Deciphering Teixobactin Resistance Mechanisms in Enterococcus faecalis Through Integrated RNA-seq and Hub Genes Identification

Deciphering Teixobactin Resistance Mechanisms in Enterococcus faecalis Through Integrated RNA-seq and Hub Genes Identification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deciphering Teixobactin Resistance Mechanisms in Enterococcus faecalis Through Integrated RNA-seq and Hub Genes Identification Deepika J, Aishwarya C Shetty, T DhanushKumar, Karthick Vasudevan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4316554/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antimicrobial resistance (AMR) poses a severe and pressing global health crisis, necessitating urgent innovative approaches to combat drug-resistant bacteria. This study investigates the genetic underpinnings of resistance in Enterococcus faecalis., a Gram-positive bacterium, in response to the novel antibiotic Teixobactin. Leveraging whole transcriptome RNA-seq analysis and sophisticated bioinformatics tools, we have identified ten central hub genes: guaA, guaB, lepA, der, secA, ftsH, obg, nusG, dnaA, and ffh. These genes display significant upregulation and robust interactions within the bacterial genome. Our comprehensive analysis uncovers the involvement of these genes in diverse critical cellular functions associated with antibiotic resistance. These functions encompass purine metabolism, protein export, stress response, transcriptional regulation, and ribosomal activities. These findings provide crucial insights into the intricate molecular mechanisms underpinning Enterococcus faecalis resistance to Teixobactin. Furthermore, potential targets were identified for the development of advanced antibiotics, aligning with the ongoing global efforts against Antimicrobial Resistance (AMR), these identified hub genes offer promising avenues for novel drug discovery, bolstering the ongoing crusade against drug-resistant bacterial infections. Teixobactin Resistance Enterococcus faecalis Integrated RNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Enterococcus faecalis [ E. faecalis ] is a Gram-positive bacterium commonly found in the gut microbiota of both humans and animals, making it a potential pathogen in various systemic infections [ 1 , 2 ]. It is frequently transmitted through the fecal-oral route and can exist in different environmental conditions, from being a commensal microbe to causing severe hospital-acquired infections [ 3 , 4 ]. Enterococci are known to be resilient, with the ability to withstand oxidative stress, high pH levels, and elevated salt concentrations [ 5 ]. They are responsible for a range of infections, including urinary tract infections, bacteremia, endocarditis, and infections associated with medical devices, often posing challenges due to their resistance to multiple antibiotics [ 6 , 7 ]. Antibiotic resistance is a pressing global health concern, with microorganisms evolving resistance to antibiotics due to factors such as overuse, misuse, and improper treatment practices [ 8 – 11 ]. Teixobactin, a novel antibiotic, has emerged as a promising solution to combat antibiotic resistance, as it exhibits no apparent bacterial resistance [ 1 ]. Comprising unique amino acids, Teixobactin has demonstrated remarkable efficacy against clinically significant Gram-positive bacteria, including vancomycin-resistant Enterococci [VRE] [ 12 , 13 ]. Its mechanism of action involves disrupting bacterial cell wall formation, potentially by binding to specific molecules like lipid II and lipid III, leading to autolytic cell death [ 14 , 15 ]. In the context of addressing antimicrobial resistance [AMR], the identification of resistant genes is crucial [ 10 ]. High-throughput RNA sequencing [RNA-seq] offers a powerful tool to analyze gene expression and uncover pathways associated with AMR [ 16 , 17 ]. In this study, we aim to identify differentially expressed genes [DEGs] in E. faecalis exposed to Teixobactin using two distinct RNA-seq pipelines, explore the pathways associated with these DEGs, and pinpoint prominent AMR genes that could serve as potential targets for drug development. The pipelines employed include the conventional RNA-seq pipeline [bowtie2/Rsubread/DESeq2] and the cufflink pipeline [tophat/cufflink/cuffdiff/cummerbund], offering comprehensive insights into the genetic mechanisms behind Teixobactin resistance [ 18 – 19 ]. Our study endeavors to contribute to the ongoing efforts to combat AMR by uncovering the genetic basis of resistance and identifying promising targets for future antibiotics. Additionally, understanding the transcriptomic response of E. faecalis to Teixobactin can provide valuable insights into the adaptative strategies employed by this bacterium in the face of antibiotic pressure. The comprehensive analysis of gene expression profiles will shed light on the molecular mechanisms that govern the development of resistance and the potential evolution of new resistance strategies. Such insights are essential not only for the development of effective therapeutic interventions but also for the surveillance and management of antibiotic resistance in clinical and environmental settings. This study bridges the gap between genomics and clinical outcomes, offering a holistic approach to address the urgent challenge of antibiotic resistance in Enterococcus faecalis . 2. Methodology 2.1 Dataset Selection and preprocessing The dataset used in this study was obtained from the SRA database [ 20 ] [ https://www.ncbi.nlm.nih.gov/sra ], with run accession ID starting from ERR2287711 to ERR2287716 with project id PRJEB24904. It was retrieved in Fastq format from the ENA database [ 21 ] [ https://www.ebi.ac.uk/ena/browser/view/PRJEB24904 ]. This dataset comprised six sets of samples, with three subjected to antibiotic treatment and three serving as untreated controls. To ensure data quality, an initial assessment of the quality of each FASTQ file was conducted using the FastQC tool (Version-0.12.1) [ 22 ] [ https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ]. Subsequently, the generated FastQC reports were consolidated using the MultiQC tool (version-1.20) [ 23 ] [ https://multiqc.info/ ]. 2.2. Bioinformatics Pipeline 1 [Alignment and DE Analysis]: In the initial bioinformatics pipeline, our primary focus was on aligning the raw sequencing reads to the reference genome, specifically E. faecalis JH2-2 [GenBank accession number NZ_KI518257.1]. This alignment task was carried out utilizing Bowtie2 (version-2.5.1), a widely-used alignment tool known for its efficiency and accuracy [ 24 ]. Impressively, this alignment process achieved a high mapping efficiency, surpassing 80% for each individual read. Subsequently, to obtain count data for individual transcripts, we employed the R Subread package (version-2.16.0) [ 25 ], an essential Bioconductor tool. All of these operations were executed within the R programming environment and R studio(version-4.3.0). For the crucial task of identifying differentially expressed genes [DEGs], we harnessed the powerful DESeq2 package (version-1.42.0) [ 26 ] from R. To visualize the results pertaining to DEGs, we made effective use of the ggplot2 package (version-3.4.1) [ 27 ], a vital component of R's rich visualization capabilities. 2.3. Bioinformatics Pipeline 2 [Alignment and DE Analysis]: The second phase of our bioinformatics pipeline initiated with the alignment of the raw sequencing reads to the reference genome, E. faecalis JH2-2. This alignment process was carried out using Tophat (version-2.0.10), which relies on Bowtie as its underlying alignment engine. Following the alignment step, we transitioned to the Cufflink pipeline (version-2.2.1) for an in-depth analysis of DEGs. This comprehensive pipeline encompasses various stages, including transcript assembly, abundance estimation, and the assessment of differential gene expression in RNA-Seq datasets. Cuffdiff, a pivotal component of the Cufflinks package, played a central role in our analysis. It not only facilitated the quantification of expression levels but also assessed the statistical significance of observed changes in gene expression. Furthermore, Cuffdiff enabled the identification of genes that exhibited differential regulation at both the transcriptional and post-transcriptional levels [ 16 , 18 ]. 2.4. Gene Ontology and Pathway Enrichment Analysis: To elucidate the functional attributes of genes, gene ontology annotations were employed. ShinyGO [ http://bioinformatics.sdstate.edu/go74/ ] (version-0.741) [ 28 ], a user-friendly web application, was chosen for gene ontology analysis. It provided intuitive visualization of enrichment results, elucidating the biological function, cellular component, and molecular function of the DEGs. 2.5. Protein-Protein Interaction Network [STRING] and visualization To explore protein-protein interactions, the STRING database (version-12.0) [ https://string-db.org/ ] [ 29 ] was harnessed. This comprehensive database encompasses direct and indirect protein interactions, each assigned a confidence score to denote interaction strength. Confidence scores were categorized as highest [above 0.90], high [0.7 to 0.89], medium [0.4 to 0.69], and low [0.15 to 0.39]. For the visualization of molecular interaction networks, Cytoscape (version 3.10.1) [ 30 ], an open-source bioinformatics software platform, was employed. It offers a multitude of tools for network analysis, including gene clustering and enrichment analysis. The CytoHubba plugin (version-0.1) within Cytoscape facilitated the identification of significant hub genes within these biological networks [ 31 ]. 3. Results 3.1. Bioinformatics Pipeline 1 The study focused on the transcript-level data to identify differentially expressed genes [DEGs]. To accomplish this, we employed the DESeq2 package integrated into the R programming environment. Among the 474 transcripts that exhibited non-zero counts, our investigation unveiled 375 genes that demonstrated statistically significant differential expression. This set comprised 178 genes with upregulated expression and 197 genes displaying downregulation. To visually represent these findings comprehensively, we harnessed various graphical tools, including dispersion plots, PCA plots, MA plots, and volcano plots, all generated using the R ggplot2 package [ 32 , 33 ]. In order to establish rigorous criteria for statistical significance, we applied a screening threshold defined as an adjusted p-value below 0.05. Furthermore, we presented the top 30 most significant genes in the form of a heatmap, as depicted in Fig. 1 . 3.2. Bioinformatics Pipeline 2 In the second segment of the bioinformatics pipeline, the merged files generated by cuffmerge were utilized, and cuffdiff was applied to explore the intricate landscape of differential gene expression and regulation. The outcomes were meticulously documented in a series of tab-delimited text files, serving as the foundation for subsequent analyses. For comparative assessments between different treatments, the cummeRbund package within the R environment was employed. This comprehensive analysis revealed the presence of 2,454 genes, 5,195 isoforms, 2,819 transcription start sites [TSS], 2,741 coding sequences [CDS], 2,454 promoters, 2,819 splicing events, and 2,375 relative CDS [coding sequences], among other key attributes. To provide a comprehensive visualization of the expression levels in each treatment, density plots were generated using the csDensity function. Additionally, csVolcano was utilized to create volcano plots, which offer a graphical representation of differentially expressed genes between treatments. Furthermore, the csScatter function was applied to generate scatter plots, facilitating the comparison of gene expression patterns across two distinct samples [ 34 ] [Figure 2 ]. 3.3. Enrichment analysis Gene ontology is a fundamental tool in bioinformatics, utilized to annotate genes and their products. It categorizes genes into three major ontological classes: Biological Process [BP], Molecular Function [MF], and Cellular Component [CC]. To discern the biological functions of differentially expressed genes [DEGs] derived from both analysis pipelines, we focused on the common genes identified by both methods. In total, 142 upregulated and 150 downregulated common genes were uncovered [Figure 3 ]. These common genes underwent annotation using ShinyGO to elucidate their functions. Among the upregulated DEGs in BP, we observed involvement in processes such as cellular processes, cellular metabolic processes, organic substance metabolic processes, metabolic processes, and primary metabolic processes, among others. In terms of CC, these DEGs were predominantly associated with intracellular locations, cytoplasmic regions, and various cellular anatomical entities. For MF, the enriched terms encompassed binding, catalytic activity, ion binding, small molecule binding, and nucleoside phosphate binding, to name a few as depicted in Fig. 4 Conversely, the downregulated genes exhibited functional enrichment in biological processes like protein metabolic processes, cellular protein metabolic processes, macromolecule biosynthetic processes, cellular macromolecule biosynthetic processes, amide biosynthetic processes, and translation in BP. Their cellular localization, as indicated by CC, included intracellular regions, cellular anatomical entities, ribosomes, organelles, non-membrane-bounded organelles, and intracellular organelles. In terms of MF, these genes were associated with functions such as binding, heterocyclic compound binding, organic cyclic compound binding, nucleic acid binding, and RNA binding [Figure 5 ]. To further gain insights into the functional pathways associated with the differentially expressed genes [DEGs], we conducted KEGG pathway enrichment analysis using a STRING database. This analysis revealed that the upregulated genes were prominently involved in several vital pathways, including metabolic pathways, biosynthesis of secondary metabolites, fatty acid metabolism, fatty acid biosynthesis, purine metabolism, and biosynthesis of amino acids, among others. These pathways play critical roles in various cellular processes and metabolic activities [Figure 6 ]. 3.4. Protein-protein interaction network The analysis of differentially expressed genes [DEGs] extended to exploring protein-protein interactions. All common DEGs from both pipelines were uploaded to the STRING database and visualized in Cytoscape. This interaction network, as revealed by STRING, comprised 212 nodes, 3,038 edges, an average node degree of 28.7, an average local clustering coefficient of 0.493, and a highly significant protein-protein interaction [PPI] enrichment p-value of < 1.0e-16 . Further investigation involved identifying the top 100 hub genes using the CytoHubba plugin, which were subsequently subjected to cluster analysis. The MCODE plugin in Cytoscape facilitated this analysis, resulting in the identification of five distinct clusters denoted as C1, C2, C3, C4, and C5 [Figure 7 ]. This clustering allowed us to gain insights into highly interconnected regions within the gene interaction network. Out of the 100 genes in the network, 74 were identified as part of these clusters. Among them, cluster C1 exhibited the highest degree of interconnectedness, consisting of 54 nodes and 1,328 edges with an MCODE score of 50.1. This was followed by C2 with 6 nodes and 12 edges, scoring 4.8, C3 with 6 nodes and 10 edges, scoring 4.0, C4 with 5 nodes and 7 edges, scoring 3.5, and C5 with 3 nodes and 3 edges, scoring 3.0. For a more focused examination, the upregulated genes identified by DESeq were utilized to pinpoint highly interactive genes, or hub genes, using the CytoHubba plugin. The top 10 hub genes identified were guaA, guaB, lepA, der, secA, ftsH, obg, nusG, dnaA, and ffh. These genes, directly or indirectly contributing to bacterial resistance, have the potential to serve as valuable targets for various drugs due to their involvement in antimicrobial resistance [AMR] mechanisms [Figure 8 ]. 4. Discussion Tolerance to antimicrobials plays a pivotal role in the development of antimicrobial resistance [AMR] within bacteria, often arising from the inappropriate use of antibiotics in clinical and agricultural settings [ 35 ]. This resistance can propagate through various channels, such as direct exposure, the food chain, and environmental transmission, leading to the emergence of antimicrobial resistance genes [ARGs] in diverse sources, including humans, animals, food, plants, and the environment [ 36 ]. Teixobactin, a novel cyclic depsipeptide containing the rare amino acid enduracididine, has emerged as a groundbreaking class of antibiotics designed to target specific cellular processes involved in cell wall production [ 14 ]. Despite the global surge in antibiotic resistance among Gram-positive bacteria, teixobactin has demonstrated effectiveness against them [ 37 ]. However, some bacteria, like Enterococcus faecalis, have innate tolerance to high doses of teixobactin, prompting investigations into the molecular mechanisms underlying this tolerance and its potential contribution to teixobactin resistance [ 12 ]. In our study, we conducted comprehensive RNA-seq analysis of the entire transcriptome using three samples subjected to antibiotic treatment and three untreated controls. We employed two distinct bioinformatics pipelines for analysis, using two pipelines improves result reliability. When two pipelines discover the same AMR genes, it increases the likelihood of their occurrence. The conventional RNA-seq pipeline involving bowtie2, Rsubread, and DESeq2, as well as the cufflink pipeline employing tophat, cufflink, cuffdiff, and cummerbund. Subsequently, we pinpointed common differentially expressed genes [DEGs] identified by both pipelines for further scrutiny [ 38 – 41 ]. Our analysis encompassed gene ontology investigations of DEGs pertaining to biological processes, cellular components, and molecular functions, facilitated by ShinyGO. Furthermore, we delved into pathway analysis via the KEGG pathway analysis tool using the STRING database. Protein-protein interactions were explored using the STRING database and visualized in Cytoscape. Among the identified hub genes, guaA and guaB, which encode GMP synthetase and IMP dehydrogenase [IMPDH], respectively, emerged as pivotal players in de novo purine synthesis, a process essential for bacterial viability. The guanine nucleotide biosynthesis pathway, in which these genes play a role, holds significance in various cellular processes, rendering them promising targets for antibiotic development [ 42 , 43 ] The potential therapeutic benefits of targeting guaB or guaA are evident in their potential to combat persistent bacterial infections [ 44 ]. SecA, another upregulated gene within our study, holds a crucial role in the post-translational translocation of secretory and outer membrane proteins across the SecYEG translocation machinery. It proves indispensable for bacterial adhesion to host cells and presents itself as a viable target for antibacterial interventions [ 45 – 49 ]. FtsH, an ATP-dependent protease, surfaces as a key component of bacterial virulence and resilience in the face of diverse environmental stressors. Its upregulation suggests a role in stress response and adaptation, thus positioning it as a candidate for antibiotic targeting [ 50 – 53 ]. LepA, involved in protein synthesis, contributes to ribosome back-translocation and is implicated in ribosome biosynthesis. Disrupting LepA's function could impede protein synthesis and inhibit bacterial growth [ 54 – 56 ]. Obg, characterized as a monomeric guanine nucleotide binding protein, is associated with antibiotic tolerance and the formation of persister cells. Inhibition of Obg could enhance bacterial susceptibility to antibiotics [ 57 – 59 ]. NusG, functioning as an elongation factor, plays a pivotal role in gene expression regulation. Targeting NusG might disrupt transcription processes, thereby serving as a valuable target for novel antibiotics [ 60 – 63 ]. In addition to these highlighted genes, der, dnaA, and ffh also exhibited significant interactions and may warrant consideration as potential targets for antibiotic development. Ffh, for instance, plays a pivotal role in protein translocation, while DnaA assumes a central role in initiating chromosomal replication [ 64 – 65 ]. Conclusion This study provides valuable insights into the intricate network of highly interconnected genes within Enterococcus faecalis, shedding light on their potential roles in driving antimicrobial resistance [AMR] and tolerance mechanisms. Notably, genes such as guaA, guaB, secA, ftsH, lepA, obg, NusG, and others have emerged as compelling candidates for targeted exploration in the pursuit of innovative antibiotics aimed at mitigating the pressing issue of AMR. The current investigation underscores the presence of resistance genes in E. faecalis . Through rigorous gene ontology and pathway analyses of the upregulated genes, we unveiled their active involvement in pivotal cellular processes encompassing cellular metabolism, binding, catalytic activities, and more. These carefully selected genes, known to influence resistance, were meticulously examined for their significant interactions. The results showcased a highly enriched interaction network, shedding light on the critical drug resistance genes within E. faecalis, with a particular focus on their response to texiobactin. The comprehensive examination of the functions of these pivotal genes positions them as promising targets warranting further in-depth exploration in the quest to combat AMR effectively. Declarations Authorship contribution statement Deepika : Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization. Aishwarya C Shetty : Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization. DhanushKumar T : Writing – review & editing, Formal analysis, Conceptualization Karthick Vasudevan : review & editing, Writing – original draft, Supervision, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization, Project administration Conflict of interest The authors declare no conflict of interest. Data availability Data will be made available on request. Acknowledgments The authors express deep gratitude to the management of REVA University, for providing necessary facilities, assistance, and constant encouragement to carry out this work. References Jarkhi, A., Lee, A. H. 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S., Chen, W., Jin, J., Tai, P. C., & Wang, B. [2015]. SecA: a potential antimicrobial target. Future medicinal chemistry, 7[8], 989-1007. De Waelheyns, E., Segers, K., Sardis, M. F., Anné, J., Nicolaes, G. A., & Economou, A. [2015]. Identification of small-molecule inhibitors against SecA by structure-based virtual ligand screening. The Journal of antibiotics, 68[11], 666-673. Wang, Y., Cao, W., Merritt, J., Xie, Z., & Liu, H. [2021]. Characterization of FtsH essentiality in Streptococcus mutans via genetic suppression. Frontiers in Genetics, 12, 659220. Yeo, W. S., Jeong, B., Ullah, N., Shah, M. A., Ali, A., Kim, K. K., & Bae, T. [2021]. FtsH sensitizes methicillin-resistant Staphylococcus aureus to β-lactam antibiotics by degrading YpfP, a lipoteichoic acid synthesis enzyme. Antibiotics, 10[10], 1198. Fiocco, D., Collins, M., Muscariello, L., Hols, P., Kleerebezem, M., Msadek, T., & Spano, G. [2009]. The Lactobacillus plantarum ftsH gene is a novel member of the CtsR stress response regulon. Journal of bacteriology, 191[5], 1688-1694. Bourdineaud, J. P., Nehmé, B., Tesse, S., & Lonvaud-Funel, A. [2003]. The ftsH gene of the wine bacterium Oenococcus oeni is involved in protection against environmental stress. Applied and Environmental Microbiology, 69[5], 2512-2520. Balakrishnan, R., Oman, K., Shoji, S., Bundschuh, R., & Fredrick, K. [2014]. The conserved GTPase LepA contributes mainly to translation initiation in Escherichia coli. Nucleic acids research, 42[21], 13370-13383. Shoji, S., Janssen, B. D., Hayes, C. S., & Fredrick, K. [2010]. Translation factor LepA contributes to tellurite resistance in Escherichia coli but plays no apparent role in the fidelity of protein synthesis. Biochimie, 92[2], 157-163. Gibbs, M. R., Moon, K. M., Chen, M., Balakrishnan, R., Foster, L. J., & Fredrick, K. [2017]. Conserved GTPase LepA [Elongation Factor 4] functions in biogenesis of the 30S subunit of the 70S ribosome. Proceedings of the National Academy of Sciences, 114[5], 980-985. Kint, C., Verstraeten, N., Hofkens, J., Fauvart, M., & Michiels, J. [2014]. Bacterial Obg proteins: GTPases at the nexus of protein and DNA synthesis. Critical reviews in microbiology, 40[3], 207-224. Chakraborty, A., Halder, S., Kishore, P., Saha, D., Saha, S., Sikder, K., & Basu, A. [2022]. The structure–function analysis of Obg‐like GTPase proteins along the evolutionary tree from bacteria to humans. Genes to Cells, 27[7], 469-481. Buglino, J., Shen, V., Hakimian, P., & Lima, C. D. [2002]. Structural and biochemical analysis of the Obg GTP binding protein. Structure, 10[11], 1581-1592. Strauß, M., Schweimer, K., Burmann, B. M., Richter, A., Güttler, S., Wöhrl, B. M., & Rösch, P. [2016]. The two domains of Mycobacterium tuberculosis NusG protein are dynamically independent. Journal of Biomolecular Structure and Dynamics, 34[2], 352-361. Bailey, E. J., Gottesman, M. E., & Gonzalez Jr, R. L. [2022]. NusG-mediated coupling of transcription and translation enhances gene expression by suppressing RNA polymerase backtracking. Journal of molecular biology, 434[2], 167330. Wang, B., & Artsimovitch, I. [2021]. NusG, an ancient yet rapidly evolving transcription factor. Frontiers in Microbiology, 11, 619618. Mandell, Z. F., Oshiro, R. T., Yakhnin, A. V., Vishwakarma, R., Kashlev, M., Kearns, D. B., & Babitzke, P. [2021]. NusG is an intrinsic transcription termination factor that stimulates motility and coordinates gene expression with NusA. Elife, 10, e61880. Park, S. K., Jiang, F., Dalbey, R. E., & Phillips, G. J. [2002]. Functional analysis of the signal recognition particle in Escherichia coli by characterization of a temperature-sensitive ffh mutant. Journal of bacteriology, 184[10], 2642-2653. Menikpurage, I. P., Woo, K., & Mera, P. E. [2021]. Transcriptional activity of the bacterial replication initiator DnaA. Frontiers in Microbiology, 12, 662317. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4316554","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297686806,"identity":"a10c72d4-6d6b-4c3b-a41f-8f8a6ec7fe28","order_by":0,"name":"Deepika J","email":"","orcid":"","institution":"REVA University","correspondingAuthor":false,"prefix":"","firstName":"Deepika","middleName":"","lastName":"J","suffix":""},{"id":297686808,"identity":"26df3a53-3367-405d-83ae-23674c12769a","order_by":1,"name":"Aishwarya C Shetty","email":"","orcid":"","institution":"REVA University","correspondingAuthor":false,"prefix":"","firstName":"Aishwarya","middleName":"C","lastName":"Shetty","suffix":""},{"id":297686810,"identity":"7ed32f82-77b5-47fb-9f33-f6cf89b82862","order_by":2,"name":"T DhanushKumar","email":"","orcid":"","institution":"REVA University","correspondingAuthor":false,"prefix":"","firstName":"T","middleName":"","lastName":"DhanushKumar","suffix":""},{"id":297686811,"identity":"4f7fb29d-2b23-407d-bb62-dbd4e203454e","order_by":3,"name":"Karthick Vasudevan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACCSBmZjAAQmbGxsd/KkBc5gZCWhibwVrYmQ8b8JwBaWEkRgsDUAs/W5oEbxtIjIAWyfazzx8XFNgZMzDzGBtIzquN5m8HavlRsQ2nFmmedMPmGQbJZkAthg8Mtx3PnXGYsYGx58xtnFrkGNIYm3kMDtiAbUncdiy3AaiFmbENjxb+Z3AtZhIH5xzLnU9Ii7QExBagw9jSJBsbanI3ENIiOeMZ42weg2RjNmbmw8YMxw7kbgRqOYjPLxLn0xg+8/yxM+znP9j4mKGmLnfe+cMHH/yowK0FDtgg1GEweYCwegSoI0XxKBgFo2AUjBAAALF7UYSGtxagAAAAAElFTkSuQmCC","orcid":"","institution":"REVA University","correspondingAuthor":true,"prefix":"","firstName":"Karthick","middleName":"","lastName":"Vasudevan","suffix":""}],"badges":[],"createdAt":"2024-04-24 08:19:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4316554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4316554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55709919,"identity":"30e62e80-089e-4ceb-b250-851d0f6f7442","added_by":"auto","created_at":"2024-05-02 06:02:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":223147,"visible":true,"origin":"","legend":"\u003cp\u003eA) PCA plot of 500 most variable genes B) Volcano plot showing DEG’s in \u003cem\u003eE.faecalis\u003c/em\u003e. Each dot represents a gene, in which red, grey and green represent down-regulated, non-significant and up-regulated genes respectively. C) dispersion plot: plots expected dispersion value for genes of a given expression strength D) MA plot displaying the log fold-change compared with mean expression using a DESeq2 package E) Heatmap showing the distribution of DEGs between different groups. [DEGs = differentially expressed genes, PCA = principal component analysis, MA = M (log ratio) and A (\u003ca href=\"https://en.wikipedia.org/wiki/Arithmetic_mean\" title=\"Arithmetic mean\"\u003emean average\u003c/a\u003e)]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/58cde4d748d1895fede6ae86.png"},{"id":55710310,"identity":"3b4cbd9a-0a99-46e8-ab47-d13e50d55cce","added_by":"auto","created_at":"2024-05-02 06:10:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268828,"visible":true,"origin":"","legend":"\u003cp\u003eA) Density plot: plots of the expression level distribution for all genes in experimental conditions C1 and C2. B) Scatter plot: highlights general similarities and specific outliers between genes of conditions C1 and C2.C) volcano plot: indicates the presence of differentially expressed genes between the two condition D) MA plot between two condition E) Dispersion plot of two condition. [ FPKM = fragments per kilobase of transcript per million fragments mapped, C1 = Normal, C2 = antibiotic treated]\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/6f49a52f007cf705d0f43b32.png"},{"id":55709917,"identity":"11a8532a-1d45-4b5a-b7ff-68206f06bb07","added_by":"auto","created_at":"2024-05-02 06:02:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111838,"visible":true,"origin":"","legend":"\u003cp\u003eRepresents common upregulated and downregulated genes between two pipelines\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/347f68189a980bc80827a841.png"},{"id":55709920,"identity":"c5aefbff-c35c-45f4-92ce-38c37f1ac9ca","added_by":"auto","created_at":"2024-05-02 06:02:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117596,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of DEGs. Enrichment of up-regulated DEGs. Showing A) biological process, B) cellular component and C) molecular function\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/86d4db4ef8def3065d60f5a2.png"},{"id":55710311,"identity":"fa83ec0b-5006-40bc-a653-54ee0375d1bd","added_by":"auto","created_at":"2024-05-02 06:10:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98340,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of DEGs. Enrichment of down-regulated DEGs of A) biological process, B) cellular component and C) molecular function. [DEGs = differentially expressed genes, GO = gene ontology]\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/74c07954710a5daa9b5f0cb4.png"},{"id":55709922,"identity":"e269f46f-264b-4e0c-95f8-a696979d6d36","added_by":"auto","created_at":"2024-05-02 06:02:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115549,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of DEGs. (A) Enriched pathway of up-regulated DEGs. (B) Enriched pathway of down-regulated DEGs. KEGG = Kyoto encyclopedia of genes and genomes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/bc837ef68e4664d35af9ee51.png"},{"id":55709923,"identity":"7c285dd9-e1e7-4e69-9663-b9eb049a41b8","added_by":"auto","created_at":"2024-05-02 06:02:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1013221,"visible":true,"origin":"","legend":"\u003cp\u003eClustering analysis of antibiotic treated \u003cem\u003eE.faecalis\u003c/em\u003egene interaction network using MCODE. The genes were grouped into five clusters, i.e., C1, C2, C3, C4 and C5. Cluster C1 showed the highest level of clustering, followed by other clusters. The unclustered genes are highlighted in pink color.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/adb43dae5fb1a8c9906e5c31.png"},{"id":55709924,"identity":"7a7ba0ad-f736-43de-a1e5-98dc84ce43a7","added_by":"auto","created_at":"2024-05-02 06:02:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":83460,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 upregulated hub genes and their interaction.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/c99c461547aa5b0cd3236e47.png"},{"id":56112545,"identity":"9aeed24a-43e4-47b6-907c-31d08f1872dc","added_by":"auto","created_at":"2024-05-08 16:57:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2452547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4316554/v1/14a62dc5-30bb-407b-9075-02ca2f1a82c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering Teixobactin Resistance Mechanisms in Enterococcus faecalis Through Integrated RNA-seq and Hub Genes Identification","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eEnterococcus faecalis\u003c/em\u003e [\u003cem\u003eE. faecalis\u003c/em\u003e] is a Gram-positive bacterium commonly found in the gut microbiota of both humans and animals, making it a potential pathogen in various systemic infections [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is frequently transmitted through the fecal-oral route and can exist in different environmental conditions, from being a commensal microbe to causing severe hospital-acquired infections [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Enterococci are known to be resilient, with the ability to withstand oxidative stress, high pH levels, and elevated salt concentrations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They are responsible for a range of infections, including urinary tract infections, bacteremia, endocarditis, and infections associated with medical devices, often posing challenges due to their resistance to multiple antibiotics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAntibiotic resistance is a pressing global health concern, with microorganisms evolving resistance to antibiotics due to factors such as overuse, misuse, and improper treatment practices [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Teixobactin, a novel antibiotic, has emerged as a promising solution to combat antibiotic resistance, as it exhibits no apparent bacterial resistance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Comprising unique amino acids, Teixobactin has demonstrated remarkable efficacy against clinically significant Gram-positive bacteria, including vancomycin-resistant Enterococci [VRE] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Its mechanism of action involves disrupting bacterial cell wall formation, potentially by binding to specific molecules like lipid II and lipid III, leading to autolytic cell death [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of addressing antimicrobial resistance [AMR], the identification of resistant genes is crucial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. High-throughput RNA sequencing [RNA-seq] offers a powerful tool to analyze gene expression and uncover pathways associated with AMR [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, we aim to identify differentially expressed genes [DEGs] in \u003cem\u003eE. faecalis\u003c/em\u003e exposed to Teixobactin using two distinct RNA-seq pipelines, explore the pathways associated with these DEGs, and pinpoint prominent AMR genes that could serve as potential targets for drug development. The pipelines employed include the conventional RNA-seq pipeline [bowtie2/Rsubread/DESeq2] and the cufflink pipeline [tophat/cufflink/cuffdiff/cummerbund], offering comprehensive insights into the genetic mechanisms behind Teixobactin resistance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study endeavors to contribute to the ongoing efforts to combat AMR by uncovering the genetic basis of resistance and identifying promising targets for future antibiotics.\u003c/p\u003e \u003cp\u003eAdditionally, understanding the transcriptomic response of \u003cem\u003eE. faecalis\u003c/em\u003e to Teixobactin can provide valuable insights into the adaptative strategies employed by this bacterium in the face of antibiotic pressure. The comprehensive analysis of gene expression profiles will shed light on the molecular mechanisms that govern the development of resistance and the potential evolution of new resistance strategies. Such insights are essential not only for the development of effective therapeutic interventions but also for the surveillance and management of antibiotic resistance in clinical and environmental settings. This study bridges the gap between genomics and clinical outcomes, offering a holistic approach to address the urgent challenge of antibiotic resistance in \u003cem\u003eEnterococcus faecalis\u003c/em\u003e.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dataset Selection and preprocessing\u003c/h2\u003e \u003cp\u003eThe dataset used in this study was obtained from the SRA database [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], with run accession ID starting from ERR2287711 to ERR2287716 with project id PRJEB24904. It was retrieved in Fastq format from the ENA database [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/ena/browser/view/PRJEB24904\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/ena/browser/view/PRJEB24904\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. This dataset comprised six sets of samples, with three subjected to antibiotic treatment and three serving as untreated controls.\u003c/p\u003e \u003cp\u003eTo ensure data quality, an initial assessment of the quality of each FASTQ file was conducted using the FastQC tool (Version-0.12.1) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Subsequently, the generated FastQC reports were consolidated using the MultiQC tool (version-1.20) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://multiqc.info/\u003c/span\u003e\u003cspan address=\"https://multiqc.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Bioinformatics Pipeline 1 [Alignment and DE Analysis]:\u003c/h2\u003e \u003cp\u003eIn the initial bioinformatics pipeline, our primary focus was on aligning the raw sequencing reads to the reference genome, specifically E. faecalis JH2-2 [GenBank accession number NZ_KI518257.1]. This alignment task was carried out utilizing Bowtie2 (version-2.5.1), a widely-used alignment tool known for its efficiency and accuracy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Impressively, this alignment process achieved a high mapping efficiency, surpassing 80% for each individual read. Subsequently, to obtain count data for individual transcripts, we employed the R Subread package (version-2.16.0) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], an essential Bioconductor tool. All of these operations were executed within the R programming environment and R studio(version-4.3.0). For the crucial task of identifying differentially expressed genes [DEGs], we harnessed the powerful DESeq2 package (version-1.42.0) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] from R. To visualize the results pertaining to DEGs, we made effective use of the ggplot2 package (version-3.4.1) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], a vital component of R's rich visualization capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Bioinformatics Pipeline 2 [Alignment and DE Analysis]:\u003c/h2\u003e \u003cp\u003eThe second phase of our bioinformatics pipeline initiated with the alignment of the raw sequencing reads to the reference genome, E. faecalis JH2-2. This alignment process was carried out using Tophat (version-2.0.10), which relies on Bowtie as its underlying alignment engine. Following the alignment step, we transitioned to the Cufflink pipeline (version-2.2.1) for an in-depth analysis of DEGs. This comprehensive pipeline encompasses various stages, including transcript assembly, abundance estimation, and the assessment of differential gene expression in RNA-Seq datasets. Cuffdiff, a pivotal component of the Cufflinks package, played a central role in our analysis. It not only facilitated the quantification of expression levels but also assessed the statistical significance of observed changes in gene expression. Furthermore, Cuffdiff enabled the identification of genes that exhibited differential regulation at both the transcriptional and post-transcriptional levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Gene Ontology and Pathway Enrichment Analysis:\u003c/h2\u003e \u003cp\u003eTo elucidate the functional attributes of genes, gene ontology annotations were employed. ShinyGO [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go74/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go74/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] (version-0.741) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], a user-friendly web application, was chosen for gene ontology analysis. It provided intuitive visualization of enrichment results, elucidating the biological function, cellular component, and molecular function of the DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Protein-Protein Interaction Network [STRING] and visualization\u003c/h2\u003e \u003cp\u003eTo explore protein-protein interactions, the STRING database (version-12.0) [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was harnessed. This comprehensive database encompasses direct and indirect protein interactions, each assigned a confidence score to denote interaction strength. Confidence scores were categorized as highest [above 0.90], high [0.7 to 0.89], medium [0.4 to 0.69], and low [0.15 to 0.39]. For the visualization of molecular interaction networks, Cytoscape (version 3.10.1) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], an open-source bioinformatics software platform, was employed. It offers a multitude of tools for network analysis, including gene clustering and enrichment analysis. The CytoHubba plugin (version-0.1) within Cytoscape facilitated the identification of significant hub genes within these biological networks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Bioinformatics Pipeline 1\u003c/h2\u003e \u003cp\u003eThe study focused on the transcript-level data to identify differentially expressed genes [DEGs]. To accomplish this, we employed the DESeq2 package integrated into the R programming environment. Among the 474 transcripts that exhibited non-zero counts, our investigation unveiled 375 genes that demonstrated statistically significant differential expression. This set comprised 178 genes with upregulated expression and 197 genes displaying downregulation. To visually represent these findings comprehensively, we harnessed various graphical tools, including dispersion plots, PCA plots, MA plots, and volcano plots, all generated using the R ggplot2 package [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In order to establish rigorous criteria for statistical significance, we applied a screening threshold defined as an adjusted p-value below 0.05. Furthermore, we presented the top 30 most significant genes in the form of a heatmap, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Bioinformatics Pipeline 2\u003c/h2\u003e \u003cp\u003eIn the second segment of the bioinformatics pipeline, the merged files generated by cuffmerge were utilized, and cuffdiff was applied to explore the intricate landscape of differential gene expression and regulation. The outcomes were meticulously documented in a series of tab-delimited text files, serving as the foundation for subsequent analyses. For comparative assessments between different treatments, the cummeRbund package within the R environment was employed. This comprehensive analysis revealed the presence of 2,454 genes, 5,195 isoforms, 2,819 transcription start sites [TSS], 2,741 coding sequences [CDS], 2,454 promoters, 2,819 splicing events, and 2,375 relative CDS [coding sequences], among other key attributes.\u003c/p\u003e \u003cp\u003eTo provide a comprehensive visualization of the expression levels in each treatment, density plots were generated using the csDensity function. Additionally, csVolcano was utilized to create volcano plots, which offer a graphical representation of differentially expressed genes between treatments. Furthermore, the csScatter function was applied to generate scatter plots, facilitating the comparison of gene expression patterns across two distinct samples [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Enrichment analysis\u003c/h2\u003e \u003cp\u003eGene ontology is a fundamental tool in bioinformatics, utilized to annotate genes and their products. It categorizes genes into three major ontological classes: Biological Process [BP], Molecular Function [MF], and Cellular Component [CC]. To discern the biological functions of differentially expressed genes [DEGs] derived from both analysis pipelines, we focused on the common genes identified by both methods. In total, 142 upregulated and 150 downregulated common genes were uncovered [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese common genes underwent annotation using ShinyGO to elucidate their functions. Among the upregulated DEGs in BP, we observed involvement in processes such as cellular processes, cellular metabolic processes, organic substance metabolic processes, metabolic processes, and primary metabolic processes, among others. In terms of CC, these DEGs were predominantly associated with intracellular locations, cytoplasmic regions, and various cellular anatomical entities. For MF, the enriched terms encompassed binding, catalytic activity, ion binding, small molecule binding, and nucleoside phosphate binding, to name a few as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003eConversely, the downregulated genes exhibited functional enrichment in biological processes like protein metabolic processes, cellular protein metabolic processes, macromolecule biosynthetic processes, cellular macromolecule biosynthetic processes, amide biosynthetic processes, and translation in BP. Their cellular localization, as indicated by CC, included intracellular regions, cellular anatomical entities, ribosomes, organelles, non-membrane-bounded organelles, and intracellular organelles. In terms of MF, these genes were associated with functions such as binding, heterocyclic compound binding, organic cyclic compound binding, nucleic acid binding, and RNA binding [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo further gain insights into the functional pathways associated with the differentially expressed genes [DEGs], we conducted KEGG pathway enrichment analysis using a STRING database. This analysis revealed that the upregulated genes were prominently involved in several vital pathways, including metabolic pathways, biosynthesis of secondary metabolites, fatty acid metabolism, fatty acid biosynthesis, purine metabolism, and biosynthesis of amino acids, among others. These pathways play critical roles in various cellular processes and metabolic activities [Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Protein-protein interaction network\u003c/h2\u003e \u003cp\u003eThe analysis of differentially expressed genes [DEGs] extended to exploring protein-protein interactions. All common DEGs from both pipelines were uploaded to the STRING database and visualized in Cytoscape. This interaction network, as revealed by STRING, comprised 212 nodes, 3,038 edges, an average node degree of 28.7, an average local clustering coefficient of 0.493, and a highly significant protein-protein interaction [PPI] enrichment p-value of \u0026lt;\u0026thinsp;1.0e-16 .\u003c/p\u003e \u003cp\u003eFurther investigation involved identifying the top 100 hub genes using the CytoHubba plugin, which were subsequently subjected to cluster analysis. The MCODE plugin in Cytoscape facilitated this analysis, resulting in the identification of five distinct clusters denoted as C1, C2, C3, C4, and C5 [Figure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e]. This clustering allowed us to gain insights into highly interconnected regions within the gene interaction network. Out of the 100 genes in the network, 74 were identified as part of these clusters. Among them, cluster C1 exhibited the highest degree of interconnectedness, consisting of 54 nodes and 1,328 edges with an MCODE score of 50.1. This was followed by C2 with 6 nodes and 12 edges, scoring 4.8, C3 with 6 nodes and 10 edges, scoring 4.0, C4 with 5 nodes and 7 edges, scoring 3.5, and C5 with 3 nodes and 3 edges, scoring 3.0.\u003c/p\u003e \u003cp\u003eFor a more focused examination, the upregulated genes identified by DESeq were utilized to pinpoint highly interactive genes, or hub genes, using the CytoHubba plugin. The top 10 hub genes identified were guaA, guaB, lepA, der, secA, ftsH, obg, nusG, dnaA, and ffh. These genes, directly or indirectly contributing to bacterial resistance, have the potential to serve as valuable targets for various drugs due to their involvement in antimicrobial resistance [AMR] mechanisms [Figure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTolerance to antimicrobials plays a pivotal role in the development of antimicrobial resistance [AMR] within bacteria, often arising from the inappropriate use of antibiotics in clinical and agricultural settings [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This resistance can propagate through various channels, such as direct exposure, the food chain, and environmental transmission, leading to the emergence of antimicrobial resistance genes [ARGs] in diverse sources, including humans, animals, food, plants, and the environment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTeixobactin, a novel cyclic depsipeptide containing the rare amino acid enduracididine, has emerged as a groundbreaking class of antibiotics designed to target specific cellular processes involved in cell wall production [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite the global surge in antibiotic resistance among Gram-positive bacteria, teixobactin has demonstrated effectiveness against them [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, some bacteria, like Enterococcus faecalis, have innate tolerance to high doses of teixobactin, prompting investigations into the molecular mechanisms underlying this tolerance and its potential contribution to teixobactin resistance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we conducted comprehensive RNA-seq analysis of the entire transcriptome using three samples subjected to antibiotic treatment and three untreated controls. We employed two distinct bioinformatics pipelines for analysis, using two pipelines improves result reliability. When two pipelines discover the same AMR genes, it increases the likelihood of their occurrence. The conventional RNA-seq pipeline involving bowtie2, Rsubread, and DESeq2, as well as the cufflink pipeline employing tophat, cufflink, cuffdiff, and cummerbund. Subsequently, we pinpointed common differentially expressed genes [DEGs] identified by both pipelines for further scrutiny [\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e–\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis encompassed gene ontology investigations of DEGs pertaining to biological processes, cellular components, and molecular functions, facilitated by ShinyGO. Furthermore, we delved into pathway analysis via the KEGG pathway analysis tool using the STRING database. Protein-protein interactions were explored using the STRING database and visualized in Cytoscape. Among the identified hub genes, guaA and guaB, which encode GMP synthetase and IMP dehydrogenase [IMPDH], respectively, emerged as pivotal players in de novo purine synthesis, a process essential for bacterial viability. The guanine nucleotide biosynthesis pathway, in which these genes play a role, holds significance in various cellular processes, rendering them promising targets for antibiotic development [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] The potential therapeutic benefits of targeting guaB or guaA are evident in their potential to combat persistent bacterial infections [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecA, another upregulated gene within our study, holds a crucial role in the post-translational translocation of secretory and outer membrane proteins across the SecYEG translocation machinery. It proves indispensable for bacterial adhesion to host cells and presents itself as a viable target for antibacterial interventions [\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. FtsH, an ATP-dependent protease, surfaces as a key component of bacterial virulence and resilience in the face of diverse environmental stressors. Its upregulation suggests a role in stress response and adaptation, thus positioning it as a candidate for antibiotic targeting [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e–\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLepA, involved in protein synthesis, contributes to ribosome back-translocation and is implicated in ribosome biosynthesis. Disrupting LepA's function could impede protein synthesis and inhibit bacterial growth [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e–\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Obg, characterized as a monomeric guanine nucleotide binding protein, is associated with antibiotic tolerance and the formation of persister cells. Inhibition of Obg could enhance bacterial susceptibility to antibiotics [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e–\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. NusG, functioning as an elongation factor, plays a pivotal role in gene expression regulation. Targeting NusG might disrupt transcription processes, thereby serving as a valuable target for novel antibiotics [\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e–\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In addition to these highlighted genes, der, dnaA, and ffh also exhibited significant interactions and may warrant consideration as potential targets for antibiotic development. Ffh, for instance, plays a pivotal role in protein translocation, while DnaA assumes a central role in initiating chromosomal replication [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e–\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable insights into the intricate network of highly interconnected genes within Enterococcus faecalis, shedding light on their potential roles in driving antimicrobial resistance [AMR] and tolerance mechanisms. Notably, genes such as guaA, guaB, secA, ftsH, lepA, obg, NusG, and others have emerged as compelling candidates for targeted exploration in the pursuit of innovative antibiotics aimed at mitigating the pressing issue of AMR.\u003c/p\u003e\u003cp\u003eThe current investigation underscores the presence of resistance genes in \u003cem\u003eE. faecalis\u003c/em\u003e. Through rigorous gene ontology and pathway analyses of the upregulated genes, we unveiled their active involvement in pivotal cellular processes encompassing cellular metabolism, binding, catalytic activities, and more. These carefully selected genes, known to influence resistance, were meticulously examined for their significant interactions. The results showcased a highly enriched interaction network, shedding light on the critical drug resistance genes within E. faecalis, with a particular focus on their response to texiobactin. The comprehensive examination of the functions of these pivotal genes positions them as promising targets warranting further in-depth exploration in the quest to combat AMR effectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeepika\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAishwarya C Shetty\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization.\u0026nbsp;\u003cstrong\u003eDhanushKumar T\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Formal analysis, Conceptualization\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKarthick Vasudevan\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization, Project administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express deep gratitude to the management of REVA University, for providing necessary facilities, assistance, and constant encouragement to carry out this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJarkhi, A., Lee, A. 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[2002]. Functional analysis of the signal recognition particle in Escherichia coli by characterization of a temperature-sensitive ffh mutant. Journal of bacteriology, 184[10], 2642-2653.\u003c/li\u003e\n \u003cli\u003eMenikpurage, I. P., Woo, K., \u0026amp; Mera, P. E. [2021]. Transcriptional activity of the bacterial replication initiator DnaA. Frontiers in Microbiology, 12, 662317.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Teixobactin Resistance, Enterococcus faecalis, Integrated RNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-4316554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4316554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial resistance (AMR) poses a severe and pressing global health crisis, necessitating urgent innovative approaches to combat drug-resistant bacteria. This study investigates the genetic underpinnings of resistance in Enterococcus faecalis., a Gram-positive bacterium, in response to the novel antibiotic Teixobactin. Leveraging whole transcriptome RNA-seq analysis and sophisticated bioinformatics tools, we have identified ten central hub genes: guaA, guaB, lepA, der, secA, ftsH, obg, nusG, dnaA, and ffh. These genes display significant upregulation and robust interactions within the bacterial genome.\u003c/p\u003e \u003cp\u003eOur comprehensive analysis uncovers the involvement of these genes in diverse critical cellular functions associated with antibiotic resistance. These functions encompass purine metabolism, protein export, stress response, transcriptional regulation, and ribosomal activities. These findings provide crucial insights into the intricate molecular mechanisms underpinning \u003cem\u003eEnterococcus faecalis\u003c/em\u003e resistance to Teixobactin. Furthermore, potential targets were identified for the development of advanced antibiotics, aligning with the ongoing global efforts against Antimicrobial Resistance (AMR), these identified hub genes offer promising avenues for novel drug discovery, bolstering the ongoing crusade against drug-resistant bacterial infections.\u003c/p\u003e","manuscriptTitle":"Deciphering Teixobactin Resistance Mechanisms in Enterococcus faecalis Through Integrated RNA-seq and Hub Genes Identification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 06:02:34","doi":"10.21203/rs.3.rs-4316554/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6b8ddf7-daf1-4dc1-95df-db3d25cbdf06","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T16:19:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 06:02:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4316554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4316554","identity":"rs-4316554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}