https://orcid.org/0000-0002-0064-0265
Figures
Abstract
Nowadays, the emergence of some microbial species resistant to antibiotics, both gram-positive and gram-negative bacteria, is due to changes in molecular activities, biological processes and their cellular structure in order to survive. The aim of the gene network analysis for the drug-resistant Enterococcus faecium as gram-positive and Salmonella Typhimurium as gram-negative bacteria was to gain insights into the important interactions between hub genes involved in key molecular pathways associated with cellular adaptations and the comparison of survival mechanisms of these two bacteria exposed to ciprofloxacin. To identify the gene clusters and hub genes, the gene networks in drug-resistant E. faecium and S. Typhimurium were analyzed using Cytoscape. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. In addition, the interaction between milRNAs and up-regulated genes was predicted. RcsC and D920_01853 have been identified as the most important of the hub genes in S. Typhimurium and E. faecium, respectively. The enrichment analysis of hub genes revealed the importance of efflux pumps, and different enzymatic and binding activities in both bacteria. However, E. faecium specifically increases phospholipid biosynthesis and isopentenyl diphosphate biosynthesis, whereas S. Typhimurium focuses on phosphorelay signal transduction, transcriptional regulation, and protein autophosphorylation. The similarities in the GO findings of the promoters suggest common pathways for survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport and cellular homeostasis. The genes with the most interactions with milRNAs include dpiB, rcsC and kdpD in S. Typhimurium and EFAU004_01228, EFAU004_02016 and EFAU004_00870 in E. faecium, respectively. The results showed that gram-positive and gram-negative bacteria have different mechanisms to survive under antibiotic stress. By deciphering their intricate adaptations, we can develop more effective therapeutic approaches and combat the challenges posed by multidrug-resistant bacteria.
Citation: Davati N, Ghorbani A (2024) Comparison of the antibiotic resistance mechanisms in a gram-positive and a gram-negative bacterium by gene networks analysis. PLoS ONE 19(11): e0311434. https://doi.org/10.1371/journal.pone.0311434
Editor: Guadalupe Virginia Nevárez-Moorillón, Universidad Autonoma de Chihuahua, MEXICO
Received: May 31, 2024; Accepted: September 20, 2024; Published: November 15, 2024
Copyright: © 2024 Davati, Ghorbani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: They authors declare that they have no conflict of interest and have no relevant financial or non-financial interests to disclose.
1. Introduction
Fluoroquinolone antibiotics (FQs), as synthetic antibacterial drugs, are used to treat a variety of bacterial infections. However, improper usage of these drugs has increased bacteria’s resistance to FQ and microbial infections become difficult to treat [1]. Ciprofloxacin is an antibiotic that belongs to the fluoroquinolone (FQ) class and is used to treat both gram-positive and gram-negative bacteria [2,3]. ESKAPE is one of the most significant bacterial groups implicated in infections, including Staphylococcus aureus, Enterococcus faecium, Acinetobacter baumannii, Enterobacter spp., Pseudomonas aeruginosa, and Klebsiella pneumoniae, involved in infections and distinguished by its resistance to multidrug [4]. Recently, there have been several reports of ciprofloxacin resistance in Salmonella Typhimurium, Bacillus anthracis, E. faecium, Enterococci spp., Neisseria gonorrhoeae, K. pneumoniae, P. aeruginosa, and Escherichia coli [5–8]. DNA replication, repair, and recombination of bacteria are affected by FQs through inhibiting DNA gyrase (gyrA) enzymes [6]. Hence, the mutations in the gyrA gene or efflux pumps may be the cause of this drug resistance [9]. These antibiotic-resistant species can be transmitted to humans through food. Davati (2022) reported that the local cheeses could be responsible for transmitting a variety of antibiotic-resistant microbial species, special Enterococcus spp. [10]. In addition to the selection advantage derived from both innate and acquired antibiotic resistance features, E. faecium’s remarkable genomic flexibility and adaptable metabolism enable it to withstand a wide range of environmental stressors [11]. To prevent the development and spread of FQ resistance, it is crucial to understand the mechanisms underlying bacteria with increased drug resistance. Investigating gene networks can shed light on the relationships between genes and proteins linked to particular biochemical processes, which can help us comprehend the physiological state of the organism [12,13]. Following previous studies and in agreement with Li, Dai [8], who pointed out the need to perform gene network analysis of FQs-resistant Salmonella in further studies and also to compare the gene network in this FQs-resistant gram-negative bacteria with the gene network in an FQs-resistant gram-positive bacteria, the aim of this study was to investigate the gene interaction networks of the bacteria E. faecium (gram-positive) and S. Typhimurium (gram-negative) in order to identify key gene clusters and hub genes associated with their response to ciprofloxacin. Analysis of previously reported differentially expressed genes (DEGs) of these drug-resistant bacteria [7,8] revealed crucial molecular pathways and adaptations in E. faecium and S. Typhimurium when exposed to ciprofloxacin. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. The survival mechanisms of these two bacteria were then compared. The results could contribute to the development of more effective therapeutic approaches and to combating the challenges posed by multidrug-resistant bacteria.
2. Materials and methods
2.1. Selection of DEGs
Data were selected from two appropriate articles [7,8] and obtained as follows. The gene expression data of the drug-resistant species of E. faecium and S. Typhimurium were downloaded from the supplementary data of https://doi.org/10.1128/aac.02763-16 [7] under Gene Expression Omnibus ID: GSE94507 and https://doi.org/10.1371/journal.pone.0175234 [8] under SRA ID: SRP100813. In this study, the up-regulated genes with fold change > 2 (p < 0.05) associated with drug-resistant mechanisms were evaluated for the comparison of drug-resistant E. faecium and drug-resistant S. Typhimurium. The count of retrieved up- and down-regulated genes found in drug-resistant species of Salmonella Typhimurium and Enterococcus faecium were listed in S1 Table.
2.2. The Retrieval of the protein-protein interaction (PPI) networks
To understand the interactions between the significantly up-regulated genes of E. faecium and S. Typhimurium, the PPI networks were created using the STRING database (version 12) (https://string-db.org/). E. faecium and Salmonella enterica subsp. enterica serovar Typhimurium were selected as reference organisms. The significant interactions with a medium confidence value of more than 0.4 were selected as significant interactions separately for E. faecium and S. Typhimurium [14].
2.3. PPIs networks analysis
The analysis of the PPI networks retrieved from the STRING database was performed with Cytoscape-v3.9.1. The CytoHubba plugin was used to identify the hub genes. The hub genes have the most interactions with other genes in the networks and play an important role in the gene network structures. The topological algorithms Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), and Degree were used to determine the top genes in the PPI networks [15]. Cluster analysis of the PPI network was carried out using the Identifying Protein Complex Algorithm (IPCA) of the Cytocluster plugin as a density-based clustering algorithm to predict protein complex clusters [16]. The following settings were considered:
Tin threshold = 0.5, shortest path length = 2, Complex size threshold = 10.
IPCA is a density-based clustering algorithm that can detect dense subgraphs in PPI networks. Based on their connectivity values, the clusters were scored and the most significant protein complex clusters (rank 1–4) were found and displayed in the network.
2.4. Pathway enrichment and Gene Ontology (GO)
Pathway enrichment and GO analyses were carried out to gain insights into the molecular functions and biological processes related to the hub genes and their connections. The STRING database was used to perform pathway enrichment analysis on the networks containing the hub genes and their interactions. The enriched pathways and biological processes that are statistically overrepresented among the up-regulated hub genes can be identified by the pathway enrichment analysis. The results of the pathway enrichment analysis provide important insights into the functional significance of hub genes in the context of antibiotic resistance. The hub genes were categorized according to cellular components (CC), molecular functions (MF), and biological processes (BP) using GO analysis [17]. The search for the functional annotations and features of these antibiotic-resistance genes is enabled by assigning GO keywords to the hub genes. Based on the annotations derived from GO analysis, the genes can be categorized into different functional groups, which provides important information about the functions and interactions of individual genes within cellular processes. The cellular components, biological processes, and molecular functions that are especially relevant in the context of antibiotic resistance are highlighted by the GO keywords associated with the hub genes.
2.5. Promoter analysis of Hub genes
We aimed to uncover putative regulatory elements (cis-elements) involved in the response to antibiotic treatment by examining the promoter regions of the differentially expressed hub genes. The 200 kbp upstream flanking regions of the hub up-regulated genes as promoter sequences were obtained from NCBI. The database of prokaryote DNA for the bacterial TF motif with threshold E-value < 10 was used to identify the known motifs using the motif comparison tool (Tomtom) version 5.5.5 (https://meme-suite.org/meme/tools/tomtom) [18]. GO for motifs (GOMo) version 5.5.5 was then used to determine the potential roles of the detected motifs (https://meme-suite.org/meme/tools/gomo) [19].
2.6. Prediction of up-regulated genes interaction with miRNAs
Data on small RNAs, including microRNA-like RNA fragments (miRNA), are from https://www.mirbase.org. The interaction between miRNAs and up-regulated genes of S. Typhimurium and E. faecium was predicted using the analysis server psRNATarget (2017 Update) (http://www/.zhaolab.org/psRNATarget/) by entering genes as targets and miRNAs as queries. In general, the following settings were considered: Number of top targets: 200, the penalty for G:U pair: 0.5, extra weight in the seed region: 1.5, number of allowed mismatches in the seed region: 2, the penalty for the opening gap: 2, expectation: 5, the penalty for other mismatches: 1, seed region: 2–13 NT, HSP size: 19, the penalty for extending gap: 0.5, and translation inhibition range: 10 NT-11 NT [20]. Interaction networks of miRNAs with genes and were created using Cytoscape (version 3.9.1). The nodes in this network consisted of miRNAs, while the genes acted as targets of the miRNAs. The CytoHubba plugin was used to identify the genes that have the most interactions with miRNAs in the S. Typhimurium and E. faecium networks using Degree’s topological algorithm.
3. Results
Ciprofloxacin has been used for about thirty years to treat a variety of illnesses, such as urinary tract infections, endocarditis, lower respiratory tract infections, gastrointestinal tract infections, and skin, and soft tissue infections. The main mechanism of action of ciprofloxacin is to prevent DNA replication by blocking the A subunit of DNA gyrase and exerting additional influence on the components of cell walls. A rise in ciprofloxacin resistance has been observed in several bacteria both gram-positive and gram-negative bacteria over time, despite the antibiotic’s exceptional properties. In the present study, gene clusters and hub genes in E. faecium and S. Typhimurium that might be involved in cellular adaptions in response to inhibitory or subinhibitory concentrations of ciprofloxacin were found using network analysis.
3.1. Gene network analysis
Earlier studies by Sinel, Cacaci [7] and Li, Dai [8] investigated the mechanism of ciprofloxacin resistance based on the gene expression patterns of the resistant species E. faecium and S. Typhimurium, respectively. Nevertheless, there might be other unknown genes that are crucial for this stress response and that are part of the gene network activated by ciprofloxacin resistance. Therefore, a network analysis was performed to find other important genes associated with inhibitory or sub-inhibitory levels of ciprofloxacin. The possible protein-protein interactions between DEGs were visualized using the STRING database. 23 genes with 104 interactions for S. Typhimurium and 28 genes with 237 interactions for E. faecium were identified using the interaction data from this database. The topological parameters (S2 Table) for the analysis of the subnetworks of upregulated genes and their interactions in the drug-resistant species of S. Typhimurium and E. faecium are accessible.
3.2. Identification of Hub genes
The list of the top of hub up-regulated genes found in the network analysis of S. Typhimurium and E. faecium can be found in S3 Table. Hub genes, which have the most gene interactions, can involved as important players in many different biological processes and molecular activities. These hub genes include rcsC, narL, phoQ, basR, phoP, rstB, envZ, and rstA in S. Typhimurium and include D920_01853, D920_01857, D920_02619, D920_01002, D920_02133, D920_02981, D920_00703, D920_02792, D920_02266, and D920_03079 in E. faecium.
3.3. GO analysis
To learn more about the biological mechanisms, molecular roles, and cellular components associated with the hub genes found in E. faecium and S. Typhimurium that are affected by inhibitory or subinhibitory concentrations of ciprofloxacin, an enrichment analysis of the functional genes was performed. Fig 1 shows a summary of the results. Numerous significant functions connected to the hub genes involved in BP, MF, and CC were revealed by the functional enrichment analysis for E. faecium and S. Typhimurium.
Gene Ontology enrichment analysis of hub genes of Enterococcus faecium (a) and Salmonella Typhimurium (b).
3.4. Clustering analysis
In protein-protein interaction networks, the IPCA algorithm helps to determine which protein complex clusters are most important. The clusters were ranked according to their connectivity values using IPCA analysis. As can be seen in Table 1, this algorithm determines the weight of each edge by finding the common neighbor of two connected nodes [16,21]. The protein complex clusters with the highest scores and significance were represented by the rank 1 clusters. In Fig 2, only the rank 1 cluster (shown within a black ring) was shown in E. faecium and S. Typhimurium. Cluster analysis can be used to find groups of genes that show coordinated expression patterns and are likely to be involved in related biological processes or pathways. This research sheds light on the functional organization and possible interactions between different elements of the cellular response to ciprofloxacin in S. Typhimurium and E. faecium by clustering the hub genes.
The gene network of Enterococcus faecium (a) and Salmonella Typhimurium (b). Hub genes are shown in green and the major hub gene is visualized within a blue ring. FC amounts of S. Typhimurium are visualized based on the thickness of the knot border. The rank 1 clusters in gene networks are visualized within a black dashed ring.
3.5. Promoter motif analysis
We subjected the 200 bp upstream flanking regions of the hub DEGs in E. faecium and S. Typhimurium to promoter motif analysis to gain a better understanding of the regulatory mechanisms underlying the differential expression of hub genes in response to ciprofloxacin stress. This analysis aimed to find conserved motifs that may be associated with the regulation of these genes. Based on the prokaryote DNA motif database for bacterial transcription factor motifs, we searched the promoter sequences of E. faecium and S. Typhimurium for known motifs using the MEME motif comparison tool (Tomtom). We used an E-value of less than 10 to identify meaningful matches. In addition, we determined the likely functions of the discovered motifs using the GO for motifs (GOMo) program. The analysis revealed the presence of several motifs with known GO annotations in the promoter regions of the hub genes (Tables 2 and 3). These motifs ranged in length from 11 to 48 base pairs. Understanding the biological and molecular roles associated with the motifs found was enabled by this analysis. The results also showed that there were similar GO results of motifs between E. faecium as a gram-positive bacteria and S. Typhimurium as a gram-negative bacteria, but these two bacteria also have some differences. Several motifs in both E. faecium and S. Typhimurium were found to be associated with biological processes such as cellular homeostasis, cellular carbohydrate catabolic process, peptidoglycan-based cell wall organization, serine family amino acid catabolic process, carbohydrate transport, cytochrome complex assembly, peptidoglycan biosynthetic process, and proton transport. However, motifs with biological processes associated with biofilm formation, biological regulation, and multi-organism processes were only discovered in E. faecium. In addition, motifs with biological processes related to aerobic respiration, alcohol catabolic process, chemical homeostasis, macromolecule catabolic process, monosaccharide catabolic process, pentose catabolic process, transcription, DNA-dependent, and pentose metabolic process were only found in S. Typhimurium. Motifs with different GO are associated with molecular functions, such as driven active transmembrane transporter activity, heme binding, intramolecular oxidoreductase activity, racemase and epimerase activity, sugar transmembrane transporter activity, active transmembrane transporter activity, oxidoreduction, interconverting aldoses and ketoses, and acting on carbohydrates and derivatives in both E. faecium and S. Typhimurium. But motifs with molecular functions associated with cation transmembrane transporter activity, oligopeptide, ATPase activity, ion transmembrane transporter activity, transporting ATPase activity, and coupled to transmembrane movement of substances were only discovered in E. faecium. In addition, motifs with molecular functions associated with electron carrier activity, glutamate decarboxylase, mismatched DNA binding, and zinc ion binding activity were only discovered in S. Typhimurium. Interestingly, a GO motif associated with cellular components was only discovered in E. faecium. This motif (motif 6) was associated with cellular components of the ATP-binding cassette (ABC) transporter complex. These results suggest that the motifs found in the hub DEG promoter regions may be important regulators of gene expression when exposed to ciprofloxacin. Functional studies and additional experimental validation are required to confirm the regulatory relationships between these motifs and their associated genes. Nonetheless, the study of promoter motifs provides important insights into putative regulatory mechanisms supporting the differential expression of hub genes upon ciprofloxacin stress. In general, the study of promoter motifs provides an additional level of understanding of the changes in gene expression associated with antibiotic resistance. The complex network of relationships that determines the cellular adaptations of S. Typhimurium and E. faecium during ciprofloxacin resistance can be partially elucidated by identifying conserved regulatory motifs.
3.6. Predicted interactions between up-regulated genes and milRNAs
Research has uncovered the role of milRNAs in Salmonella infections, particularly in how they help the bacteria express their virulence genes. These milRNAs are crucial for regulating gene expression in bacterial cells and influence functions such as virulence and environmental responses. For example, a specific microRNA-like RNA fragment called Sal-1 is produced by Salmonella in infected cells to facilitate intracellular survival. This demonstrates the sophisticated mechanisms that bacteria such as Salmonella use to adapt to and survive in their host environment [22]. The results showed that the genes with the most interactions with milRNAs in S. Typhimurium (Figs 3–5) include dpiB (990 interactions), rcsC (803 interactions) and kdpD (642 interactions), respectively, and in E. faecium (Figs 6–8) EFAU004_01228 (1345 interactions), EFAU004_02016 (853 interactions) and EFAU004_00870 (722 interactions), respectively (for more detailed information please see S1 File). The prediction results of interactions between upregulated genes and milRNAs showed that even the up-regulated genes of dpiB and kdpD as non-hub genes can be effective for cellular survival through interactions with milRNAs under antibiotic stress. Based on the string database, rcsC, dpiB and kdpD are associated with the two-component system sensory histidine kinase in two-component regulatory systems. Consequently, most milRNAs could be involved in the regulation of gene expression of two-component systems affected by inhibitory or sub-inhibitory antibiotic concentrations in negative Gram bacteria such as Salmonella. In a Gram-positive bacterium such as Enterococcus, most interactions of milRNAs were dedicated to genes associated with transferase activity (EFAU004_02016) and antibiotic binding protein (EFAU004_00870). The antibiotic binding protein and also the transferase by altering the molecules of antibiotics contribute to antibiotic resistance, thereby reducing the effectiveness of the drugs.
4. Discussion
In our study, we investigated differentially expressed genes (DEGs) under both subinhibitory and inhibitory conditions to gain a comprehensive understanding of the mechanisms of antibiotic resistance in gram-positive and gram-negative bacteria. Despite originating from different experimental environments, the analysis of these datasets is justified because antibiotic resistance often involves overlapping pathways and mechanisms that may be affected by different levels of antibiotic exposure. By comparing the DEGs of both conditions, we aim to highlight common and non-common genes and resistance pathways while capturing a broader spectrum of bacterial responses. This approach improves our ability to identify novel resistance-related genes and contributes to a more comprehensive understanding of how bacteria adapt to different antibiotic exposures, enriching the insights from our comparative analysis.
4.1. Identification of Hub genes and GO analysis
The discovery of hub genes in gram-negative bacteria such as antibiotic-resistant S. Typhimurium and gram-positive bacteria such as antibiotic-resistant E. faecium reveals a fascinating aspect of the bacterial exposure to antibiotics. The activation of metabolic pathways in S. Typhimurium and E. faecium cells under ciprofloxacin resistance may have led to an increase in the expression of these genes. A previous study by Li, Dai [8] highlighted the importance of two-component regulatory systems in S. Typhimurium under the influence of ciprofloxacin. By altering gene expression and cellular processes, these systems play a crucial role in the adaptability of bacteria to environmental changes, including antibiotic stress [23]. Genes associated with the two-component regulatory system in S. Typhimurium include the RcsB, RstA, NarX, PhoP, BasS, and PhoQ genes as well as transcriptional regulatory proteins [8,24]. Therefore, the hub genes of S. Typhimurium associated with the sensory histidine kinases in the two-component regulatory system with RcsB and RstA, the response regulator in the two-component regulatory system with NarX, the sensory kinase protein in the two-component regulatory system with PhoP, the response regulators in the two-component regulatory system with BasS and PhoQ, and the transcriptional regulatory proteins were expected to be identified in this study. Among the hub genes of S. Typhimurium, the rcsC gene (sensory histidine kinases in the two-component regulatory system with RcsB) and narL gene (response regulator in the two-component regulatory system with NarX) had the highest significance based on the Hubba node method and their scores. In contrast, the hub genes in E. faecium were associated with the HAD superfamily hydrolase; acyltransferase; riboflavin biosynthetic protein; guanylate kinase; pyridine nucleotide disulfide; oxidoreductase; transglycosylase; ATPase; putative penicillin-binding protein; putative cysteine desulfurase; thioredoxin reductase. Among the identified hub genes of E. faecium, gene D920_01853 (efc: EFAU004_02486) had the highest significance based on the method and its score. These hub genes are involved in several cellular processes, including metabolism, cell wall synthesis, and redox reactions, which may be critical for the survival and virulence of E. faecium under antibiotic stress. Similarly, the earlier study by Sinel, Cacaci [7] indicated the importance of HAD superfamily hydrolase, oxidoreductase, guanylate kinase, pyridine nucleotide disulfide ATPase, thioredoxin reductase, and nucleotide biosynthesis in E. faecium, which is affected by ciprofloxacin. The difference in hub genes between these two types of bacteria can be attributed to their different cell structures and the mechanisms they have evolved to cope with environmental stresses. Gram-negative bacteria such as S. Typhimurium have an outer membrane and a more complex cell envelope containing lipopolysaccharides, and they often rely on two-component systems to recognize and respond to external stimuli [25–27]. Gram-positive bacteria such as E. faecium do not have an outer membrane, but a thicker peptidoglycan layer. Their stress response may involve a broader range of metabolic adaptations and enzyme activities to maintain cellular function and integrity. Understanding these differences is critical for the development of targeted strategies to combat antibiotic resistance in these bacteria, as the key regulatory systems and metabolic pathways they rely on under stress are potential targets for new antimicrobial agents. The biological processes related to phosphate-containing compounds; phosphorylation; phospholipid biosynthetic process; nucleotide and nucleoside monophosphate metabolic process; and mevalonate pathway in antibiotic-resistant bacteria such as Enterococcus spp. may increase in response to inhibitory concentrations of ciprofloxacin for a variety of reasons. The increase in metabolic processes of phosphate-containing compounds may be related to the need to repair damaged DNA and membranes and to generate ATP to power efflux pumps that eject antibiotics from the cell. The increase in the organophosphate biosynthesis and phosphorylation process could be attributed to the synthesizing of the necessary intermediates and modifying the proteins to adapt to the stress induced by ciprofloxacin [28,29]. To maintain the integrity of the membrane and create new components for cell growth and division, the cell can improve its metabolic and phospholipid biosynthesis processes under the inhibitory concentration of ciprofloxacin [30]. To ensure a constant supply of nucleotides for DNA repair and replication in stress situations, the activities of nucleotide and nucleoside monophosphate metabolism are probably increased [31]. The production of isoprenoids, which are essential for the biosynthesis of cell walls and the modification of certain proteins, depends on isopentenyl diphosphate biosynthesis, which is a step in the mevalonate pathway. Overexpression of this system may be a response to the need for protein modification as a defense mechanism and for cell wall repair under ciprofloxacin stress [32,33]. The biological processes associated with the phosphorelay signal transduction system, transcriptional regulation, DNA templating, protein autophosphorylation, and growth regulation in antibiotic-resistant bacteria such as Salmonella spp. may increase in response to inhibitory concentrations of ciprofloxacin for a variety of reasons (see below). Transcriptional control of genes required for bacterial survival under environmental stresses, including antibiotic resistance, is one of the most important processes contributing to the development of antibiotic resistance. By controlling the expression of functional genes, transcriptional regulators (TRs) are essential for this process. A two-component system that enables bacteria to recognize and respond to changes in the environment, such as the presence of antibiotics, is the phosphorelay signal transduction system. This system consists of a response regulator (RR), which binds to DNA and alters gene expression when phosphorylated, and a histidine kinase, which autophosphorylates in response to an external stimulus. The RR, which typically binds to DNA and mediates a cellular response, receives information about the stimulus from a histidine kinase (HK). The response usually involves the phosphorylated response regulator binding to DNA to affect gene expression, which can lead to greater antibiotic resistance [29,34–36]. Several transcriptional regulators can be activated in response to inhibitory doses of the quinolone antibiotic ciprofloxacin. For example, drug-induced growth restriction in Pseudomonas aeruginosa activates transcriptional regulators such as the superoxide sensor regulator (SoxR) and the quorum sensing receptor (QscR) [30,37,38]. Furthermore, stimulation of efflux pump systems and other processes that support bacterial resistance to the antibiotic may occur in response to ciprofloxacin [29]. In addition, TRs bind to DNA to control the transcription of genes in a process known as DNA-templated transcription regulation. Mutations in TRs or their overexpression can lead to increased resistance to antibiotics by promoting the expression of efflux pump genes and other resistance mechanisms [39]. Protein autophosphorylation is a modification that can alter the function of proteins, including those involved in antibiotic resistance. Thus, phosphorylation of Ser/Thr/Tyr is a regulatory mechanism in bacteria that influences a range of activities, such as antibiotic resistance and pathogenicity [40]. This adaptation may involve changes in cell membrane composition, efflux pump activity, and other cellular processes that contribute to survival in the presence of the antibiotic. The increase in cellular components associated with the cellular anatomical entity in antibiotic-resistant E. faecium in response to the subinhibitory concentration of ciprofloxacin can be explained by several factors. As early mentioned, ciprofloxacin is a fluoroquinolone antibiotic that targets bacterial DNA gyrase and topoisomerase IV, which are crucial for DNA replication and repair. When bacteria are exposed to ciprofloxacin, it causes DNA damage and triggers a stress response that can lead to upregulation of genes involved in DNA repair, stress response, and efflux pump systems [28]. One of the most important defense mechanisms with which bacteria defend themselves against the effects of antibiotics is the efflux pump. Exposure to antibiotics can lead to increased expression of efflux pump genes, which increases efflux activity and lowers intracellular antibiotic concentrations [28,29,41]. In addition, bacteria can use the formation of biofilms as a defense mechanism in response to antibiotic stress. Bacteria form complex communities known as biofilms when they adhere to surfaces and become embedded in an extracellular matrix that they produce themselves. This matrix can be resistant to antibiotics, and bacteria living in biofilms can develop transient phenotypic resistance to them. Antibiotic tolerance can also be influenced by persister cells, i.e. dormant forms of normal bacterial cells found in biofilms [29]. In addition, activation of DNA damage triggers the SOS response, a global regulatory system that can trigger mutagenesis and DNA repair mechanisms, potentially leading to the emergence of antibiotic resistance [29]. For gram-negative bacteria such as S. Typhimurium, which has both innate and acquired resistance mechanisms, the plasma membrane and its essential components are of critical importance [28,29,41]. Reduced sensitivity to ciprofloxacin can also be due to changes in lipopolysaccharides (LPS) in the outer membrane of gram-negative bacteria such as Salmonella and membrane proteins [42]. In addition to these mechanisms, bacteria may also undergo genetic changes that lead to resistance, such as changes in the target enzymes DNA gyrase and topoisomerase IV, which reduce their susceptibility to inhibition by ciprofloxacin [43]. In addition, plasmids containing resistance genes, such as those expressing Qnr proteins, can increase an individual’s resistance to quinolones, such as ciprofloxacin. These plasmids can spread resistance within microbial communities by being transferred between bacteria [44]. In summary, the upregulation of genes related to the cellular components of S. Typhimurium in response to ciprofloxacin is a complex process involving multiple resistance mechanisms, including activation of efflux pumps, membrane modifications, DNA repair and genetic mutations. The drug-resistant bacteria have evolved multiple strategies to counteract the effects of antibiotics, and the upregulation of these molecular functions is part of their adaptive response to survive in the presence of the antibiotic. The biosynthesis of enzymes that render an antibiotic inactive is one of the most important resistance mechanisms. For example, the β-lactam ring, which is an essential component of many antibiotics such as cephalosporin and penicillin, can be inactivated by β-lactamase enzymes. In addition, enzymes with transferase activity such as acetyltransferase, phosphotransferase, and adenyltransferase can modify antibiotics, resulting in inactivation [45,46]. Another method to become resistant to antibiotics is to modify the target of the antibiotic without affecting cellular functions. For example, the mecA gene found in methicillin-resistant S. aureus (MRSA) encodes PBP2a, a novel penicillin-binding protein that is unaffected by previous β-lactam drugs. Due to this change in the target region, the antibiotic cannot bind as well, leading to resistance [45,47]. As a further resistance mechanism, bacteria can alter the structure of the cell surface to reduce cellular permeability, thereby limiting the amount of antibiotics that can penetrate the cell. In addition, bacteria such as MRSA can become even more resistant to drugs and the host’s immune system through the formation of aggressive biofilms [45,48]. Antibiotics can also activate the bacterial two-component system, also known as two-component signal (TCS), which regulates gene expression in response to external stimuli. Genes involved in antibiotic resistance, such as genes encoding efflux pumps or enzymes that degrade antibiotics, can be overexpressed as a result of this activation [49]. It consists of the response regulator and the sensor kinase, i.e. at least two proteins. In the case of ciprofloxacin, mutations in the genes that produce DNA gyrase or topoisomerase IV, the antibiotic’s targets, can also lead to the development of resistance. Due to these changes, the drug can no longer bind to the intended target site, allowing the bacteria to grow and multiply while the drug is there [49]. These bacteria have developed sophisticated defense mechanisms, such as enzyme production, target alteration, permeability reduction, biofilm formation, and efflux, to resist the lethal effects of antibiotics. In conclusion, these findings contribute to a better understanding of the cellular adaptations and survival mechanisms employed by negative-gram and positive-gram bacteria exposed to ciprofloxacin.
4.2. Clustering analysis
GO analysis of cluster genes under ciprofloxacin stress reveals different KEGG pathways for E. faecium and S. Typhimurium due to the different physiological adaptations and resistance mechanisms that these bacteria have evolved in response to antibiotic stress. E. faecium, a gram-positive bacterium, shows the cluster genes associated with transferase, kinase, and FAD in its KEGG pathways. Transferase and kinase are frequently involved in metabolic processes and may contribute to antibiotic resistance by altering the molecules of antibiotics or the targets they act on, thereby reducing drug efficacy [50]. However, GO analysis of cluster genes in the gram-negative bacterium S. Typhimurium includes pathways such as the two-component system and CAMP (cationic antimicrobial peptides) resistance. CAMP resistance mechanisms often include changes to the bacterial membrane that prevent cationic antimicrobial peptides from penetrating it, which is a common mode of action for some antibiotics [51–53]. To avoid the negative effects of ciprofloxacin, S. Typhimurium focuses more on membrane protection and regulatory systems than E. faecium. However, E. faecium has been shown to adapt to antimicrobial drugs by altering the chemical structure of its lipid membrane and expressing genes that respond to membrane stress [54]. In addition, certain genes and mobile genetic elements contribute to the pathogenicity and antibiotic resistance of E. faecium [55–57]. Previous research has shown that IreB acts as a negative regulator of cephalosporin resistance in E. faecalis [58]. Gram-positive and gram-negative bacteria differ in their cell wall structure, which also influences their unique KEGG signaling pathways and resistance mechanisms. Due to their unique physiological adaptations and resistance mechanisms, which are determined by structural variations and the selection pressure imposed by the antibiotic environment, E. faecium and S. Typhimurium have different KEGG signaling pathways.
4.3. Promoter motif analysis
The similarities of the GO results in the hub genes of S. Typhimurium and E. faecium suggest common pathways or functions necessary for the survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport, and cellular homeostasis. These common motifs may reflect conserved mechanisms between different bacterial species, particularly in terms of cell structure and metabolism. On the other hand, the differences in GO results may reflect the different ecological niches, pathogenic strategies, or evolutionary histories of the two bacteria. For example, motifs related to biofilm formation and biological regulation found only in E. faecium could be related to its ability to form biofilms, which is a key factor for its persistence and virulence [59–61]. The specific motifs associated with aerobic respiration and alcohol catabolic process may be related to its potential to survive in a variety of environments, including the gastrointestinal tract of humans and other animals [62,63]. The occurrence of a GO motif related to the ABC transporter complex in E. faecium suggests that this bacterium plays a special role in nutrient absorption or drug resistance [11,64,65]. The differences in molecular activities, such as cation transmembrane transporters and electron carriers, may be indicative of host interactions or environmental adaptations. Overall, GO analysis highlights both similar and different biological features between E. faecium and S. Typhimurium that provide insights into their functional capabilities. These results may be useful for further research into the pathogenic mechanisms of these bacteria and may also contribute to the development of targeted interventions or therapies.
5. Conclusion
Our study aimed to understand how the mechanisms of drug resistance of E. faecium and S. Typhimurium act at the molecular level to survive under the stress of ciprofloxacin. Depending on the structure of the cell wall, Gram-negative bacteria rely on two-component systems to cope with external signals such as antibiotic stress. In contrast, gram-positive bacteria utilise various metabolic and enzymatic adaptations to maintain their cellular integrity under antibiotic stress. Their stress response may involve a wider array of metabolic adjustments and enzyme functions to uphold cellular function and integrity. Understanding these differences is critical for the development of targeted strategies to combat antibiotic resistance in these bacteria, as the key regulatory systems and metabolic pathways they rely on under stress are potential targets for new antimicrobial agents. Both S. Typhimurium and E. faecium exhibit increased phosphorylation and metabolic processes, highlighting their common adaptive mechanisms under ciprofloxacin stress. However, the different strategies of efflux pumps in both and phospholipid biosynthesis in Enterococcus indicated their unique genetic makeup and evolutionary pathway. The shared GO terms related to peptidoglycan production, glucose transport, and cellular homeostasis suggest basic survival pathways. The divergent GO results are likely due to their different ecological niches, pathogenic approaches, and evolutionary histories. In conclusion, these results contribute to a better understanding of the cellular adaptations and survival mechanisms utilized by negative-gram and positive-gram bacteria exposed to ciprofloxacin. Analyzing these complex adaptations will allow us to develop more effective therapeutic strategies and overcome the difficulties caused by drug-resistant bacteria.
Supporting information
S1 Table. The count of retrieved up- and down-regulated genes of drug-resistant species of Salmonella Typhimurium and Enterococcus faecium.
https://doi.org/10.1371/journal.pone.0311434.s001
(DOCX)
S2 Table. The topological parameters of the subnetworks of upregulated genes and their interactions in the drug-resistant species of Salmonella Typhimurium and Enterococcus faecium.
https://doi.org/10.1371/journal.pone.0311434.s002
(DOCX)
S3 Table. The differentially expressed hub genes of the drug-resistant species of Salmonella Typhimurium and Enterococcus faecium.
https://doi.org/10.1371/journal.pone.0311434.s003
(DOCX)
S1 File. The network of predicted relationships between hub genes and milRNAs in Salmonella Typhimurium and Enterococcus faecium.
https://doi.org/10.1371/journal.pone.0311434.s004
(XLSX)
References
- 1. Guerrant RL, Van Gilder T, Steiner TS, Thielman NM, Slutsker L, Tauxe RV, et al. Practice guidelines for the management of infectious diarrhea. Clinical infectious diseases. 2001;32(3):331–51. pmid:11170940
- 2. Hooper DC, Jacoby GA. Topoisomerase inhibitors: fluoroquinolone mechanisms of action and resistance. Cold Spring Harbor perspectives in medicine. 2016;6(9). pmid:27449972
- 3. Yang Y, Niehaus KE, Walker TM, Iqbal Z, Walker AS, Wilson DJ, et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics. 2018;34(10):1666–71. pmid:29240876
- 4. De Oliveira DM, Forde BM, Kidd TJ, Harris PN, Schembri MA, Beatson SA, et al. Antimicrobial resistance in ESKAPE pathogens. Clinical microbiology reviews. 2020;33(3):10.1128/cmr. 00181–19. pmid:32404435
- 5. Mabonga E, Parkes-Ratanshi R, Riedel S, Nabweyambo S, Mbabazi O, Taylor C, et al. Complete ciprofloxacin resistance in gonococcal isolates in an urban Ugandan clinic: findings from a cross-sectional study. International journal of STD & AIDS. 2019;30(3):256–63. http://dx.doi.org/10.1177/0956462418799017.
- 6. Shariati A, Arshadi M, Khosrojerdi MA, Abedinzadeh M, Ganjalishahi M, Maleki A, et al. The resistance mechanisms of bacteria against ciprofloxacin and new approaches for enhancing the efficacy of this antibiotic. Frontiers in Public Health. 2022;10:1025633. pmid:36620240
- 7. Sinel C, Cacaci M, Meignen P, Guérin F, Davies BW, Sanguinetti M, et al. Subinhibitory concentrations of ciprofloxacin enhance antimicrobial resistance and pathogenicity of Enterococcus faecium. Antimicrobial Agents and Chemotherapy. 2017;61(5):10.1128/aac. 02763–16. http://dx.doi.org/10.1128/aac.02763-16.
- 8. Li L, Dai X, Wang Y, Yang Y, Zhao X, Wang L, et al. RNA-seq-based analysis of drug-resistant Salmonella enterica serovar Typhimurium selected in vivo and in vitro. PLoS One. 2017;12(4):e0175234. http://dx.doi.org/10.1371/journal.pone.0175234.
- 9. Campoli-Richards DM, Monk JP, Price A, Benfield P, Todd PA, Ward A. Ciprofloxacin: a review of its antibacterial activity, pharmacokinetic properties and therapeutic use. Drugs. 1988;35:373–447. http://dx.doi.org/10.2165/00003495-198835040-00003.
- 10.
Davati N. Evaluation of antimicrobial and antibiotic resistance properties of microbial community in a traditional cheese. http://dx.doi.org/10.22069/EJFPP.2022.19620.1685.
- 11. Arias CA, Murray BE. The rise of the Enterococcus: beyond vancomycin resistance. Nature Reviews Microbiology. 2012;10(4):266–78. pmid:22421879
- 12. Miryala SK, Anbarasu A, Ramaiah S. Discerning molecular interactions: a comprehensive review on biomolecular interaction databases and network analysis tools. Gene. 2018;642:84–94. pmid:29129810
- 13. Davati N, Ghorbani A, Ashrafi-Dehkordi E, Karbanowicz TP. Gene Networks Analysis of Salmonella Typhimurium Reveals New Insights on Key Genes Involved in Response to Low Water Activity. Iranian Journal of Biotechnology. 2023;21(4):71–82. pmid:38269200
- 14. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39(Database issue):D561–8. pmid:21045058
- 15. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8 Suppl 4(Suppl 4):S11. pmid:25521941
- 16. Li M, Li D, Tang Y, Wu F, Wang J. CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. International journal of molecular sciences. 2017;18(9):1880. pmid:28858211
- 17. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. pmid:10802651
- 18. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009;37(Web Server issue):W202–8. pmid:19458158
- 19. Buske FA, Boden M, Bauer DC, Bailey TL. Assigning roles to DNA regulatory motifs using comparative genomics. Bioinformatics. 2010;26(7):860–6. pmid:20147307
- 20. Davati N, Ghorbani A. Discovery of long non-coding RNAs in Aspergillus flavus response to water activity, CO2 concentration, and temperature changes. Scientific Reports. 2023;13(1):10330. pmid:37365206
- 21. Li M, Chen J-e, Wang J-x, Hu B, Chen G Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC bioinformatics. 2008;9(1):1–16. pmid:18816408
- 22. Gu H, Zhao C, Zhang T, Liang H, Wang X-M, Pan Y, et al. Salmonella produce microRNA-like RNA fragment Sal-1 in the infected cells to facilitate intracellular survival. Scientific reports. 2017;7(1):2392. pmid:28539638
- 23. Salazar ME, Laub MT. Temporal and evolutionary dynamics of two-component signaling pathways. Current opinion in microbiology. 2015;24:7–14. pmid:25589045
- 24. Olaitan AO, Morand S, Rolain J-M. Mechanisms of polymyxin resistance: acquired and intrinsic resistance in bacteria. Frontiers in microbiology. 2014;5:643. pmid:25505462
- 25. Latour X. The evanescent GacS signal. Microorganisms. 2020;8(11):1746. pmid:33172195
- 26. Fiebig A, Herrou J, Willett J, Crosson S. General stress signaling in the Alphaproteobacteria. Annual review of genetics. 2015;49:603–25. pmid:26442844
- 27. Pasqua M, Coluccia M, Eguchi Y, Okajima T, Grossi M, Prosseda G, et al. Roles of two-component signal transduction systems in shigella virulence. Biomolecules. 2022;12(9):1321. pmid:36139160
- 28. Miranda CD, Concha C, Godoy FA, Lee MR. Aquatic environments as hotspots of transferable low-level quinolone resistance and their potential contribution to high-level quinolone resistance. Antibiotics. 2022;11(11):1487. pmid:36358142
- 29. Nesse LL, Mohr Osland A, Asal B, Mo SS. Evolution of antimicrobial resistance in E. coli biofilm treated with high doses of ciprofloxacin. Frontiers in Microbiology. 2023;14:1246895. pmid:37731931
- 30. Ramsay KA, McTavish SM, Wardell SJ, Lamont IL. The effects of sub-inhibitory antibiotic concentrations on Pseudomonas aeruginosa: Reduced susceptibility due to mutations. Frontiers in microbiology. 2021;12:789550. pmid:34987489
- 31. Talukdar PK, Crockett TM, Gloss LM, Huynh S, Roberts SA, Turner KL, et al. The bile salt deoxycholate induces Campylobacter jejuni genetic point mutations that promote increased antibiotic resistance and fitness. Frontiers in Microbiology. 2022;13:1062464. pmid:36619995
- 32. Saleh NM, Moemen YS, Mohamed SH, Fathy G, Ahmed AA, Al-Ghamdi AA, et al. Experimental and Molecular Docking Studies of Cyclic Diphenyl Phosphonates as DNA Gyrase Inhibitors for Fluoroquinolone-Resistant Pathogens. Antibiotics. 2022;11(1):53. pmid:35052930
- 33. Desai J, Wang Y, Wang K, Malwal SR, Oldfield E. Isoprenoid biosynthesis inhibitors targeting bacterial cell growth. ChemMedChem. 2016;11(19):2205–15. pmid:27571880
- 34. Sexauer A, Frankenberg-Dinkel N. A bacterial signal transduction phosphorelay in the methanogenic archaeon Methanosarcina acetivorans. bioRxiv. 2021:2021.03. 04.434021. http://dx.doi.org/10.1101/2021.03.04.434021.
- 35. Chen H, Yu C, Wu H, Li G, Li C, Hong W, et al. Recent advances in histidine kinase-targeted antimicrobial agents. Frontiers in Chemistry. 2022;10:866392. pmid:35860627
- 36. Bhate MP, Molnar KS, Goulian M, DeGrado WF. Signal transduction in histidine kinases: insights from new structures. Structure. 2015;23(6):981–94. pmid:25982528
- 37. Martins D, McKay GA, English AM, Nguyen D. Sublethal paraquat confers multidrug tolerance in Pseudomonas aeruginosa by inducing superoxide dismutase activity and lowering envelope permeability. Frontiers in Microbiology. 2020;11:576708. pmid:33101252
- 38. Yeom DH, Im S-J, Kim S-K, Lee J-H. Activation of multiple transcriptional regulators by growth restriction in Pseudomonas aeruginosa. Molecules and Cells. 2014;37(6):480. pmid:24938225
- 39. Li Z, Zhang L, Song Q, Wang G, Yang W, Tang H, et al. Proteomics analysis reveals bacterial antibiotics resistance mechanism mediated by ahslyA against Enoxacin in Aeromonas hydrophila. Frontiers in Microbiology. 2021;12:699415. pmid:34168639
- 40. Bonne Køhler J, Jers C, Senissar M, Shi L, Derouiche A, Mijakovic I. Importance of protein Ser/Thr/Tyr phosphorylation for bacterial pathogenesis. FEBS letters. 2020;594(15):2339–69. pmid:32337704
- 41. Kim J, Jo A, Chukeatirote E, Ahn J. Assessment of antibiotic resistance in Klebsiella pneumoniae exposed to sequential in vitro antibiotic treatments. Annals of clinical microbiology and antimicrobials. 2016;15(1):1–7. pmid:27938381
- 42. Montis C, Joseph P, Magnani C, Marín-Menéndez A, Barbero F, Estrada AR, et al. Multifunctional nanoassemblies target bacterial lipopolysaccharides for enhanced antimicrobial DNA delivery. Colloids and Surfaces B: Biointerfaces. 2020;195:111266. pmid:32739771
- 43. Fu Y, Zhang W, Wang H, Zhao S, Chen Y, Meng F, et al. Specific patterns of gyr A mutations determine the resistance difference to ciprofloxacin and levofloxacin in Klebsiella pneumoniae and Escherichia coli. BMC infectious diseases. 2013;13(1):1–6. http://dx.doi.org/10.1186/1471-2334-13-8.
- 44. Nohejl T, Valcek A, Papousek I, Palkovicova J, Wailan AM, Pratova H, et al. Genomic analysis of qnr-harbouring IncX plasmids and their transferability within different hosts under induced stress. BMC microbiology. 2022;22(1):136. pmid:35590235
- 45. Alghamdi M, Al-Judaibi E, Al-Rashede M, Al-Judaibi A. Comparative De Novo and Pan-Genome Analysis of MDR Nosocomial Bacteria Isolated from Hospitals in Jeddah, Saudi Arabia. Microorganisms. 2023;11(10):2432. pmid:37894090
- 46. Terreni M, Taccani M, Pregnolato M. New antibiotics for multidrug-resistant bacterial strains: latest research developments and future perspectives. Molecules. 2021;26(9):2671. pmid:34063264
- 47. Formosa-Dague C, Speziale P, Foster TJ, Geoghegan JA, Dufrêne YF. Zinc-dependent mechanical properties of Staphylococcus aureus biofilm-forming surface protein SasG. Proceedings of the National Academy of Sciences. 2016;113(2):410–5. pmid:26715750
- 48. Bukhari SAR, Irfan M, Ahmad I, Chen L. Comparative genomics and pan-genome driven prediction of a reduced genome of Akkermansia muciniphila. Microorganisms. 2022;10(7):1350. pmid:35889069
- 49. Worthington RJ, Melander C. Combination approaches to combat multidrug-resistant bacteria. Trends in biotechnology. 2013;31(3):177–84. pmid:23333434
- 50. Khodadadi E, Zeinalzadeh E, Taghizadeh S, Mehramouz B, Kamounah FS, Khodadadi E, et al. Proteomic applications in antimicrobial resistance and clinical microbiology studies. Infection and Drug Resistance. 2020:1785–806. pmid:32606829
- 51. Gupta A, Malwe AS, Srivastava GN, Thoudam P, Hibare K, Sharma VK. MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets. BMC bioinformatics. 2022;23(1):1–17. http://dx.doi.org/10.1186/s12859-022-05061-7.
- 52. Wimley WC. Describing the mechanism of antimicrobial peptide action with the interfacial activity model. ACS chemical biology. 2010;5(10):905–17. pmid:20698568
- 53. Ye M-Q, Chen G-J, Du Z-J. Effects of antibiotics on the bacterial community, metabolic functions and antibiotic resistance genes in mariculture sediments during enrichment culturing. Journal of Marine Science and Engineering. 2020;8(8):604. http://dx.doi.org/10.3390/jmse8080604.
- 54. Chilambi GS, Hinks J, Matysik A, Zhu X, Choo PY, Liu X, et al. Enterococcus faecalis adapts to antimicrobial conjugated oligoelectrolytes by lipid rearrangement and differential expression of membrane stress response genes. Frontiers in microbiology. 2020;11:155. pmid:32117172
- 55. Guzman Prieto AM, van Schaik W, Rogers MR, Coque TM, Baquero F, Corander J, et al. Global emergence and dissemination of enterococci as nosocomial pathogens: attack of the clones? Frontiers in microbiology. 2016;7:788. pmid:27303380
- 56. Leavis HL, Willems RJ, Top J, Spalburg E, Mascini EM, Fluit AC, et al. Epidemic and nonepidemic multidrug-resistant Enterococcus faecium. Emerging infectious diseases. 2003;9(9):1108. pmid:14519248
- 57. Hegstad K, Mikalsen T, Coque T, Werner G, Sundsfjord A. Mobile genetic elements and their contribution to the emergence of antimicrobial resistant Enterococcus faecalis and Enterococcus faecium. Clinical microbiology and infection. 2010;16(6):541–54. pmid:20569265
- 58. Hall CL, Lytle BL, Jensen D, Hoff JS, Peterson FC, Volkman BF, et al. Structure and dimerization of IreB, a negative regulator of cephalosporin resistance in Enterococcus faecalis. Journal of Molecular Biology. 2017;429(15):2324–36. pmid:28551334
- 59. Ghaziasgar F, Poursina F, Hassanzadeh A. Virulence factors, biofilm formation and antibiotic resistance pattern in Enterococcus faecalis and Enterococcus faecium isolated from clinical and commensal human samples in Isfahan, Iran. Annali Di Igiene Medicina Preventiva E Di Comunita. 2019;31(2):156–64. pmid:30714613
- 60. Sieńko A, Wieczorek P, Majewski P, Ojdana D, Wieczorek A, Olszańska D, et al. Comparison of antibiotic resistance and virulence between biofilm-producing and non-producing clinical isolates of Enterococcus faecium. Acta Biochimica Polonica. 2015;62(4). pmid:26637375
- 61. Gök ŞM, Kara F, Arslan U, Fındık D. Investigation of antibiotic resistance and virulence factors of Enterococcus faecium and Enterococcus faecalis strains isolated from clinical samples. Mikrobiyoloji bulteni. 2020;54(1):26–39. http://dx.doi.org/10.5578/mb.68810.
- 62. Fan H-H, Fang S-B, Chang Y-C, Huang S-T, Huang C-H, Chang P-R, et al. Effects of colonization-associated gene yqiC on global transcriptome, cellular respiration, and oxidative stress in Salmonella Typhimurium. Journal of Biomedical Science. 2022;29(1):102. pmid:36457101
- 63. Barrow PA, Berchieri A, Freitas Neto OCd, Lovell M. The contribution of aerobic and anaerobic respiration to intestinal colonization and virulence for Salmonella typhimurium in the chicken. Avian Pathology. 2015;44(5):401–7. pmid:26443064
- 64. Burnie J, Carter T, Rigg G, Hodgetts S, Donohoe M, Matthews R. Identification of ABC transporters in vancomycin-resistant Enterococcus faecium as potential targets for antibody therapy. FEMS Immunology & Medical Microbiology. 2002;33(3):179–89. pmid:12110480
- 65. Wei L, Li M, Xia F, Wang J, Ran S, Huang Z, et al. Phosphate transport system mediates the resistance of Enterococcus faecalis to multidrug. Microbiological Research. 2021;249:126772. pmid:33930841