Deciphering Carbapenem-Resistance Mechanisms by DIA Proteomics Analysis in clinical isolates of Pseudomonas aeruginosa

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Prestagiacomo, Marta Pantanella, Caterina Gabriele, Annarita Giuliano, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9267285/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background The excessive use of antibiotics has promoted the global spread of a resistant bacterial strains leading to a silent pandemic. Drug-resistant infections are currently responsible for approximately 700,000 deaths annually, a number projected to rise to 10 million by 2050 if effective containment strategies are not implemented. In this context, carbapenem-resistant Pseudomonas aeruginosa (CRPA) represents a serious public health threat due to its ability to escape multiple classes of antibiotics, including those of the latest generation. Method In this work, a mass spectrometry-based proteomics approach relying on Data-Independent Acquisition (DIA) was applied to compare 16 CRPA and 18 carbapenem-sensitive of P. aeruginosa clinical isolates. Results A total of 4,227 proteins were quantified representing the highest number of identified proteins reported to date in clinical isolates of P. aeruginosa. Among these, 26 proteins exhibited significant differential expression between two groups. Briefly, CRPA isolates showed increased expression of proteins implicated in the metabolism of phosphate (e.g. pstC, pstS, pstA, phoB, phoX), as well as those related to sugar, glycerol, amino acids metabolism. Up-regulation of transport-associated proteins, including ABC transport and OprO porin, was also observed. Conversely, CRPA showed marked downregulation of OprD a porin associated with reduced- ß-lactam permeability. Some of the regulated proteins have not yet been characterized, underscoring the significant work that remains to fully elucidate the molecular dynamics underlying resistance. Conclusion The deep proteome coverage enabled by DIA provides valuable insights into resistance-associated pathways, thus supporting to surveillance of multidrug-resistant infections and promoting the development of more effective therapeutic strategies. Antibiotics CRPA Data-Independent Acquisition Mass spectrometry resistance mechanisms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Pseudomonas aeruginosa is a Gram-negative opportunistic bacterium that causes respiratory, urinary tract, surgical site and bloodstream infections, resulting in 300,000 deaths each year. In 2017, the World Health Organization (WHO) classified P. aeruginosa as “priority one pathogen” due to its role in healthcare-associated infections and its high level of antibiotic resistance, especially to carbapenems ( 1 ). Carbapenem-resistance in P. aeruginosa ( 2 , 3 ) (CRPA) is mediated by multiple mechanisms including biofilm formation, membrane permeability modulation (e.g. overexpression of efflux pumps and downregulation of porins) ( 4 , 5 ) and carbapenemases production ( 6 ). The most frequently detected carbapenemases in P. aeruginosa are metallo-ß-lactamase of class B including Verona Imipenemase (VIM), Imipenemase (IMP), German Imipenemase (GIM), and New Delhi metallo-ß-lactamase (NDM)( 7 ). Instead, class A carbapenemase like Klebsiella pneumoniae carbapenemase (KPC) and Guyana extend-spectrum ß-lactamase (GES) are less common but still clinically significant ( 8 ). These enzymes restrict therapeutic strategies and are associated with increased mortality. Therefore, the implementation of approaches capable of identifying the carbapenemases represents a valuable support to elaborate a personalized therapy and to intervene before the infection progresses to a fatal outcome. A significant advancement toward this goal has been made through genomics, which currently allows both the identification of bacterial strains, based on the genetic material detected in the sample, and the presence of resistance genes. However, the detection of a resistance gene, does not provide information regarding its expression or the corresponding gene product. Consequently, proteomics could offer an important contribution by identifying protein expression patterns associated with the CRPA phenotype, thereby suggesting novel biomarkers ( 9 , 10 ). To date, proteomics has been used to identify bacterial isolates by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry ( 11 ). MALDI-TOF requires relatively simple sample preparation and short analysis times; however, it is unable to discriminate between closely related strains, requiring the use of different platforms. In contrast, high-resolution mass spectrometry (HRMS) coupled with nano-liquid chromatography offers accurate pathogen identification combined with a comprehensive proteomic profiling, including proteins potentially involved in resistance pathways. This approach may significantly advance: (i) the understanding of resistance mechanisms, (ii) epidemiological surveillance and (iii) the development of more effective therapeutic strategies. However, the potential of HRMS in routine clinical settings is limited by several factors, such as complex sample preparation, high costs, and the need for skilled operators. For this reason, the current research has focused on developing streamlined workflows for high-throughput sample processing. In this study, we employed high-resolution mass spectrometry in data-independent acquisition (DIA; ( 12 )) mode to analyse the proteome of carbapenem-susceptible and resistant clinical isolates of P. aeruginosa. This approach enabled the identification and quantification of nearly the entire P. aeruginosa proteome, including also proteins at low abundance; these proteins often function as molecular switches of key biological processes. For this reason, the DIA approach represents a promising strategy for detailed proteomic mapping of P. aeruginosa and to detect proteomic signatures linked to resistance phenotypes. MATERIALS AND METHODS Clinical isolates of P. aeruginosa were obtained from samples of patients hospitalized between 2022 and 2025 at the “Renato-Dulbecco” University Hospital of Catanzaro. Bacterial isolation, identification and Antimicrobial Susceptibility Testing In this study, proteomics analysis was performed on clinical isolates of P. aeruginosa with two distinct susceptibility profile: 16 CRPA isolates and 18 carbapenem-sensitive isolates. For CRPA isolates, clinical specimens were sourced from the following sites: throat swab (n = 3), rectal swab (n = 1), wound swab (n = 1), blood (n = 3), bronchoalveolar lavage (n = 2), urine (n = 3), bronchial aspirates (n = 3). Carbapenem-sensitive isolates were obtained from: blood (n = 10), skin swab (n = 2), wound swab (n = 2), urine (n = 2), bronchoalveolar lavage (n = 1), sputum (n = 1). The identification of clinical isolates was carried out using MALDI-TOF and Vitek® 2 System (bioMèrieux, Italia). Antimicrobial susceptibility testing was conducted using the Vitek ® 2 system, following the guidelines of the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Briefly, the minimal inhibitory concentration (MIC) for each strain was categorized as: susceptible (S), susceptible increased exposure (I) and resistant (R) based on the EUCAST breakpoints criteria. Bacterial strains and Protein Sample Preparation The bacterial isolates were streaked onto Agar Blood plates and incubated for 18–24 h at 37°C. Following incubation, the bacterial strains were transferred to 500 µL of physiological saline solution (0.9% NaCl) and processed as previously described by Oscarsson et al. ( 13 ). In detail, the bacterial cells were collected by centrifugation at 13,000 rpm for 15 min and the cell pellets were resuspended in the lysis buffer composed by 4% sodium dodecyl sulphate (SDS), 100 mM Tris HCl pH 8.0 e 10 mM dithiothreitol (DTT). The samples were incubated in a thermomixer at 95°C, with shaking at 900 rpm for 5 min. After incubation, the samples were placed on ice (40 s) and subsequently subjected to an alternating cycle of sonication and exposure to room temperature, with each phase lasting 3 min; the sonication/room temperature cycle was repeated twice (Fig. 1 ). Finally, the samples were centrifuged at 14,000 rpm for 20 min and the protein concentration of the supernatant was determined using the Qubit protein broad range assay kit (Thermo Fisher Scientific). In addition to the clinical isolates, the P. aeruginosa reference strain ATCC 27853 (KWIK STIK) was processed in quintuplicate to serve as reference sample within the workflow. Protein Aggregation Capture An aliquot containing twenty-micrograms of protein from each sample was subjected to cysteine alkylation by 24 mM (final concentration) iodoacetamide (IAA) for 1h at 37°C. Ten micrograms (half of the reduced and alkylated sample) were processed using the Protein Aggregation Capture (PAC, Fig. 2 ) protocol ( 14 ). Briefly, 5 µL of MagResyn Hydroxyl beads, corresponding to 100 µg of magnetic beads (Resyn Biosciences), were conditioned with 70% acetonitrile (ACN) and added to each sample. Protein precipitation on the magnetic beads was performed by adding ACN to a final concentration of 70%, followed by incubation in a thermomixer at room temperature with 1100 rpm for ten minutes. After incubation, the samples were placed on a magnetic rack to separate and discard the supernatant. The samples were washed four times: three washes with 100% ACN and one wash with 70% ethanol. After removing the last wash, 51 µL of digestion buffer containing 50 mM triethylammonium bicarbonate (TEAB) and 200 ng of trypsin was added to the beads and incubated overnight at 37°C with shaking at 1100 rpm. The following day, the samples were placed on the magnetic rack to collect the peptide mixture. After collection, 50 µL of 0,1% formic acid (FA) solution was added to the beads (2 min at room temperature, 1100 rpm) to promote the elution of peptides still bound to the beads. The peptides were dissolved in approximately 100 µL of solution. Strong Cation Exchange purification Typically, samples processed using the PAC protocol are suitable for direct LC-MS/MS injection. However, in our experience, when the extraction buffer contains SDS at concentrations higher than 1%, a purification step using a strong cation exchange (SCX; ( 15 )) sorbent is required to remove residual detergent. Specifically, half of the resulting peptide mixture (corresponding to 5 µg of proteins) was purified as follows: 50 µL of the peptide mixture were diluted 4-fold in wash 2 solution (0.5% FA and 80% ACN) to reduce the salt concentration to below 5 mM, as higher concentrations interfere with peptide binding to the resin. The purification procedure involved the following steps: (i) conditioning the resin with 50 µL of wash 1 (0.5% FA and 20% ACN) and 50 µL of wash 2, (ii) loading the diluted sample, (iii) washing the resin with 50 µL of wash 1, (iv) washing the resin with 50 µL of wash 2, and (v) eluting the peptides in 10 µL of solution containing 500 mM ammonium acetate and 20% ACN. The resulting eluate was evaporated at 30°C and the peptides were resuspended in 50 µL of solution A (0.1% FA and 2% ACN). LC-MS/MS analysis Peptides were separated by using an Easy nLC-1200 chromatographic instrument coupled to an Exploris 480 mass spectrometer (both from Thermo Scientific, Bremen, Germany). Peptides mixtures were separated using a linear gradient over 63 min at a flow rate of 300 nL/min on a 15 cm, 75 µm i.d., in-house-made column packed with 3 µm C 18 silica particles (Dr. Maisch). The binary gradient was performed with mobile phase A (0.1% FA, 2% ACN) and mobile phase B (0.1% FA and 80% ACN). Peptide elution was conducted with the following gradient: from 6% B to 14% B in 16 min, from 14% to 40% in 32 min and from 40% to 100% in 8 min. The column was cleaned for 5 min with 100% B. The mass spectrometer was operated in DIA mode (scan range 350–1000 m/z ) using a method consisting of 32 consecutive windows with a resolution of 30,000, AGC target 5 x 10 5 and maximum injection time equal to 50 ms. Specifically, the method included (i) 24 windows with an isolation window of 15 m/z , (ii) 5 windows with an isolation window of 30 m/z and (iii) 3 windows with an isolation window of 50 m/z; each window overlapped by 0.5 m/z. DIA data processing The 39 DIA raw files (34 clinical isolates and 5 replicates of ATCC) were searched by Spectronaut software (Biognosys, version 19.0) against the P. aeruginosa UniProt protein sequence database (May 2025, 5563 sequences) with default parameters, applying the “only protein group specific” filter for protein quantification. Precursors were filtered retaining only those identified in at least the 26% of the runs. Missing values were imputed using background signal as imputation strategy and the data were finally normalized using “global normalization”. Statistical analysis was performed with Perseus software (version 2.0.11.0, Max-Planck-Gesellschaft, München). Briefly, protein intensity values were log-transformed (log 2 ) and only proteins quantified in at least 75% of at least one sample group were retained; missing values were imputed using a width of 0.3 SD and down shift of 1.8 SD (default settings). Significantly different proteins between CRPA and carbapenem-sensitive were detected using Student’s t-test corrected for multiple hypothesis testing with a Permutation-based FDR equal to 0.05 and S0 value of 0.2 (Fig. 3 ). RESULTS In this work, a total of 34 clinical isolates of P. aeruginosa and the P. aeruginosa reference strain ATCC were analysed. Based on the susceptibility profiles of clinical isolates two groups were defined:16 CRPA and 18 carbapenem-sensitive isolates (Table S1 and Table S2). The analysis of the clinical isolate distribution revealed that CRPA isolates predominantly originated from critical care units, such as intensive care. Proteomic analysis performed using DIA approach enabled the identification and quantification of a total of 4,227 proteins, of which 4,069 (Table S3) were quantified across the entire sample set (Fig. 4 ). Principal component analysis revealed a clear separation between the reference sample (ATCC) and the clinical isolates, demonstrating that the clinical isolates expressed distinctive proteomic signature (Fig. 5 ). Quantitative analysis between CRPA and carbapenem-susceptible isolates detected significant differences in the abundance of 26 proteins. Specifically, 19 proteins were upregulated in CRPA strains (Gene name: PA3383, PA1606, pstS, PA3318, glpF, ctpL, PA2635, PA3250, PA4913, PA0698, PA0980, glpK2, rbsB, PA1260, pstC, eddA, pstA, PhoB, oprO), while 7 proteins were downregulated (Gene name: ftls, oprD, PA4155, alg44, PA33066, PA2569, PA0223; Fig. 6 ). The list of significantly regulated proteins was subjected to a gene ontology-term enrichment analysis using the STRING( 16 ) database ( https://string-db.org ). This analysis showed a noteworthy finding: a subset of the 26 proteins appeared to be highly interconnected and involved in common functional pathways (Fig. 7 ). DISCUSSION Proteomic analysis performed using DIA approach enabled the identification and quantification of a total of 4,227 proteins, representing the most comprehensive proteomic mapping of clinical P. aeruginosa isolates to date ( 17 , 18 ). Of these 4,227 proteins, 4,069 proteins were quantified across all samples, highlighting the extensive proteome coverage achieved through DIA. This depth is due to ability of the DIA to detect low-abundance proteins, proteins which often act as modulators of signalling pathways. Therefore, this approach could be useful for identifying key proteins involved in antimicrobial resistance mechanisms, thus contributing to a deeper understanding of this phenomenon. In particular, in our comparison between CRPA and carbapenem-susceptible isolates 26 proteins significantly regulated were detected. Of these, 19 were upregulated and 7 downregulated in CRPA strains. Among the proteins increased in CRPA isolates many are implicated in the metabolism of phosphate, sugar, glycerol and amino acids and in ABC transport systems. In detail, the proteins encoded by pstC, pstS, pstA, PA3383 and PA3250 are known to play a crucial role in phosphate uptake and responses to environmental stress. PstS, in addition to its role in phosphate transport, actively contributes to biofilm formation under phosphate-limited-conditions ( 19 ). Moreover PhoB, another protein upregulated in CRPA isolates under low phosphate conditions, modulates: (i) the expression of the high-affinity phosphate transporter, (ii) quorum sensing, and (iii) secretion systems. All of these mechanisms promote bacterial survival, immune evasion, and infection persistence ( 20 , 21 ). This network also includes PhoX (a periplasmic alkaline phosphatase), OprO (a porine involved in pyrophosphate transport), and the methyl-accepting chemotaxis protein CtpL. PhoX, OprO and CtpL activate signalling pathways that promote biofilm formation in response to the environment ( 22 – 24 ). The increase in these proteins in CRPA isolates indicates a metabolic adaption that supports survival under antibiotic stress. Furthermore, compared to sensitive strains, CRPA strains exhibited increased expression of proteins involved in amino acid metabolism (Gene name:PA4913 and PA1260), suggesting that these strains play out an adaptive strategy to efficiently exploit available substrates under hostile conditions ( 25 , 26 ). This metabolic flexibility was further supported by the upregulation of (i) GlpF and GlpK2 which are involved in glycerol uptake and phosphorylation ( 27 , 28 ), respectively, (ii) sugar binding protein and D-ribose/D-allose-binding proteins (components of ABC transporter; ( 29 )). Despite these proteins do not play a direct role in resistance mechanisms, they contribute to bacteria adaptability and promote survival under adverse conditions. The multifactorial nature of the resistance phenotype was also confirmed by the increase of extracellular DNA degradation protein. Specifically, this protein is known to degrade neutrophil extracellular traps, thereby facilitating immune evasion, biofilm formation, tissue invasion, and supporting bacterial dissemination ( 30 – 32 ). In addition to the proteins mentioned above, upregulation of other proteins with currently poorly characterized function (PA0698, PA0980, PA1606) was also observed in CRPA strains. While their precise function remains to be fully elucidated, it could be hypothesized that these proteins play a role in membrane stability, a structure directly implicated in antibiotic resistance. For example, PA0980 (a lipoprotein), may contribute structurally to membrane integrity, whereas PA0223, a probable dihydropicolinate synthase involved in peptidoglycan biosynthesis, might enhance resistance through alteration in cell wall architecture. Among the downregulated proteins in CRPA strains, noteworthy was the porin OprD, a well-known marker of antibiotic resistance. Reduced levels of OprD have been associated with decreased permeability to ß-lactam antibiotics ( 33 – 35 ), a finding that was consistent with our observations. Additionally, a decrease in the abundance of peptidoglycan D,D-transpeptidase (gene name: FtsI), a key enzyme involved in peptidoglycan synthesis, was detected in CRPA strains. FtsI has been previously investigated in the context of antibiotic resistance, as mutation of this protein are known to reduce the binding affinity of ß-lactams, for which it serves as a pharmacological target ( 36 ). However, in our study, only a decrease in FtsI abundance was observed, and thus it remains unclear whether this reduction contributes to the carbapenem resistant. Regarding the downregulation of alginate synthase in CRPA, no evidence has been found in the literature. Considering the accumulation of alginate in resistant strains ( 37 , 38 ), it is possible to hypothesize the existence of a negative feedback mechanism, mediated by alginate itself, thus inhibiting the production of enzyme. Obviously, this hypothesis requires further study. As observed for a subset of overexpressed proteins, some of the downregulated proteins in CRPA also remain poorly characterized (Gene name: PA2569, PA3066, PA4155. Although a significant reduction in their expression was detected in CRPA strains within our sample set, their potential role in resistance mechanisms cannot be inferred. Further studies are needed to investigate their possible involvement in antibiotic resistance. Finally, our findings and the congruence with literature underscore the robustness of our experimental design highlighting the multifactorial and complex nature of resistance strategies of P. aeruginosa . In particular, the DIA analysis through the depth proteome mapping enriches the knowledge about the pathways associated to antibiotic resistance leading to better management of MDR infections. The detailed proteomic profiling of clinical isolates, in fact, provides a valuable resource to unravel novel resistance pathways, to detect new therapeutic targets and consequently to improve personalized therapy. CONCLUSIONS The remarkable adaptability of P. aeruginosa and its ability to develop multiple resistance mechanisms represent an increasing threat. A comprehensive understanding of the molecular dynamics can enhance current diagnostic approaches and open new avenues in the management of infections caused by MDR bacteria. In this context, the method presented here, based on the characterization of proteomics signatures associated with antibiotic resistance, offers a tangible response to this challenge. A better understanding and management of these infections is critical to achieving improved clinical outcome and, ultimately, establishing a patient-tailored treatment. Declarations ETHICS DECLARATION not applicable. FUNDING DECLARATION This research did not receive funding. Author Contribution L.E.P. conceptualization; L.E.P., M.P., C.G., A.G., M.G. methodology; N.M., G.M., A.Q. project administration; A.Q., M.G. supervision, A.Q., M.G. review and editing. Data Availability Data are available via ProteomeXchange with the identifier PXD074600 References Li J, Tang M, Liu Z, Wei Y, Xia F, Xia Y, et al. Molecular characterization of extensively drug-resistant hypervirulent Pseudomonas aeruginosa isolates in China. Ann Clin Microbiol Antimicrob. 2024;23(1):1–9. 10.1186/s12941-024-00674-7 . PubMed PMID: 38347529. Bassetti M, Vena A, Russo A, Croxatto A, Calandra T, Guery B. Rational approach in the management of Pseudomonas aeruginosa infections. 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Antibiotic-Resistant Pseudomonas aeruginosa: Current Challenges and Emerging Alternative Therapies. Microorganisms. 2025;13(4):1–29. 10.3390/microorganisms13040913 . Tran N, Tien N, Thu NQ, Kim DH, Park S, Long NP. EasyPubPlot: A Shiny Web Application for Rapid Omics Data Exploration and Visualization. 2025. 10.1021/acs.jproteome.4c01068 Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.xlsx Graphicalabstract.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 30 Mar, 2026 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-9267285","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635011290,"identity":"6c7cf5f7-6d37-441a-8be8-f405c831c9ce","order_by":0,"name":"Licia E. Prestagiacomo","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Licia","middleName":"E.","lastName":"Prestagiacomo","suffix":""},{"id":635011291,"identity":"df8f37fe-1e5e-4221-86e5-47c23b025fd7","order_by":1,"name":"Marta Pantanella","email":"","orcid":"","institution":"\"Magna Graecia\" University of Catanzaro- \"Renato Dulbecco\" Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Pantanella","suffix":""},{"id":635011292,"identity":"518f2fa4-4dda-40c1-9227-3a3276beec29","order_by":2,"name":"Caterina Gabriele","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Caterina","middleName":"","lastName":"Gabriele","suffix":""},{"id":635011293,"identity":"4cb708d0-2331-4b97-add9-d58d2c0fd8f0","order_by":3,"name":"Annarita Giuliano","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Annarita","middleName":"","lastName":"Giuliano","suffix":""},{"id":635011294,"identity":"21d89861-454a-4407-9e7b-755310c556d1","order_by":4,"name":"Nadia Marascio","email":"","orcid":"","institution":"\"Magna Graecia\" University of Catanzaro- \"Renato Dulbecco\" Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Marascio","suffix":""},{"id":635011295,"identity":"e3245b70-db27-445d-b14d-49227aa95035","order_by":5,"name":"Giovanni Matera","email":"","orcid":"","institution":"\"Magna Graecia\" University of Catanzaro- \"Renato Dulbecco\" Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Matera","suffix":""},{"id":635011296,"identity":"9d7104cd-b6c4-44f8-985c-b32a67cb3e26","order_by":6,"name":"Angela Quirino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYFACxgaGBAYGOTCDgUGCeC3GMC1E6QGDxAYog7AWvhvJbR8e7rBJ33C7ufHjDwaLOoJaJG8kNs9IPJOWu+HOwWagq4hwmAFQC0Ni2+HcDTcS2xgMSNGSbgDSkkCKlgSwlgPEaJE88xCkJc1wJlCvZIOBhGQDIS18x9MfM/5ss5Hnu5H+8OOPijp+grYA3YLiTsIa0LWMglEwCkbBKMACALrTPJz6PtoxAAAAAElFTkSuQmCC","orcid":"","institution":"\"Magna Graecia\" University of Catanzaro- \"Renato Dulbecco\" Teaching Hospital","correspondingAuthor":true,"prefix":"","firstName":"Angela","middleName":"","lastName":"Quirino","suffix":""},{"id":635011297,"identity":"4eade70a-d68b-4a55-af5f-c5109ab6269f","order_by":7,"name":"Marco Gaspari","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Gaspari","suffix":""}],"badges":[],"createdAt":"2026-03-30 13:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9267285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9267285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109280764,"identity":"2c562ab0-8a43-4cd0-9a8d-54fd2ba5cb51","added_by":"auto","created_at":"2026-05-14 17:28:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331194,"visible":true,"origin":"","legend":"\u003cp\u003eProtein extraction protocol summary.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/aac47f72c146f16929df4923.jpg"},{"id":109296555,"identity":"30ec67e4-07a1-44fc-a73e-071bac4b4e14","added_by":"auto","created_at":"2026-05-15 08:48:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233481,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows the main characteristics of the PAC protocol.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/df927bd8be9395589ca3599a.jpg"},{"id":109296473,"identity":"36db9ef0-f0b1-49bd-9b60-0ed2f18eebb1","added_by":"auto","created_at":"2026-05-15 08:47:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361749,"visible":true,"origin":"","legend":"\u003cp\u003eDIA data processing.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/c87e9919dabb8950068410ee.jpg"},{"id":109280768,"identity":"4782c8dd-818c-4c3d-998a-be1b5c0990bf","added_by":"auto","created_at":"2026-05-14 17:28:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552186,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of identified and quantified proteins in each analysed sample (ATCC, CRPA (R) and carbapenem-sensitive strains(S)).\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/c4e0a49a787f322e10578eae.jpg"},{"id":109280769,"identity":"247d9117-96f6-4d0c-97c5-79afe295e4ff","added_by":"auto","created_at":"2026-05-14 17:28:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":348489,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of clinical isolates (CPRA in red, carbapenem-sensitive in green) and ATCC (violet).\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/adaa46cdde26bf154ff68ee0.jpg"},{"id":109297770,"identity":"d025168f-8367-466e-9c0c-36d00e36bf06","added_by":"auto","created_at":"2026-05-15 09:05:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":406450,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots show the protein intensity (log2) of the 26 significantly different proteins between CRPA and carbapenem-susceptible strains. This figure was elaborated using EasyPubPlot (\u003ca href=\"https://pharmaco-omicslab.shinyapps.io/EasyPubPlot/\"\u003ehttps://pharmaco-omicslab.shinyapps.io/EasyPubPlot/\u003c/a\u003e)(39).\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/86e42fe9df129c35cac8de7c.jpg"},{"id":109280771,"identity":"1a57c683-c5ea-416c-91ae-b2b148a0997c","added_by":"auto","created_at":"2026-05-14 17:28:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":407057,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction of significantly regulated proteins between CRPA and carbapenem-susceptible strains, retrieved from the STRING database.\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/6292581615643f2e3766196d.jpg"},{"id":109299449,"identity":"21b90b65-6570-4665-8dd2-d383bfadb179","added_by":"auto","created_at":"2026-05-15 09:18:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2832244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/b81f345b-12a2-4bdb-8c4b-2531cf867e5c.pdf"},{"id":109280773,"identity":"5e304c97-2905-4d5f-9c85-06fe48796792","added_by":"auto","created_at":"2026-05-14 17:28:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2044367,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/f054a2b56333b48d37da221a.xlsx"},{"id":109280766,"identity":"a960ce9a-a23e-4f11-95a3-a817f42baafe","added_by":"auto","created_at":"2026-05-14 17:28:02","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1804440,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9267285/v1/5a6783a89066805149ad3a67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering Carbapenem-Resistance Mechanisms by DIA Proteomics Analysis in clinical isolates of Pseudomonas aeruginosa","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e is a Gram-negative opportunistic bacterium that causes respiratory, urinary tract, surgical site and bloodstream infections, resulting in 300,000 deaths each year. In 2017, the World Health Organization (WHO) classified \u003cem\u003eP. aeruginosa\u003c/em\u003e as \u0026ldquo;priority one pathogen\u0026rdquo; due to its role in healthcare-associated infections and its high level of antibiotic resistance, especially to carbapenems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCarbapenem-resistance in \u003cem\u003eP. aeruginosa\u003c/em\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) (CRPA) is mediated by multiple mechanisms including biofilm formation, membrane permeability modulation (e.g. overexpression of efflux pumps and downregulation of porins) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and carbapenemases production (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The most frequently detected carbapenemases in \u003cem\u003eP. aeruginosa\u003c/em\u003e are metallo-\u0026szlig;-lactamase of class B including Verona Imipenemase (VIM), Imipenemase (IMP), German Imipenemase (GIM), and New Delhi metallo-\u0026szlig;-lactamase (NDM)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Instead, class A carbapenemase like Klebsiella pneumoniae carbapenemase (KPC) and Guyana extend-spectrum \u0026szlig;-lactamase (GES) are less common but still clinically significant (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These enzymes restrict therapeutic strategies and are associated with increased mortality. Therefore, the implementation of approaches capable of identifying the carbapenemases represents a valuable support to elaborate a personalized therapy and to intervene before the infection progresses to a fatal outcome.\u003c/p\u003e \u003cp\u003eA significant advancement toward this goal has been made through genomics, which currently allows both the identification of bacterial strains, based on the genetic material detected in the sample, and the presence of resistance genes. However, the detection of a resistance gene, does not provide information regarding its expression or the corresponding gene product. Consequently, proteomics could offer an important contribution by identifying protein expression patterns associated with the CRPA phenotype, thereby suggesting novel biomarkers (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, proteomics has been used to identify bacterial isolates by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). MALDI-TOF requires relatively simple sample preparation and short analysis times; however, it is unable to discriminate between closely related strains, requiring the use of different platforms.\u003c/p\u003e \u003cp\u003eIn contrast, high-resolution mass spectrometry (HRMS) coupled with nano-liquid chromatography offers accurate pathogen identification combined with a comprehensive proteomic profiling, including proteins potentially involved in resistance pathways. This approach may significantly advance: (i) the understanding of resistance mechanisms, (ii) epidemiological surveillance and (iii) the development of more effective therapeutic strategies.\u003c/p\u003e \u003cp\u003eHowever, the potential of HRMS in routine clinical settings is limited by several factors, such as complex sample preparation, high costs, and the need for skilled operators. For this reason, the current research has focused on developing streamlined workflows for high-throughput sample processing.\u003c/p\u003e \u003cp\u003eIn this study, we employed high-resolution mass spectrometry in data-independent acquisition (DIA; (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)) mode to analyse the proteome of carbapenem-susceptible and resistant clinical isolates of \u003cem\u003eP. aeruginosa.\u003c/em\u003e This approach enabled the identification and quantification of nearly the entire \u003cem\u003eP. aeruginosa\u003c/em\u003e proteome, including also proteins at low abundance; these proteins often function as molecular switches of key biological processes.\u003c/p\u003e \u003cp\u003eFor this reason, the DIA approach represents a promising strategy for detailed proteomic mapping of \u003cem\u003eP. aeruginosa\u003c/em\u003e and to detect proteomic signatures linked to resistance phenotypes.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eClinical isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e were obtained from samples of patients hospitalized between 2022 and 2025 at the \u0026ldquo;Renato-Dulbecco\u0026rdquo; University Hospital of Catanzaro.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBacterial isolation, identification and Antimicrobial Susceptibility Testing\u003c/h2\u003e \u003cp\u003eIn this study, proteomics analysis was performed on clinical isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e with two distinct susceptibility profile: 16 CRPA isolates and 18 carbapenem-sensitive isolates.\u003c/p\u003e \u003cp\u003eFor CRPA isolates, clinical specimens were sourced from the following sites: throat swab (n\u0026thinsp;=\u0026thinsp;3), rectal swab (n\u0026thinsp;=\u0026thinsp;1), wound swab (n\u0026thinsp;=\u0026thinsp;1), blood (n\u0026thinsp;=\u0026thinsp;3), bronchoalveolar lavage (n\u0026thinsp;=\u0026thinsp;2), urine (n\u0026thinsp;=\u0026thinsp;3), bronchial aspirates (n\u0026thinsp;=\u0026thinsp;3). Carbapenem-sensitive isolates were obtained from: blood (n\u0026thinsp;=\u0026thinsp;10), skin swab (n\u0026thinsp;=\u0026thinsp;2), wound swab (n\u0026thinsp;=\u0026thinsp;2), urine (n\u0026thinsp;=\u0026thinsp;2), bronchoalveolar lavage (n\u0026thinsp;=\u0026thinsp;1), sputum (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eThe identification of clinical isolates was carried out using MALDI-TOF and Vitek\u0026reg; 2 System (bioM\u0026egrave;rieux, Italia). Antimicrobial susceptibility testing was conducted using the Vitek \u0026reg; 2 system, following the guidelines of the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Briefly, the minimal inhibitory concentration (MIC) for each strain was categorized as: susceptible (S), susceptible increased exposure (I) and resistant (R) based on the EUCAST breakpoints criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBacterial strains and Protein Sample Preparation\u003c/h3\u003e\n\u003cp\u003eThe bacterial isolates were streaked onto Agar Blood plates and incubated for 18\u0026ndash;24 h at 37\u0026deg;C. Following incubation, the bacterial strains were transferred to 500 \u0026micro;L of physiological saline solution (0.9% NaCl) and processed as previously described by Oscarsson et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn detail, the bacterial cells were collected by centrifugation at 13,000 rpm for 15 min and the cell pellets were resuspended in the lysis buffer composed by 4% sodium dodecyl sulphate (SDS), 100 mM Tris HCl pH 8.0 e 10 mM dithiothreitol (DTT). The samples were incubated in a thermomixer at 95\u0026deg;C, with shaking at 900 rpm for 5 min. After incubation, the samples were placed on ice (40 s) and subsequently subjected to an alternating cycle of sonication and exposure to room temperature, with each phase lasting 3 min; the sonication/room temperature cycle was repeated twice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, the samples were centrifuged at 14,000 rpm for 20 min and the protein concentration of the supernatant was determined using the Qubit protein broad range assay kit (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eIn addition to the clinical isolates, the \u003cem\u003eP. aeruginosa\u003c/em\u003e reference strain ATCC 27853 (KWIK STIK) was processed in quintuplicate to serve as reference sample within the workflow.\u003c/p\u003e\n\u003ch3\u003eProtein Aggregation Capture\u003c/h3\u003e\n\u003cp\u003eAn aliquot containing twenty-micrograms of protein from each sample was subjected to cysteine alkylation by 24 mM (final concentration) iodoacetamide (IAA) for 1h at 37\u0026deg;C.\u003c/p\u003e \u003cp\u003eTen micrograms (half of the reduced and alkylated sample) were processed using the Protein Aggregation Capture (PAC, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) protocol (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Briefly, 5 \u0026micro;L of MagResyn Hydroxyl beads, corresponding to 100 \u0026micro;g of magnetic beads (Resyn Biosciences), were conditioned with 70% acetonitrile (ACN) and added to each sample. Protein precipitation on the magnetic beads was performed by adding ACN to a final concentration of 70%, followed by incubation in a thermomixer at room temperature with 1100 rpm for ten minutes. After incubation, the samples were placed on a magnetic rack to separate and discard the supernatant. The samples were washed four times: three washes with 100% ACN and one wash with 70% ethanol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter removing the last wash, 51 \u0026micro;L of digestion buffer containing 50 mM triethylammonium bicarbonate (TEAB) and 200 ng of trypsin was added to the beads and incubated overnight at 37\u0026deg;C with shaking at 1100 rpm.\u003c/p\u003e \u003cp\u003eThe following day, the samples were placed on the magnetic rack to collect the peptide mixture. After collection, 50 \u0026micro;L of 0,1% formic acid (FA) solution was added to the beads (2 min at room temperature, 1100 rpm) to promote the elution of peptides still bound to the beads. The peptides were dissolved in approximately 100 \u0026micro;L of solution.\u003c/p\u003e\n\u003ch3\u003eStrong Cation Exchange purification\u003c/h3\u003e\n\u003cp\u003eTypically, samples processed using the PAC protocol are suitable for direct LC-MS/MS injection. However, in our experience, when the extraction buffer contains SDS at concentrations higher than 1%, a purification step using a strong cation exchange (SCX; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)) sorbent is required to remove residual detergent.\u003c/p\u003e \u003cp\u003eSpecifically, half of the resulting peptide mixture (corresponding to 5 \u0026micro;g of proteins) was purified as follows: 50 \u0026micro;L of the peptide mixture were diluted 4-fold in wash 2 solution (0.5% FA and 80% ACN) to reduce the salt concentration to below 5 mM, as higher concentrations interfere with peptide binding to the resin.\u003c/p\u003e \u003cp\u003eThe purification procedure involved the following steps: (i) conditioning the resin with 50 \u0026micro;L of wash 1 (0.5% FA and 20% ACN) and 50 \u0026micro;L of wash 2, (ii) loading the diluted sample, (iii) washing the resin with 50 \u0026micro;L of wash 1, (iv) washing the resin with 50 \u0026micro;L of wash 2, and (v) eluting the peptides in 10 \u0026micro;L of solution containing 500 mM ammonium acetate and 20% ACN.\u003c/p\u003e \u003cp\u003eThe resulting eluate was evaporated at 30\u0026deg;C and the peptides were resuspended in 50 \u0026micro;L of solution A (0.1% FA and 2% ACN).\u003c/p\u003e\n\u003ch3\u003eLC-MS/MS analysis\u003c/h3\u003e\n\u003cp\u003ePeptides were separated by using an Easy nLC-1200 chromatographic instrument coupled to an Exploris 480 mass spectrometer (both from Thermo Scientific, Bremen, Germany).\u003c/p\u003e \u003cp\u003ePeptides mixtures were separated using a linear gradient over 63 min at a flow rate of 300 nL/min on a 15 cm, 75 \u0026micro;m i.d., in-house-made column packed with 3 \u0026micro;m C\u003csub\u003e18\u003c/sub\u003e silica particles (Dr. Maisch). The binary gradient was performed with mobile phase A (0.1% FA, 2% ACN) and mobile phase B (0.1% FA and 80% ACN). Peptide elution was conducted with the following gradient: from 6% B to 14% B in 16 min, from 14% to 40% in 32 min and from 40% to 100% in 8 min. The column was cleaned for 5 min with 100% B.\u003c/p\u003e \u003cp\u003eThe mass spectrometer was operated in DIA mode (scan range 350\u0026ndash;1000 \u003cem\u003em/z\u003c/em\u003e) using a method consisting of 32 consecutive windows with a resolution of 30,000, AGC target 5 x 10\u003csup\u003e5\u003c/sup\u003e and maximum injection time equal to 50 ms. Specifically, the method included (i) 24 windows with an isolation window of 15 \u003cem\u003em/z\u003c/em\u003e, (ii) 5 windows with an isolation window of 30 \u003cem\u003em/z\u003c/em\u003e and (iii) 3 windows with an isolation window of 50 \u003cem\u003em/z;\u003c/em\u003e each window overlapped by 0.5 \u003cem\u003em/z.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDIA data processing\u003c/h2\u003e \u003cp\u003eThe 39 DIA raw files (34 clinical isolates and 5 replicates of ATCC) were searched by Spectronaut software (Biognosys, version 19.0) against the \u003cem\u003eP. aeruginosa\u003c/em\u003e UniProt protein sequence database (May 2025, 5563 sequences) with default parameters, applying the \u0026ldquo;only protein group specific\u0026rdquo; filter for protein quantification. Precursors were filtered retaining only those identified in at least the 26% of the runs. Missing values were imputed using background signal as imputation strategy and the data were finally normalized using \u0026ldquo;global normalization\u0026rdquo;.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed with Perseus software (version 2.0.11.0, Max-Planck-Gesellschaft, M\u0026uuml;nchen). Briefly, protein intensity values were log-transformed (log\u003csub\u003e2\u003c/sub\u003e) and only proteins quantified in at least 75% of at least one sample group were retained; missing values were imputed using a width of 0.3 SD and down shift of 1.8 SD (default settings).\u003c/p\u003e \u003cp\u003eSignificantly different proteins between CRPA and carbapenem-sensitive were detected using Student\u0026rsquo;s t-test corrected for multiple hypothesis testing with a Permutation-based FDR equal to 0.05 and S0 value of 0.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn this work, a total of 34 clinical isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e and the \u003cem\u003eP. aeruginosa\u003c/em\u003e reference strain ATCC were analysed. Based on the susceptibility profiles of clinical isolates two groups were defined:16 CRPA and 18 carbapenem-sensitive isolates (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2).\u003c/p\u003e \u003cp\u003eThe analysis of the clinical isolate distribution revealed that CRPA isolates predominantly originated from critical care units, such as intensive care.\u003c/p\u003e \u003cp\u003eProteomic analysis performed using DIA approach enabled the identification and quantification of a total of 4,227 proteins, of which 4,069 (Table S3) were quantified across the entire sample set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis revealed a clear separation between the reference sample (ATCC) and the clinical isolates, demonstrating that the clinical isolates expressed distinctive proteomic signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative analysis between CRPA and carbapenem-susceptible isolates detected significant differences in the abundance of 26 proteins. Specifically, 19 proteins were upregulated in CRPA strains (Gene name: PA3383, PA1606, pstS, PA3318, glpF, ctpL, PA2635, PA3250, PA4913, PA0698, PA0980, glpK2, rbsB, PA1260, pstC, eddA, pstA, PhoB, oprO), while 7 proteins were downregulated (Gene name: ftls, oprD, PA4155, alg44, PA33066, PA2569, PA0223; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe list of significantly regulated proteins was subjected to a gene ontology-term enrichment analysis using the STRING(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) database (\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). This analysis showed a noteworthy finding: a subset of the 26 proteins appeared to be highly interconnected and involved in common functional pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eProteomic analysis performed using DIA approach enabled the identification and quantification of a total of 4,227 proteins, representing the most comprehensive proteomic mapping of clinical \u003cem\u003eP. aeruginosa\u003c/em\u003e isolates to date (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Of these 4,227 proteins, 4,069 proteins were quantified across all samples, highlighting the extensive proteome coverage achieved through DIA. This depth is due to ability of the DIA to detect low-abundance proteins, proteins which often act as modulators of signalling pathways. Therefore, this approach could be useful for identifying key proteins involved in antimicrobial resistance mechanisms, thus contributing to a deeper understanding of this phenomenon.\u003c/p\u003e \u003cp\u003eIn particular, in our comparison between CRPA and carbapenem-susceptible isolates 26 proteins significantly regulated were detected. Of these, 19 were upregulated and 7 downregulated in CRPA strains.\u003c/p\u003e \u003cp\u003eAmong the proteins increased in CRPA isolates many are implicated in the metabolism of phosphate, sugar, glycerol and amino acids and in ABC transport systems. In detail, the proteins encoded by pstC, pstS, pstA, PA3383 and PA3250 are known to play a crucial role in phosphate uptake and responses to environmental stress. PstS, in addition to its role in phosphate transport, actively contributes to biofilm formation under phosphate-limited-conditions (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Moreover PhoB, another protein upregulated in CRPA isolates under low phosphate conditions, modulates: (i) the expression of the high-affinity phosphate transporter, (ii) quorum sensing, and (iii) secretion systems. All of these mechanisms promote bacterial survival, immune evasion, and infection persistence (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This network also includes PhoX (a periplasmic alkaline phosphatase), OprO (a porine involved in pyrophosphate transport), and the methyl-accepting chemotaxis protein CtpL. PhoX, OprO and CtpL activate signalling pathways that promote biofilm formation in response to the environment (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The increase in these proteins in CRPA isolates indicates a metabolic adaption that supports survival under antibiotic stress.\u003c/p\u003e \u003cp\u003eFurthermore, compared to sensitive strains, CRPA strains exhibited increased expression of proteins involved in amino acid metabolism (Gene name:PA4913 and PA1260), suggesting that these strains play out an adaptive strategy to efficiently exploit available substrates under hostile conditions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This metabolic flexibility was further supported by the upregulation of (i) GlpF and GlpK2 which are involved in glycerol uptake and phosphorylation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), respectively, (ii) sugar binding protein and D-ribose/D-allose-binding proteins (components of ABC transporter; (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eDespite these proteins do not play a direct role in resistance mechanisms, they contribute to bacteria adaptability and promote survival under adverse conditions. The multifactorial nature of the resistance phenotype was also confirmed by the increase of extracellular DNA degradation protein. Specifically, this protein is known to degrade neutrophil extracellular traps, thereby facilitating immune evasion, biofilm formation, tissue invasion, and supporting bacterial dissemination (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the proteins mentioned above, upregulation of other proteins with currently poorly characterized function (PA0698, PA0980, PA1606) was also observed in CRPA strains. While their precise function remains to be fully elucidated, it could be hypothesized that these proteins play a role in membrane stability, a structure directly implicated in antibiotic resistance. For example, PA0980 (a lipoprotein), may contribute structurally to membrane integrity, whereas PA0223, a probable dihydropicolinate synthase involved in peptidoglycan biosynthesis, might enhance resistance through alteration in cell wall architecture.\u003c/p\u003e \u003cp\u003eAmong the downregulated proteins in CRPA strains, noteworthy was the porin OprD, a well-known marker of antibiotic resistance. Reduced levels of OprD have been associated with decreased permeability to \u0026szlig;-lactam antibiotics (\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), a finding that was consistent with our observations. Additionally, a decrease in the abundance of peptidoglycan D,D-transpeptidase (gene name: FtsI), a key enzyme involved in peptidoglycan synthesis, was detected in CRPA strains. FtsI has been previously investigated in the context of antibiotic resistance, as mutation of this protein are known to reduce the binding affinity of \u0026szlig;-lactams, for which it serves as a pharmacological target (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, in our study, only a decrease in FtsI abundance was observed, and thus it remains unclear whether this reduction contributes to the carbapenem resistant.\u003c/p\u003e \u003cp\u003eRegarding the downregulation of alginate synthase in CRPA, no evidence has been found in the literature. Considering the accumulation of alginate in resistant strains (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), it is possible to hypothesize the existence of a negative feedback mechanism, mediated by alginate itself, thus inhibiting the production of enzyme. Obviously, this hypothesis requires further study.\u003c/p\u003e \u003cp\u003eAs observed for a subset of overexpressed proteins, some of the downregulated proteins in CRPA also remain poorly characterized (Gene name: PA2569, PA3066, PA4155. Although a significant reduction in their expression was detected in CRPA strains within our sample set, their potential role in resistance mechanisms cannot be inferred. Further studies are needed to investigate their possible involvement in antibiotic resistance.\u003c/p\u003e \u003cp\u003eFinally, our findings and the congruence with literature underscore the robustness of our experimental design highlighting the multifactorial and complex nature of resistance strategies of \u003cem\u003eP. aeruginosa\u003c/em\u003e. In particular, the DIA analysis through the depth proteome mapping enriches the knowledge about the pathways associated to antibiotic resistance leading to better management of MDR infections. The detailed proteomic profiling of clinical isolates, in fact, provides a valuable resource to unravel novel resistance pathways, to detect new therapeutic targets and consequently to improve personalized therapy.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe remarkable adaptability of \u003cem\u003eP. aeruginosa\u003c/em\u003e and its ability to develop multiple resistance mechanisms represent an increasing threat. A comprehensive understanding of the molecular dynamics can enhance current diagnostic approaches and open new avenues in the management of infections caused by MDR bacteria.\u003c/p\u003e \u003cp\u003eIn this context, the method presented here, based on the characterization of proteomics signatures associated with antibiotic resistance, offers a tangible response to this challenge.\u003c/p\u003e \u003cp\u003eA better understanding and management of these infections is critical to achieving improved clinical outcome and, ultimately, establishing a patient-tailored treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eETHICS DECLARATION\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eDECLARATION\u003c/p\u003e \u003cp\u003eThis research did not receive funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.E.P. conceptualization; L.E.P., M.P., C.G., A.G., M.G. methodology; N.M., G.M., A.Q. project administration; A.Q., M.G. supervision, A.Q., M.G. review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available via ProteomeXchange with the identifier PXD074600\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi J, Tang M, Liu Z, Wei Y, Xia F, Xia Y, et al. 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EasyPubPlot: A Shiny Web Application for Rapid Omics Data Exploration and Visualization. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jproteome.4c01068\u003c/span\u003e\u003cspan address=\"10.1021/acs.jproteome.4c01068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antibiotics, CRPA, Data-Independent Acquisition, Mass spectrometry, resistance mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-9267285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9267285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe excessive use of antibiotics has promoted the global spread of a resistant bacterial strains leading to a silent pandemic. Drug-resistant infections are currently responsible for approximately 700,000 deaths annually, a number projected to rise to 10\u0026nbsp;million by 2050 if effective containment strategies are not implemented. In this context, carbapenem-resistant \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (CRPA) represents a serious public health threat due to its ability to escape multiple classes of antibiotics, including those of the latest generation.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eIn this work, a mass spectrometry-based proteomics approach relying on Data-Independent Acquisition (DIA) was applied to compare 16 CRPA and 18 carbapenem-sensitive of \u003cem\u003eP. aeruginosa\u003c/em\u003e clinical isolates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4,227 proteins were quantified representing the highest number of identified proteins reported to date in clinical isolates of \u003cem\u003eP. aeruginosa.\u003c/em\u003e Among these, 26 proteins exhibited significant differential expression between two groups. Briefly, CRPA isolates showed increased expression of proteins implicated in the metabolism of phosphate (e.g. pstC, pstS, pstA, phoB, phoX), as well as those related to sugar, glycerol, amino acids metabolism. Up-regulation of transport-associated proteins, including ABC transport and OprO porin, was also observed. Conversely, CRPA showed marked downregulation of OprD a porin associated with reduced- \u0026szlig;-lactam permeability. Some of the regulated proteins have not yet been characterized, underscoring the significant work that remains to fully elucidate the molecular dynamics underlying resistance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe deep proteome coverage enabled by DIA provides valuable insights into resistance-associated pathways, thus supporting to surveillance of multidrug-resistant infections and promoting the development of more effective therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Deciphering Carbapenem-Resistance Mechanisms by DIA Proteomics Analysis in clinical isolates of Pseudomonas aeruginosa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 17:27:50","doi":"10.21203/rs.3.rs-9267285/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"184219175104027720686284487088534382963","date":"2026-05-05T18:59:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238774645111513707645507279517841299815","date":"2026-05-05T18:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T18:57:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T07:37:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T07:36:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Proteomics","date":"2026-03-30T12:50:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb550ffa-fa15-42d8-8a9e-d8cdcd4c4651","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"184219175104027720686284487088534382963","date":"2026-05-05T18:59:52+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"238774645111513707645507279517841299815","date":"2026-05-05T18:59:49+00:00","index":25,"fulltext":""},{"type":"reviewersInvited","content":"3","date":"2026-05-05T18:57:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T17:27:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 17:27:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9267285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9267285","identity":"rs-9267285","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}