example, the LiaFSR-LiaX system, which is important for mediating cell envelope remodeling, thus providing cross-resistance to these peptide antibiotics and certain bacteriocins [ 46 ]. Within the Glycopeptide class, an antibiotic of interest in the Enterococci literature is Vancomycin, in which 14 of 20 strains studied presented resistance genes against it. Vancomycin-resistant Enterococci (VRE) are mainly composed of E. faecalis and E. faecium , with the latter being frequently acquired in hospital settings. The treatment of these serious VRE infections is complex, often requiring the use of combination therapies. This is due to the fact that many effective antimicrobial agents, when used alone, demonstrate only a bacteriostatic effect [ 47 ]. In addition to the information mentioned above, one notable aspect of the T110 strain is that studies such as those by Natarajan, P., and Parani, M [ 48 ]. and Im, E., et al. [ 49 ] indicate that this strain has been and continues to be tested to determine whether it qualifies as a probiotic strain. Thus, such variations related to gene percentage, similarity to other microorganisms, and the presence of resistance or virulence genes may be acceptable and plausible. 5 possible drug target candidates were found In the search for orthologous proteins, 3574 protein groups were found with the 21 genomes used in the analysis. The proteins of these groups were separated into three types of subgroups: core, shared and singletons. 1612, 1961 and 656 proteins were classified in the core, shared and singletons subgroups, respectively. The core subgroup represents the set of proteins present in all lineages, so it was selected for subsequent analyses, once the drug target has to be effective against all strains. The BLAST tool was used to compare the core proteins found and the human proteome available from NCBI (taxid: 9606). 279 proteins had no homology against the host and were submitted for subcellular localization research, of which 30 were classified as PSE, 89 as membrane, 3 secreted, and 157 as cytoplasmic. All proteins considered cytoplasmic were analyzed for potential drug target candidates by the MHOLline program, once they are normally involved in basic processes and conserved metabolic pathways. A total of 5 proteins were filtered in the G2 structural quality subtype and are plotted in Table 2 . The first protein of interest is HPr ( WP_002287602.1 ), or histidine-containing phosphocarrier, a small cytoplasmic protein that plays a crucial role in the sugar transport system known as the phosphoenolpyruvate-dependent phosphotransferase (PTS) system [ 50 ]. The second protein ( WP_002288353.1 ) is the GNAT family N-acetyltransferase, related to the N-acetyltransferase (GNAT) superfamily and associated with Gcn5. These enzymes are involved in diverse cellular activities, from antibiotic resistance to histone modifications [ 51 ]. The third ( WP_002288695.1 ) is related to the translation initiation factor IF1, a highly conserved element of the prokaryotic translation apparatus. This factor stimulates, in vitro, the initiation phase of protein synthesis [ 52 ]. The fourth drug target is the HU family DNA-binding protein ( WP_002290178.1 ), which is associated with DNA replication, recombination, and repair processes [ 53 , 54 ]. Finally, the fifth protein ( WP_002295134.1 ), a Sugar-binding domain-containing protein, encoded by the SorC gene, involves binding to carbohydrates, which encompasses monosaccharides, oligosaccharides, and polysaccharides, as well as derivatives resulting from the reduction of the carbonyl group (alditols) of monosaccharides, oxidation of hydroxyl groups to generate aldehydes, ketones or carboxylic acids, or the exchange of hydroxy groups for hydrogen atoms [ 55 ]. In general, cytoplasmic proteins are correlated with the essential survival processes of these organisms and can normally be found inside their cells [ 56 ]. Among these processes, all proteins found in this work play roles in enzymatic functions, structural biosynthesis, signaling, and even DNA replication and repair processes. Because these processes are vital to the microorganism, any change that occurs in the functionality of these proteins or even in their structure can be a key point for the start of a successful treatment of diseases caused by it. Therefore, as previously demonstrated in the literature, these targets are strongly considered potential candidates for drug targets, which favors drug research more quickly than conventional methods [ 11 , 57 ]. Drug targets and ligands interactions The information regarding the top 10 interactions between drug targets and the ZINC compounds is presented in Table S2 . The strongest interactions are highlighted in yellow. For the first drug target, WP_002287602.1 , the best interaction was observed with ZINC03840479, showing three bonding interactions (LYS28, THR80, and GLU84) and a binding affinity of −7.074. The next target, WP_002288353.1 , showed the strongest interaction with ZINC04270543, presenting four bonding interactions (VAL97, THR99, ASN105, and MET109) and a binding affinity of −10.33. The protein WP_002288695.1 interacted with ZINC03842061, with three bonding interactions (ILE47) and a binding affinity of −7.78. WP_002290178.1 showed interaction with ZINC04235972, forming three bonds (LYS4, SER31, and GLN34) and a binding affinity of −7.869. Lastly, the protein WP_002295134.1 interacted with ZINC04259094, forming four bonds (GLU98, VAL112, and GLU133 twice) and a binding affinity of −9.12 (Fig. 9 ). Fig. 9. Open in a new tab Docking interactions among proteins and ZINC Compound. Each 3D image represents the proteins interacting with your best ZINC Compound. The proteins are colored in gold, and the compound in shades of blue. The link interaction is a green line among the molecules In general, ZINC compounds are mainly composed of natural products, which are chemical substances produced by living organisms. These include small molecules involved in metabolic reactions, whether essential or non-essential for survival. Due to these characteristics, natural products are frequently used in virtual screening studies, either for drug discovery or for identifying biological activity. Additionally, they are generally more accessible and cost-effective in modern pharmaceutical research [ 58 ]. Limitations of work This study initially involves an analysis of a small cluster of selected genomes from clades on the NCBI phylogenetic tree. Therefore, it cannot be definitively stated that all the genes identified here are present in other lineages of the same species; consequently, this information requires confirmation. Furthermore, since this is an in silico study, all findings presented here are theoretical in nature, intended to advance our understanding of the microorganism in question and help reduce the time required to develop new therapeutic treatments; consequently, the information presented here requires in vitro and in vivo confirmation. The research group is addressing all of these limitations, and they are expected to be resolved soon in new publications on the subject. Conclusions The study on the Enterococcus genus, with a focus on E. faecium , highlighted its clinical relevance and complexity, particularly in the context of nosocomial infections and antimicrobial resistance. Pan-genomic and phylogenetic analyses revealed remarkable genetic diversity, characterized by genomic rearrangements, horizontal gene transfer, and the presence of pathogenicity and resistance islands, which contribute to the adaptation and persistence of this pathogen in hospital environments. The identification of antibiotic resistance genes, including those associated with vancomycin resistance, underscores the urgent need for novel antimicrobial therapies and combination treatment strategies. The application of reverse vaccinology and orthologous protein analysis identified five essential cytoplasmic proteins as potential therapeutic targets. These proteins, involved in critical processes such as sugar transport, histone modification, translation initiation, and DNA repair, represent promising targets for the development of innovative therapeutic approaches against E. faecium . These findings emphasize the importance of continuous genomic surveillance and advancements in the identification of novel therapeutic targets to combat antimicrobial resistance. Strategies such as prudent antibiotic use, strengthened infection control measures, and investment in research focused on comparative genomics and molecular biology are crucial to addressing the challenges posed by E. faecium . Integrating insights into resistance and virulence mechanisms with the development of targeted therapies could significantly reduce the morbidity and mortality associated with infections caused by this pathogen. Electronic Supplementary Material Below is the link to the electronic supplementary material. Supplementary Material 1 (DOCX 335 KB) (335.5KB, docx) Author contributions Y. V. C. M and A.G.F. performed the download, all data processing and analysis, participated in the study design and wrote this manuscript; F. L. Z. helped in the processing and in the writing of this manuscript; V. A. S. C. participated in manuscript review by contributing suggestions and corrections; S.d.C.S. designed, coordinated the study, helped with analysis and wrote this manuscript. Funding The Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614). This work was supported by the Fundação de Amparo à Pesquisa do Estado Minas Gerais (FAPEMIG; Grant Number: APQ-01323-15), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Finance code 001). Data availability The genomes are available at Refseq as described in Table 1 . Declarations Competing interests All authors declare that they have no competing interests with this manuscript. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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