- 1Laboratory of Microbiology and Biobank, National Institute for Infectious Diseases “Lazzaro Spallanzani” IRCCS, Rome, Italy
- 2Biomechanics Research Unit, GIGA Institute, University of Liège, Liège, Belgium
- 3Bioinformatics Research Unit in Infectious Diseases, National Institute for Infectious Diseases “Lazzaro Spallanzani” IRCCS, Rome, Italy
- 4Department of Experimental Medicine, University of Rome “Tor Vergata”, Rome, Italy
- 5Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
- 6Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
- 7Intensive Care Unit, National Institute for Infectious Diseases “Lazzaro Spallanzani” IRCCS, Rome, Italy
Introduction: Understanding host-pathogen interactions is crucial for explaining the variability in sepsis outcomes, with Pseudomonas aeruginosa (PA) remaining a significant public health concern. In this work, we explored PA-human host interaction mechanisms through a data integration workflow, focusing on protein-protein and metabolite-protein interactions, along with pathway modulation in affected organs during severe infections.
Methods: A scoping literature review enabled us to construct a domain-based infection network encompassing pathogenesis concepts, molecular interactions, and host response signatures, providing a wide view of the relevant mechanisms involved in severe bacterial infections.
Results: Our analysis yielded a literature-based comprehensive description of PA infection mechanisms and an annotated dataset of 189 PA-human interactions involving 151 proteins/molecules (109 human proteins, 3 human metabolites, 34 PA proteins, and 5 PA molecules). This dataset was complemented with gene expression analysis from in vivo PA-infected lung samples. The results indicated a notable overexpression of proinflammatory pathways and PA-mediated modulation of host lung responses.
Discussion: Our comprehensive molecular network of PA infection represents a valuable tool for the understanding of severe bacterial infections and offers potential applications in predicting clinical phenotypes. Through this approach combining omics data, clinical information, and pathogen characteristics, we have provided a foundation for future research in host-pathogen interactions and the mechanistic grounds to build dynamic computational models for clinical phenotype predictions.
Introduction
Sepsis caused by multi-drug resistant pathogens remains a leading cause of mortality in intensive care units (ICU) and represents a significant public health concern (1, 2). While it is established that microbial infection outcomes depend heavily on host conditions and spatial interactions between microbes, hosts, and other microorganisms (3, 4), many molecular details of these complex relationships remain unexplored. Pseudomonas aeruginosa (PA) is one of the most common pathogens for nosocomial infections, and, along with Acinetobacter baumannii and Enterobacterales resistant to carbapenems, it was listed among critical priority pathogens for World Health Organization (5, 6). The European Centre for Disease Prevention and Control (ECDC) has included PA in its antimicrobial resistance surveillance program (7). As an opportunistic human pathogen particularly affecting Cystic Fibrosis (CF) patients, PA’s clinical significance stems from multiple drug resistance mechanisms, numerous virulence factors, and biofilm production capabilities, enhancing its infection and host colonization potential (8). Recently, computational approaches have aided in unraveling mechanistic insights of PA infections. A network-assisted experiment allowed the identification of novel genes for virulence and antibiotic resistance, confirmed through experimental validation, showing cross-resistance against multiple drugs due to the same genes (9). In another effort, a real-time deep-learning model was applied to sepsis patients aiming to estimate prognostic outcomes from early infection phases (10). The model addressed baseline acuity, comorbidities, seasonal effects, and secular trends over time, unraveling the strategic significance of computational modeling to improve the clinical outcomes in sepsis patients.
Mechanistic computational modeling, omics data analysis, and clinical research have emerged as crucial tools for bridging the gap between conceptual models and clinical practice in infectious diseases (11, 12). By structuring key pathophysiological mechanisms and identifying conceptual domains, molecular diagrams provide novel insights into biomedical knowledge (11, 13, 14). The value of network-based exploratory and molecular virus-host interactome approaches was particularly evident during the COVID-19 pandemic, where rapid identification of molecular interactions between SARS-CoV-2 and human hosts became crucial to explain the clinical manifestations (15–19), as well as enabled a timely drug repurposing (20, 21). In this context, the resulting molecular maps of disease mechanisms (e.g., a Disease Map)1 provided biological meaning to apparently unrelated interactions, facilitating the mechanistic understanding of complex disease processes (22, 23). Following this paradigm, we applied similar strategies to bacterial pathogens such as PA, to uncover actionable insights about complex host interactions in severe systemic infections.
Our study presents a data integration workflow to build a molecular map of interaction between PA and human hosts in severe infection. Through extensive literature review, data curation, and gene expression meta-analysis, we have documented PA infection pathogenic mechanisms, direct protein-protein interactions (PPI), metabolite-protein interactions (MPI), and pathway activations in affected organs, organizing these findings into three conceptual domains: “cellular interaction level”, “tissue interaction level”, and “organ interaction level”.
Materials and methods
Scoping review
We conducted independent literature reviews compliant with international reference guidelines for scoping reviews (24). For each domain, the scoping review outcomes were processed to identify features of PA interactions with the host and the direct or indirect effects that they cause within the host itself.
Using a structured search string in PubMed (Supplementary Text 1), we identified 532 articles after excluding duplicates, non-English publications, and studies not addressing systemic infection or host-pathogen interactions. We supplemented this with 27 additional articles focusing on host response to PA infection in both mouse models and human patients through omics data analysis. During the review process, papers were evaluated in three sequential inclusion criteria: (i) title relevance; (ii) abstract consisting of three conceptual domains, and (iii) identification of specific pathogenic mechanisms in PA infection through full-text analysis. The final selection comprised 150 articles which were categorized into three interaction levels: (1) “cell interaction level”; (2) “tissue interaction level”; and (3) “organ interaction level”. Full-text articles were evaluated by the curators to define the best possible conceptual domains, following the reference methodology (PRISMA-ScR) for the assessment (25, 26). Each article selected for review was independently read and evaluated by two reviewers. At the end of the evaluation, the data results were discussed and evaluated in a specific meeting of the entire working group. Each article was assigned a unique reference ID (SR) and documented in Supplementary Table 1.
Conceptual domains
First, we identified the conceptual domains that organize the information obtained from the literature, providing a hierarchical model of host-pathogen interaction, following a previous experience on mapping host-pathogen interactions in the COVID-19 Disease Map project (11, 12). Three interaction levels within the host’s system were identified: cell, tissue, and organ. For each level, we further identified conceptual domains, describing the interactions with the pathogen (Figure 1). A comprehensive description of all mechanisms and PA-human interactions, along with search string, containing all search terms used in the scoping review section on PubMed, and protein abbreviation were reported in Supplementary Text 1, while a summary can be found below in the results section.

Figure 1. Structure of data collection and analysis workflow. For each level, we identified further conceptual domains, describing the interactions with PA molecules.
Molecular interaction dataset and human host - PA interactome
We documented PPI and MPI between PA and humans. All interaction details, including type, Uniprot ID, literature reference, and subdomains of the model, were compiled in the curated dataset (Supplementary Table 2). We constructed a network-based interaction model by exploring PA-host data gathered from the scoping review, following methodology established for SARS-CoV-2-human host interactions (16, 17). Human PPI data was retrieved using R packages PSICQUIC and biomaRt (27, 28), resulting in a comprehensive large network of 13,334 nodes and 73,584 interactions that included PA-human host interactions. The mechanisms of infection were estimated using the Random Walk with Restart (RWR) algorithm (29), using each PA protein as a seed and limiting the output to the 200 closest host proteins per PA protein. Network visualizations were generated using GEPHI 0.9.2 (30). Gene set enrichment analysis (GSEA) was performed using the R package enrichR (31), testing against Reactome 2022, KEGG 2021 and WikiPathways 2023 human pathways databases (32–34).
Meta-analysis of the whole transcriptome from animal model of PA-induced sepsis
We performed a meta-analysis of gene expression in mouse lung samples comparing PA-infected tissues with healthy controls using data from two projects. The first dataset comprised 12 bulk RNAseq samples from PA-infected lung tissues (PRJNA975462; GEO: GSE233206, SRA Study SRP439193) (35), while the second included 6 bulk gene expression samples from acute and chronic PA pulmonary infection (PRJNA793679; GEO: GSE192890, SRA Study SRP353174) (36). SRA data was processed using Prefetch and converted to FASTQ files using the fastq-dump tool from the SRA Toolkit software v2.11.0 (37, 38). Reads were aligned to the mm10 mouse reference genome using HISAT2 (39). Differentially expressed genes (DEGs) were identified using DESeq2 v.1.42.1 in R version 3.4.3 (40), with thresholds set at Log2FC > |1| and Benjamin-Hochberg False Discovery Rate < 5% (BH-FDR). To account for batch effects between laboratories, we conducted a meta-analysis using metaRNASeq R packages, combining p-values from the two independent RNA-seq experiments using Fisher methods (41). The analysis focused on 21,010 genes shared between datasets, generating combined BH-adjusted p-values and average Log2FC values. Genes meeting the thresholds of Log2FC > |1| and BH FDR < 5% were classified as DEGs.
Gene enrichment on DEGs in PA infection and healthy conditions
To deliver biological meaning from the data, we performed a gene enrichment analysis using Reactome, KEGG, and WikiPathways (32–34). The enrichR R package was used to conduct gene set enrichment analysis, with significance assessed through Fisher exact test (p-value) and false discovery rate (q-value: adjusted p-value for FDR) (31).
Results
Domain-based analysis of PA-human host interactions reveals detailed pathogenic mechanisms
To understand in detail PA infection pathogenic mechanisms, we reported many PA-human host interactions mechanisms, organizing them into three conceptual domains: cellular, tissue, and organ-level interactions.
At the cellular level, four key aspects characterize PA-host interaction: (i) bacterial adhesion/colonization (PA-Ad); (ii) bacterial invasion and innate immune response of the host (PA-In); (iii) PA exotoxins activity in infection (PA-Ex); (iv) bacterial metabolic mechanisms (PA-Met). The pathogenic mechanisms in PA infection were assigned to each domain (Table 1). PA initiates infection through flagellum and type IV pili adherence, interacting with MUC1 ectodomains via NEU1 modulation (42, 43). The bacterium employs multiple adhesion strategies, including biofilm formation, psl adhesins (44, 45), and various receptors binding to extracellular matrix components (46, 47). During invasion, PA modifies host cell membranes through PI3K/PIP3/Akt pathway activation and uses specialized proteins like pilY1 for binding (48). The bacterium’s survival in macrophages relies on mgtC and oprF (49). The exotoxin family (exoS, exoT, exoU, exoY, exoA) facilitates pathogenesis through various mechanisms, including protein ribosylation, cytoskeleton modification, and membrane disruption (50–53).

Table 1. The table summarizes the main pathogenic mechanisms in PA infection for each domain, with comprehensive conceptual analysis provided in the Supplementary Text 1: (A) cell interaction level; (B) tissue interaction level; (C) organ interaction level. This structured approach enabled us to characterize specific mechanisms and experimental models of PA infections.
At the tissue level, PA affects three primary domains: (i) endothelial tissue (Endothelial Tissue - EnT); (ii) lower airway and alveolar epithelial tissue in the lung, including CF conditions (Airway Epithelial Tissue - AET); and (iii) other epithelial tissues such as desquamated bronchial and urinary epithelia (Other Epithelial Tissues - ETs). In endothelial tissue, particularly during severe infection, APOE exhibits antibacterial activity (54), while T3SS affects actin cytoskeleton dynamics (55). The bacterium adapts to blood survival by regulating metabolic pathways and virulence factors (56, 57). In airway epithelial tissue, particularly relevant in CF conditions, PA flagella binds to asialoGM1 and MUC1, triggering inflammatory responses (43, 58, 59). CFTR plays a crucial role in PA uptake and inflammation (60, 61). In other epithelial tissues, PA binds through HSPGs and N-glycans (62), with quorum sensing molecules affecting barrier integrity (63).
Finally, at the organ level, PA infection primarily impacts the lung and bloodstream. In lung infections, particularly in CF, PA causes intense inflammation with neutrophil infiltration and cytokine production, inducing changes in immune cell composition (36, 59, 64). The infection involves various immune mechanisms, including TRPV4 (65), TIM3/Gal-9 signaling (64), and NET formation (66). In bloodstream infections, PA induces differential immune cell responses and affects the vascular endothelium through multiple mechanisms, such as TREM-1 (67–69). The Hxu system contributes significantly to bloodstream infection capability (70). These multi-level interactions highlight the complexity of PA pathogenesis and its adaptive capabilities in different host environments.
PA-host proteins interaction network reveals key mechanisms modulated in humans by PA severe infection
To reveal key molecular mechanisms in PA severe infection, we collected the molecular interactions between PA and human proteins during different infection stages, which were manually curated. Analysis of 92 articles revealed multiple direct protein-protein interactions (PPI) and molecule-protein interactions (MPI), detailed in Supplementary Table 2 and annotated with Uniprot IDs, references, and model subdomains.
We identified 151 molecules: 109 human proteins, 3 human metabolites (Gangliotetraosylceramide, Phospholipid cell membrane, glycosphingolipid globotriaosylceramide), 34 PA proteins, and 5 PA molecules (3O-C12-HSL, LipidA, LPS, Exopolysaccharide, Pyocyanin), yielding 189 PA-human interactions and 7 human-human interactions. Note that the 189 interactions include multiple events involving the same molecules, while the 151 components represent unique entities within the network.
These interactions were categorized into four cellular domains: Adhesion process (PA-Ad), invasion and injury of tissue (PA-Inv), exotoxin production (PA-Ex) and bacterial metabolism (PA-Meta).
Gene enrichment analysis revealed significant pathway associations across Reactome, WikiPathways and KEGG (Supplementary Table 3). Notable enrichments included the “Pathogenic Escherichia coli Infection WP2272” pathway (WikiPathways) and “Pertussis” (KEGG) with FDR < 0.0001%. Reactome analysis highlighted three significant pathways (FDR < 0.0001%), including Programmed Cell Death R-HSA- 5357801, Toll-like Receptor Cascades R-HSA-168898, and Signaling by Interleukins R-HSA-449147. In these pathways several key proteins (e.g., exoS and exoT) would play a modulating role, such as inhibition of interleukin proteins or degradation of occludin (OCLN), a cell death regulator (109).
A full network of interactions between PA and human host proteins (Figure 2) enabled us to reveal the overall cell response to infection, digging up also new possible pathogenic mechanisms: the modulating effect of outer membrane proteins oprH, oprQ, and the elastase lasB on Complement Cascade Pathway (Reactome R-HSA-166658; 18/55; FDR < 0.0001%) for contrasting bacterial cell damage. These proteins also showed significant interactions with blood clotting factors, such as VWF, SERPINF2, PLAUR, PLAT, and PLG (Complement and Coagulation Cascade WP558; 20/58; FDR < 0.0001%), suggesting a potential involvement in thrombotic event. Furthermore, the role of exotoxin (exoS, exoY, and exoT) in PA infection proved central to triggering of cell toxicity through interactions with cytoplasmic 14-3-3 proteins (e.g., YWHAB).

Figure 2. Network of PA-human host molecular interactions, with the top 200 nearest proteins found by the Random Walk with Restart (RWR) algorithm. Nodes have different colors to show different kinds of molecules: purple, human proteins; green, PA proteins; light blue, PA molecules; orange, human proteins belonging to the complement pathway.
Meta-analysis of whole transcriptome of PA-infected lung tissues from mice reveals selective modulation of pro-inflammatory pathways
To better define the biological response in PA-infected lung tissues, we carried out a meta-analysis of gene expression of two bulk RNAseq datasets (GSE233206 and GSE192890) comparing PA-infected mice lung samples with healthy controls. Our meta-analysis identified 1,560 upregulated and 383 downregulated genes (Log2FC > 1; FDR BH < 5%, Supplementary Table 4). Pathway analysis of upregulated genes using WikiPathways revealed significant enrichment in inflammation-related pathways, notably “Overview of Proinflammatory and Profibrotic Mediators WP5095” (39/129, FDR < 0.0001%). Reactome analysis aligned with our scoping review findings, highlighting significant enrichment (FDR < 0.000001%) in key pathways: Cytokine Signaling in Immune System R-HSA-1280215 (145/702), Signaling by Interleukins R-HSA-449147 (109/453), Interleukin-10 Signaling R-HSA-6783783 (31/45) (Figures 3a, b). Proinflammatory pathways were found nested into Interleukins R-HSA-449147 (Homo sapiens) Reactome’s entry (Interleukin-2 family signaling R-HSA-451927; Interleukin-3, Interleukin-5 and GM-CSF signaling R-HSA-512988; Interferon alpha/beta signaling R-HSA-909733; Interferon gamma signaling R-HSA-877300; ISG15 antiviral mechanism (Homo sapiens) R-HSA-1169408; PKR-mediated signaling R-HSA-9833482; TNFR2 non-canonical NF-kB pathway R-HSA-5668541; Signaling by CSF1 (M-CSF) in myeloid cells; R-HSA-9680350. All these pathways have many key proteins for PA infection, which are described as targets for PA exoU, exoS, azu, lasB, aprA, oprF, pilA, and LPS. These results suggest that these pathways are directly involved in initiating the innate response to PA infection, but also highlight the potential role of PA molecules in modulating and limiting this response, particularly for interleukin signaling.

Figure 3. GSEA with WikiPathways (a) and Reactome (b), based on upregulated DEGs in PA - infected samples, obtained from meta-analysis of two infection experiments in mouse lung tissues.
Discussion
In this work, we present the development of a comprehensive data integration model to understand PA infection through detailed exploration of the literature and metanalysis of transcriptomics datasets, identifying specific human molecular targets for each PA molecule, pathogenic mechanisms, and host responses. In general, PA could be considered a useful example for studying severe systemic infections, given its multi-drug resistance capabilities, ability to cause acute and chronic infections in pulmonary disease patients, and its capacity to form biofilm in hypoxic conditions, which makes it extremely difficult to treat (110, 111).
Firstly, the central role of exoS during infection was confirmed, while enhanced activity among exo family proteins, including exoY and exoT, was widely highlighted (112). ExoS functions by inhibiting several proteins of interleukin pathways and inducing the degradation of Occludin (OCLN), an integral membrane protein involved in cytokine-induced regulation of the tight junction permeability barrier, ultimately inducing cell death (67). Through its ADP RT activity, exoS modulates host cell apoptosis, inducing PA-infected cell death by targeting various Ras proteins (113). The Complement Cascade Pathway undergoes modulation by PA’s outer membrane proteins, oprH, oprQ, and elastase lasB, which trigger cytotoxic effects and adhesion through complement binding, particularly C3 (114). This result mirrors the mechanism of activation of the complement system, in which C3 is the main actor against bacteria, through a link with oprF, a porin involved in ion transport (Na+ and Cl−) and anaerobic biofilm production (115, 116). A significant finding was the interaction between oprH, oprQ, and lasB with coagulation proteins, suggesting their involvement in thrombotic processes. PA lasB’s cleavage of a C-terminal peptide FYT21 derived from thrombin inhibits activation of the transcription factors NFκ-B and activator protein 1 (AP-1). PA demonstrates sophisticated modulation of host immune responses through multiple pathways; aprA, lasB, and exoS exhibit inhibitory effects on interleukin pathways (112, 117, 118), indicating an adaptive modulation that enhances PA survival within the host. Such an effect was confirmed in PA infection, where PA-derived DnaK negatively regulates IL-1β production by cross-talk between JNK and PI3K/PDK1/FoxO1 pathways (119). Notably, decreased PA levels in CF patients correlate with reduced proinflammatory cytokines (120).
Our findings provided a broader view of molecular perturbations in PA systemic infection and served as a foundation for developing specific disease maps for severe PA infection, supporting the integration of omics data from clinical cases into predictive computational models. Future developments may incorporate text mining and AI-assisted analysis for drug target identification (23) and digital modeling of the human immune system under infection conditions (121) to better predict real patient outcomes and test potential therapeutic strategies in a personalized fashion.
There are some limitations worth noting. While we have documented numerous significant PA-human interactions, our model may not encompass all possible interactions. The PPI/MPI dataset requires iterative updates to incorporate new experimental findings from both in vitro, in vivo and clinical studies. Furthermore, since our interaction data derives primarily from in vitro experiments, the described pathogenic mechanisms require validation in the context of severe systemic infections. Finally, our differential expression meta-analysis, conducted in mouse models with limited sample size, provides an overview of host gene-expression signatures in PA infection but requires confirmation through clinical data.
In conclusion, our study provides a comprehensive collection and analysis of molecular mechanisms in P. aeruginosa infection, combining literature-based evidence, protein-protein interaction analysis, and transcriptomic data from in vivo studies. A detailed dataset of PA-host interactions across cellular, tissue, and organ levels was built through a systematic data integration approach. Our findings highlight the complex interplay between PA virulence factors and host responses, particularly the role of exoS in modulating interleukin pathways and the involvement of outer membrane proteins in the complement cascade. The integration of differential expression analysis from mouse models further strengthens our understanding of host response patterns, particularly in proinflammatory and immune signaling pathways. As antimicrobial resistance continues to pose significant challenges in healthcare, such a comprehensive molecular understanding may prove invaluable for applying precision medicine approaches to severe bacterial infections and improving patient-tailored treatments in severe systemic infections.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
Author contributions
FM: Conceptualization, Data curation, Investigation, Software, Supervision, Writing – original draft, Writing – review & editing. CR: Data curation, Formal analysis, Methodology, Validation, Writing – review & editing. LL: Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. SC: Data curation, Methodology, Software, Writing – review & editing. MP: Data curation, Formal analysis, Methodology, Writing – review & editing. VD: Data curation, Formal analysis, Methodology, Writing – review & editing. BR: Data curation, Formal analysis, Methodology, Writing – review & editing. BS: Formal analysis, Supervision, Validation, Writing – review & editing. GC: Formal analysis, Methodology, Validation, Writing – review & editing. LG: Funding acquisition, Resources, Supervision, Writing – review & editing. MB: Formal analysis, Supervision, Validation, Writing – review & editing. CF: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants from the Italian Ministry of Health through “Ricerca Corrente” Linea 3 Project 2 and “5 per 1000–2021” grant of the Italian Ministry of Health (grant no. 5M-2021-23683787) (FM) and the European Commission with the HORIZON program BY-COVID (grant no. 101046203–BY-COVID). Moreover, the authors acknowledge funding from the European Union’s Horizon 2020 research and innovation program via the European Research Council (ERC CoG INSITE 772418).
Acknowledgments
Figure 1 has been designed using resources from Flaticon.com.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2025.1600509/full#supplementary-material
Footnotes
References
1. Kumar NR, Balraj TA, Kempegowda SN, Prashant A. Multidrug-resistant sepsis: a critical healthcare challenge. Antibiotics. (2024) 13:46. doi: 10.3390/antibiotics13010046
2. Rello J, Valenzuela-Sanchez F, Ruiz-Rodriguez M, Moyano S. Sepsis: a review of advances in management. Adv Ther. (2017) 34:2393–411. doi: 10.1007/s12325-017-0622-8
3. Saarenpaa S, Shalev O, Ashkenazy H, Carlos V, Lundberg DS, Weigel D, et al. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat Biotechnol. (2023) 42:1384–93. doi: 10.1038/s41587-023-01979-2
4. Mu A, Klare WP, Baines SL, Ignatius Pang CN, Guerillot R, Harbison-Price N, et al. Integrative omics identifies conserved and pathogen-specific responses of sepsis-causing bacteria. Nat Commun. (2023) 14:1530. doi: 10.1038/s41467-023-37200-w
5. WHO. WHO Publishes list of Bacteria For Which New Antibiotics Are Urgently Needed. Basel: World Health Organization (2022).
6. WHO. Pathogens Prioritization: a Scientific Framework for Epidemic and Pandemic Research Preparedness. Basel: World Health Organization (2024).
8. Dahal S, Renz A, Drager A, Yang L. Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation. Commun Biol. (2023) 6:165. doi: 10.1038/s42003-023-04540-8
9. Hwang S, Kim CY, Ji SG, Go J, Kim H, Yang S, et al. Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa. Sci Rep. (2016) 6:26223. doi: 10.1038/srep26223
10. Boussina A, Shashikumar SP, Malhotra A, Owens RL, El-Kareh R, Longhurst CA, et al. Impact of a deep learning sepsis prediction model on quality of care and survival. NPJ Digit Med. (2024) 7:14. doi: 10.1038/s41746-023-00986-6
11. Singh V, Naldi A, Soliman S, Niarakis A. A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint. NPJ Syst Biol Appl. (2023) 9:33. doi: 10.1038/s41540-023-00294-5
12. Montaldo C, Messina F, Abbate I, Antonioli M, Bordoni V, Aiello A, et al. Multi-omics approach to COVID-19: a domain-based literature review. J Transl Med. (2021) 19:501. doi: 10.1186/s12967-021-03168-8
13. Hemedan AA, Niarakis A, Schneider R, Ostaszewski M. Boolean modelling as a logic-based dynamic approach in systems medicine. Comput Struct Biotechnol J. (2022) 20:3161–72. doi: 10.1016/j.csbj.2022.06.035
14. Ostaszewski M, Mazein A, Gillespie ME, Kuperstein I, Niarakis A, Hermjakob H, et al. COVID-19 disease map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Sci Data. (2020) 7:e10136. doi: 10.1038/s41597-020-0477-8
15. Steiner S, Kratzel A, Barut GT, Lang RM, Aguiar Moreira E, Thomann L, et al. SARS-CoV-2 biology and host interactions. Nat Rev Microbiol. (2024) 22:206–25. doi: 10.1038/s41579-023-01003-z
16. Messina F, Giombini E, Agrati C, Vairo F, Ascoli Bartoli T, Al Moghazi S, et al. COVID-19: viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. J Transl Med. (2020) 18:233. doi: 10.1186/s12967-020-02405-w
17. Messina F, Giombini E, Montaldo C, Sharma AA, Zoccoli A, Sekaly RP, et al. Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2- host interactome. Cell Death Dis. (2021) 12:788. doi: 10.1038/s41419-021-03881-8
18. Schmidt N, Lareau CA, Keshishian H, Ganskih S, Schneider C, Hennig T, et al. The SARS-CoV-2 RNA-protein interactome in infected human cells. Nat Microbiol. (2021) 6:339–53. doi: 10.1038/s41564-020-00846-z
19. Lee S, Lee YS, Choi Y, Son A, Park Y, Lee KM, et al. The SARS-CoV-2 RNA interactome. Mol Cell. (2021) 81: 2838–50.e6. doi: 10.1016/j.molcel.2021.04.022
20. Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. (2020) 583:459–68. doi: 10.1038/s41586-020-2286-9
21. Zhou Y, Liu Y, Gupta S, Paramo MI, Hou Y, Mao C, et al. A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets. Nat Biotechnol. (2023) 41:128–39. doi: 10.1038/s41587-022-01474-0
22. Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta-Resendiz A, et al. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol (2021) 17:e10387. doi: 10.15252/msb.202110387
23. Niarakis A, Ostaszewski M, Mazein A, Kuperstein I, Kutmon M, Gillespie ME, et al. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol. (2023) 14:1282859. doi: 10.3389/fimmu.2023.1282859
24. Peters MD, Godfrey CM, Khalil H, Mcinerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. (2015) 13:141–6. doi: 10.1097/XEB.0000000000000050
25. Peterson JW. Bacterial pathogenesis. 4th ed. In: S Baron editor. Medical Microbiology. Galveston, TX: University of Texas Medical Branch at Galveston (1996).
26. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. (2018) 169:467–73. doi: 10.7326/M18-0850
27. Aranda B, Blankenburg H, Kerrien S, Brinkman FS, Ceol A, Chautard E, et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat Methods. (2011) 8:528–9. doi: 10.1038/nmeth.1637
28. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, et al. BioMart–biological queries made easy. BMC Genomics. (2009) 10:22. doi: 10.1186/1471-2164-10-22
29. Valdeolivas A, Tichit L, Navarro C, Perrin S, Odelin G, Levy N, et al. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics. (2019) 35:497–505. doi: 10.1093/bioinformatics/bty637
30. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the International AAAI Conference on Web and Social Media. (2009) p. 361–362. doi: 10.1609/icwsm.v3i1.13937
31. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. (2016) 44:W90–7. doi: 10.1093/nar/gkw377
32. Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, et al. The reactome pathway knowledgebase 2024. Nucleic Acids Res. (2024) 52:D672–8. doi: 10.1093/nar/gkz1031
33. Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, et al. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. (2018) 46:D661–7. doi: 10.1093/nar/gkx1064
34. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. (2017) 45:D353–61. doi: 10.1093/nar/gkw1092
35. Yang Y, Ma T, Zhang J, Tang Y, Tang M, Zou C, et al. An integrated multi-omics analysis of identifies distinct molecular characteristics in pulmonary infections of Pseudomonas aeruginosa. PLoS Pathog. (2023) 19:e1011570. doi: 10.1371/journal.ppat.1011570
36. Hu X, Wu M, Ma T, Zhang Y, Zou C, Wang R, et al. Single-cell transcriptomics reveals distinct cell response between acute and chronic pulmonary infection of Pseudomonas aeruginosa. MedComm. (2022) 3:e193. doi: 10.1002/mco2.193
37. Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. (2011) 39:D19–21. doi: 10.1093/nar/gkq1019
38. Han Z, Hua J, Xue W, Zhu F. Integrating the ribonucleic acid sequencing data from various studies for exploring the multiple sclerosis-related long noncoding ribonucleic acids and their functions. Front Genet. (2019) 10:1136. doi: 10.3389/fgene.2019.01136
39. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. (2015) 12:357–60. doi: 10.1038/nmeth.3317
40. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. (2014) 15:550. doi: 10.1186/s13059-014-0550-8
41. Rau A, Marot G, Jaffrezic F. Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics. (2014) 15:91. doi: 10.1186/1471-2105-15-91
42. Hyun SW, Liu A, Liu Z, Cross AS, Verceles AC, Magesh S, et al. The NEU1-selective sialidase inhibitor, C9- butyl-amide-DANA, blocks sialidase activity and NEU1-mediated bioactivities in human lung in vitro and murine lung in vivo. Glycobiology. (2016) 26:834–49. doi: 10.1093/glycob/cww060
43. Hyun SW, Imamura A, Ishida H, Piepenbrink KH, Goldblum SE, Lillehoj EP. The sialidase NEU1 directly interacts with the juxtamembranous segment of the cytoplasmic domain of mucin-1 to inhibit downstream PI3K-Akt signaling. J Biol Chem. (2021) 297:101337. doi: 10.1016/j.jbc.2021.101337
44. Kaya E, Grassi L, Benedetti A, Maisetta G, Pileggi C, Di Luca M, et al. In vitro interaction of Pseudomonas aeruginosa biofilms with human peripheral blood mononuclear cells. Front Cell Infect Microbiol. (2020) 10:187. doi: 10.3389/fcimb.2020.00187
45. Byrd MS, Pang B, Mishra M, Swords WE, Wozniak DJ. The Pseudomonas aeruginosa exopolysaccharide Psl facilitates surface adherence and NF-kappaB activation in A549 cells. mBio. (2010) 1:e140–110. doi: 10.1128/mBio.00140-10
46. Paulsson M, Su YC, Ringwood T, Udden F, Riesbeck K. Pseudomonas aeruginosa uses multiple receptors for adherence to laminin during infection of the respiratory tract and skin wounds. Sci Rep. (2019) 9:18168. doi: 10.1038/s41598-019-54622-z
47. Beaufort N, Corvazier E, Mlanaoindrou S, De Bentzmann S, Pidard D. Disruption of the endothelial barrier by proteases from the bacterial pathogen Pseudomonas aeruginosa: implication of matrilysis and receptor cleavage. PLoS One (2013) 8:e75708. doi: 10.1371/journal.pone.0075708
48. Engel J, Eran Y. Subversion of mucosal barrier polarity by pseudomonas aeruginosa. Front Microbiol. (2011) 2:114. doi: 10.3389/fmicb.2011.00114
49. Garai P, Berry L, Moussouni M, Bleves S, Blanc-Potard AB. Killing from the inside: Intracellular role of T3SS in the fate of Pseudomonas aeruginosa within macrophages revealed by mgtC and oprF mutants. PLoS Pathog. (2019) 15:e1007812. doi: 10.1371/journal.ppat.1007812
50. Henriksson ML, Rosqvist R, Telepnev M, Wolf-Watz H, Hallberg B. Ras effector pathway activation by epidermal growth factor is inhibited in vivo by exoenzyme S ADP- ribosylation of Ras. Biochem J. (2000) 347:217–22.
51. Ghatak S, Hemann C, Boslett J, Singh K, Sharma A, El Masry MS, et al. Bacterial pyocyanin inducible keratin 6A accelerates closure of epithelial defect under conditions of mitochondrial dysfunction. J Invest Dermatol. (2023) 143:2052–64.e5. doi: 10.1016/j.jid.2023.03.1671
52. Jia J, Alaoui-El-Azher M, Chow M, Chambers TC, Baker H, Jin S. c-Jun NH2- terminal kinase-mediated signaling is essential for Pseudomonas aeruginosa ExoS-induced apoptosis. Infect Immun. (2003) 71:3361–70. doi: 10.1128/IAI.71.6.3361-3370.2003
53. Krall R, Schmidt G, Aktories K, Barbieri JT. Pseudomonas aeruginosa ExoT is a Rho GTPase-activating protein. Infect Immun. (2000) 68:6066–8. doi: 10.1128/IAI.68.10.6066-6068.2000
54. Puthia M, Marzinek JK, Petruk G, Erturk Bergdahl G, Bond PJ, Petrlova J. Antibacterial and anti-inflammatory effects of apolipoprotein E. Biomedicines. (2022) 10:1430. doi: 10.3390/biomedicines10061430
55. Huber P, Bouillot S, Elsen S, Attree I. Sequential inactivation of Rho GTPases and Lim kinase by Pseudomonas aeruginosa toxins ExoS and ExoT leads to endothelial monolayer breakdown. Cell Mol Life Sci. (2014) 71:1927–41. doi: 10.1007/s00018-013-1451-9
56. Turner KH, Everett J, Trivedi U, Rumbaugh KP, Whiteley M. Requirements for Pseudomonas aeruginosa acute burn and chronic surgical wound infection. PLoS Genet. (2014) 10:e1004518. doi: 10.1371/journal.pgen.1004518
57. Elmassry MM, Mudaliar NS, Kottapalli KR, Dissanaike S, Griswold JA, San Francisco MJ, et al. Pseudomonas aeruginosa alters its transcriptome related to carbon metabolism and virulence as a possible survival strategy in blood from trauma patients. mSystems. (2019) 4:e312–8. doi: 10.1128/mSystems.00312-18
58. Adamo R, Sokol S, Soong G, Gomez MI, Prince A. Pseudomonas aeruginosa flagella activate airway epithelial cells through asialoGM1 and toll-like receptor 2 as well as toll-like receptor 5. Am J Respir Cell Mol Biol. (2004) 30:627–34. doi: 10.1165/rcmb.2003-0260OC
59. Blohmke CJ, Park J, Hirschfeld AF, Victor RE, Schneiderman J, Stefanowicz D, et al. TLR5 as an anti-inflammatory target and modifier gene in cystic fibrosis. J Immunol. (2010) 185:7731–8. doi: 10.4049/jimmunol.1001513
60. Haenisch MD, Ciche TA, Luckie DB. Pseudomonas or LPS exposure alters CFTR iodide efflux in 2WT2 epithelial cells with time and dose dependence. Biochem Biophys Res Commun. (2010) 394:1087–92. doi: 10.1016/j.bbrc.2010.03.131
61. Schroeder TH, Lee MM, Yacono PW, Cannon CL, Gerceker AA, Golan DE, et al. CFTR is a pattern recognition molecule that extracts Pseudomonas aeruginosa LPS from the outer membrane into epithelial cells and activates NF-kappa B translocation. Proc Natl Acad Sci U S A. (2002) 99:6907–12. doi: 10.1073/pnas.092160899
62. Bucior I, Pielage JF, Engel JN. Pseudomonas aeruginosa pili and flagella mediate distinct binding and signaling events at the apical and basolateral surface of airway epithelium. PLoS Pathog. (2012) 8:e1002616. doi: 10.1371/journal.ppat.1002616
63. Vikstrom E, Bui L, Konradsson P, Magnusson KE. The junctional integrity of epithelial cells is modulated by Pseudomonas aeruginosa quorum sensing molecule through phosphorylation-dependent mechanisms. Exp Cell Res. (2009) 315:313–26. doi: 10.1016/j.yexcr.2008.10.044
64. Vega-Carrascal I, Bergin DA, Mcelvaney OJ, Mccarthy C, Banville N, Pohl K, et al. Galectin-9 signaling through TIM-3 is involved in neutrophil-mediated Gram-negative bacterial killing: an effect abrogated within the cystic fibrosis lung. J Immunol. (2014) 192:2418–31. doi: 10.4049/jimmunol.1300711
65. Scheraga RG, Abraham S, Grove LM, Southern BD, Crish JF, Perelas A, et al. TRPV4 protects the lung from bacterial pneumonia via MAPK molecular pathway switching. J Immunol. (2020) 204:1310–21. doi: 10.4049/jimmunol.1901033
66. Sung PS, Peng YC, Yang SP, Chiu CH, Hsieh SL. CLEC5A is critical in Pseudomonas aeruginosa-induced NET formation and acute lung injury. JCI Insight. (2022) 7:e156613. doi: 10.1172/jci.insight.156613
67. Soong G, Parker D, Magargee M, Prince AS. The type III toxins of Pseudomonas aeruginosa disrupt epithelial barrier function. J Bacteriol. (2008) 190:2814–21. doi: 10.1128/JB.01567-07
68. Oelen R, De Vries DH, Brugge H, Gordon MG, Vochteloo M, Ye CJ, et al. Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure. Nat Commun. (2022) 13:3267. doi: 10.1038/s41467-022-30893-5
69. Gibot S, Jolly L, Lemarie J, Carrasco K, Derive M, Boufenzer A. Triggering receptor expressed on myeloid cells-1 inhibitor targeted to endothelium decreases cell activation. Front Immunol. (2019) 10:2314. doi: 10.3389/fimmu.2019.02314
70. Yang F, Zhou Y, Chen P, Cai Z, Yue Z, Jin Y, et al. High-level expression of cell-surface signaling system hxu enhances Pseudomonas aeruginosa bloodstream infection. Infect Immun. (2022) 90:e0032922. doi: 10.1128/iai.00329-22
71. Lory S, Ichikawa JK. Pseudomonas-epithelial cell interactions dissected with DNA microarrays. Chest. (2002) 121:36S–9S. doi: 10.1378/chest.121.3_suppl.36s
72. Tynan A, Mawhinney L, Armstrong ME, O’reilly C, Kennedy S, Caraher E, et al. Macrophage migration inhibitory factor enhances Pseudomonas aeruginosa biofilm formation, potentially contributing to cystic fibrosis pathogenesis. FASEB J. (2017) 31:5102–10. doi: 10.1096/fj.201700463R
73. Hsieh JC, Tham DM, Feng W, Huang F, Embaie S, Liu K, et al. Intranasal immunization strategy to impede pilin-mediated binding of Pseudomonas aeruginosa to airway epithelial cells. Infect Immun. (2005) 73:7705–17. doi: 10.1128/IAI.73.11.7705-7717.2005
74. Wong WY, Campbell AP, Mcinnes C, Sykes BD, Paranchych W, Irvin RT, et al. Structure-function analysis of the adherence-binding domain on the pilin of Pseudomonas aeruginosa strains PAK and KB7. Biochemistry. (1995) 34:12963–72. doi: 10.1021/bi00040a006
75. Brandel A, Aigal S, Lagies S, Schlimpert M, Melendez AV, Xu M, et al. The Gb3-enriched CD59/flotillin plasma membrane domain regulates host cell invasion by Pseudomonas aeruginosa. Cell Mol Life Sci. (2021) 78:3637–56. doi: 10.1007/s00018-021-03766-1
76. Demirdjian S, Hopkins D, Cumbal N, Lefort CT, Berwin B. Distinct contributions of CD18 integrins for binding and phagocytic internalization of Pseudomonas aeruginosa. Infect Immun. (2020) 88:e00011–20. doi: 10.1128/IAI.00011-20
77. Kato K, Lillehoj EP, Kim KC. Pseudomonas aeruginosa stimulates tyrosine phosphorylation of and TLR5 association with the MUC1 cytoplasmic tail through EGFR activation. Inflamm Res. (2016) 65:225–33. doi: 10.1007/s00011-015-0908-8
78. Bardoel BW, Van Kessel KP, Van Strijp JA, Milder FJ. Inhibition of Pseudomonas aeruginosa virulence: characterization of the AprA-AprI interface and species selectivity. J Mol Biol. (2012) 415:573–83. doi: 10.1016/j.jmb.2011.11.039
79. Imbert PR, Louche A, Luizet JB, Grandjean T, Bigot S, Wood TE, et al. A Pseudomonas aeruginosa TIR effector mediates immune evasion by targeting UBAP1 and TLR adaptors. EMBO J. (2017) 36:1869–87. doi: 10.15252/embj.201695343
80. Hickling TP, Sim RB, Malhotra R. Induction of TNF-alpha release from human buffy coat cells by Pseudomonas aeruginosa is reduced by lung surfactant protein A. FEBS Lett. (1998) 437:65–9. doi: 10.1016/s0014-5793(98)01200-9
81. Limoli DH, Rockel AB, Host KM, Jha A, Kopp BT, Hollis T, et al. Cationic antimicrobial peptides promote microbial mutagenesis and pathoadaptation in chronic infections. PLoS Pathog. (2014) 10:e1004083. doi: 10.1371/journal.ppat.1004083
82. Scott A, Weldon S, Buchanan PJ, Schock B, Ernst RK, Mcauley DF, et al. Evaluation of the ability of LL-37 to neutralise LPS in vitro and ex vivo. PLoS One. (2011) 6:e26525. doi: 10.1371/journal.pone.0026525
83. Barbey-Morel C, Perlmutter DH. Effect of pseudomonas elastase on human mononuclear phagocyte alpha 1-antitrypsin expression. Pediatr Res. (1991) 29:133–40. doi: 10.1203/00006450-199102000-00005
84. Song KS, Kim HJ, Kim K, Lee JG, Yoon JH. Regulator of G-protein signaling 4 suppresses LPS-induced MUC5AC overproduction in the airway. Am J Respir Cell Mol Biol. (2009) 41:40–9. doi: 10.1165/rcmb.2008-0280OC
85. Grassme H, Kirschnek S, Riethmueller J, Riehle A, Von Kurthy G, Lang F, et al. CD95/CD95 ligand interactions on epithelial cells in host defense to Pseudomonas aeruginosa. Science. (2000) 290:527–30. doi: 10.1126/science.290.5491.527
86. Riquelme SA, Hopkins BD, Wolfe AL, Dimango E, Kitur K, Parsons R, et al. Cystic fibrosis transmembrane conductance regulator attaches tumor suppressor PTEN to the membrane and promotes anti Pseudomonas aeruginosa immunity. Immunity. (2017) 47: 1169–81.e7. doi: 10.1016/j.immuni.2017.11.010
87. Choi JK, Naffouje SA, Goto M, Wang J, Christov K, Rademacher DJ, et al. Cross-talk between cancer and Pseudomonas aeruginosa mediates tumor suppression. Commun Biol. (2023) 6:16. doi: 10.1038/s42003-022-04395-5
88. Kahle NA, Brenner-Weiss G, Overhage J, Obst U, Hansch GM. Bacterial quorum sensing molecule induces chemotaxis of human neutrophils via induction of p38 and leukocyte specific protein 1 (LSP1). Immunobiology. (2013) 218:145–51. doi: 10.1016/j.imbio.2012.02.004
89. Williams SC, Patterson EK, Carty NL, Griswold JA, Hamood AN, Rumbaugh KP. Pseudomonas aeruginosa autoinducer enters and functions in mammalian cells. J Bacteriol. (2004) 186:2281–7. doi: 10.1128/JB.186.8.2281-2287.2004
90. Ras GJ, Anderson R, Taylor GW, Savage JE, Van Niekerk E, Wilson R, et al. Proinflammatory interactions of pyocyanin and 1-hydroxyphenazine with human neutrophils in vitro. J Infect Dis. (1990) 162:178–85. doi: 10.1093/infdis/162.1.178
91. Manago A, Becker KA, Carpinteiro A, Wilker B, Soddemann M, Seitz AP, et al. Pseudomonas aeruginosa pyocyanin induces neutrophil death via mitochondrial reactive oxygen species and mitochondrial acid sphingomyelinase. Antioxid Redox Signal. (2015) 22:1097–110. doi: 10.1089/ars.2014.5979
92. Pan X, Fan Z, Chen L, Liu C, Bai F, Wei Y, et al. PvrA is a novel regulator that contributes to Pseudomonas aeruginosa pathogenesis by controlling bacterial utilization of long chain fatty acids. Nucleic Acids Res. (2020) 48:5967–85. doi: 10.1093/nar/gkaa377
93. Koller B, Kappler M, Latzin P, Gaggar A, Schreiner M, Takyar S, et al. TLR expression on neutrophils at the pulmonary site of infection: TLR1/TLR2-mediated up-regulation of TLR5 expression in cystic fibrosis lung disease. J Immunol. (2008) 181:2753–63. doi: 10.4049/jimmunol.181.4.2753
94. Roy S, Karmakar M, Pearlman E. CD14 mediates Toll-like receptor 4 (TLR4). endocytosis and spleen tyrosine kinase (Syk). and interferon regulatory transcription factor 3 (IRF3). activation in epithelial cells and impairs neutrophil infiltration and Pseudomonas aeruginosa killing in vivo. J Biol Chem. (2014) 289:1174–82. doi: 10.1074/jbc.M113.523167
95. Barnes RJ, Leung KT, Schraft H, Ulanova M. Chromosomal gfp labelling of Pseudomonas aeruginosa using a mini-Tn7 transposon: application for studies of bacteria- host interactions. Can J Microbiol. (2008) 54:48–57. doi: 10.1139/w07-118
96. Yang JJ, Tsuei KC, Shen EP. The role of Type III secretion system in the pathogenesis of Pseudomonas aeruginosa microbial keratitis. Tzu Chi Med J. (2022) 34:8–14. doi: 10.4103/tcmj.tcmj_47_21
97. Aljohmani A, Opitz B, Bischoff M, Yildiz D. Pseudomonas aeruginosa triggered exosomal release of ADAM10 mediates proteolytic cleavage in trans. Int J Mol Sci. (2022) 23:1259. doi: 10.3390/ijms23031259
98. Forbes A, Davey AK, Perkins AV, Grant GD, Mcfarland AJ, Mcdermott CM, et al. ERK1/2 activation modulates pyocyanin-induced toxicity in A549 respiratory epithelial cells. Chem Biol Interact. (2014) 208:58–63. doi: 10.1016/j.cbi.2013.11.016
99. Gustke H, Kleene R, Loers G, Nehmann N, Jaehne M, Bartels KM, et al. Inhibition of the bacterial lectins of Pseudomonas aeruginosa with monosaccharides and peptides. Eur J Clin Microbiol Infect Dis. (2012) 31:207–15. doi: 10.1007/s10096-011-1295-x
100. Badaoui M, Zoso A, Idris T, Bacchetta M, Simonin J, Lemeille S, et al. Vav3 Mediates Pseudomonas aeruginosa adhesion to the cystic fibrosis airway epithelium. Cell Rep. (2020) 32:107842. doi: 10.1016/j.celrep.2020.107842
101. Gomez MI, Sokol SH, Muir AB, Soong G, Bastien J, Prince AS. Bacterial induction of TNF-alpha converting enzyme expression and IL-6 receptor alpha shedding regulates airway inflammatory signaling. J Immunol. (2005) 175:1930–6. doi: 10.4049/jimmunol.175.3.1930
102. Okuda J, Hayashi N, Okamoto M, Sawada S, Minagawa S, Yano Y, et al. Translocation of Pseudomonas aeruginosa from the intestinal tract is mediated by the binding of ExoS to an Na,K-ATPase regulator. FXYD3. Infect Immun. (2010) 78:4511–22. doi: 10.1128/IAI.00428-10
103. Lassek C, Burghartz M, Chaves-Moreno D, Otto A, Hentschker C, Fuchs S, et al. A metaproteomics approach to elucidate host and pathogen protein expression during catheter-associated urinary tract infections (CAUTIs). Mol Cell Proteomics. (2015) 14:989–1008. doi: 10.1074/mcp.M114.043463
104. Faudry E, Job V, Dessen A, Attree I, Forge V. Type III secretion system translocator has a molten globule conformation both in its free and chaperone-bound forms. FEBS J. (2007) 274:3601–10. doi: 10.1111/j.1742-4658.2007.05893.x
105. Bainbridge T, Fick RB. Functional importance of cystic fibrosis immunoglobulin G fragments generated by Pseudomonas aeruginosa elastase. J Lab Clin Med. (1989) 114:728–33.
106. Wen L, Shi L, Kong XL, Li KY, Li H, Jiang DX, et al. Gut microbiota protected against pseudomonas aeruginosa pneumonia via restoring Treg/Th17 balance and metabolism. Front Cell Infect Microbiol. (2022) 12:856633. doi: 10.3389/fcimb.2022.856633
107. Kruczek C, Kottapalli KR, Dissanaike S, Dzvova N, Griswold JA, Colmer-Hamood JA, et al. Major transcriptome changes accompany the growth of Pseudomonas aeruginosa in blood from patients with severe thermal injuries. PLoS One. (2016) 11:e0149229. doi: 10.1371/journal.pone.0149229
108. Elmassry MM, Mudaliar NS, Colmer-Hamood JA, San Francisco MJ, Griswold JA, Dissanaike S, et al. New markers for sepsis caused by Pseudomonas aeruginosa during burn infection. Metabolomics. (2020) 16:40. doi: 10.1007/s11306-020-01658-2
109. Barbieri JT. Pseudomonas aeruginosa exoenzyme S, a bifunctional type-III secreted cytotoxin. Int J Med Microbiol. (2000) 290:381–7. doi: 10.1016/S1438-4221(00)80047-8
110. Qin S, Xiao W, Zhou C, Pu Q, Deng X, Lan L, et al. Pseudomonas aeruginosa: pathogenesis, virulence factors, antibiotic resistance, interaction with host, technology advances and emerging therapeutics. Signal Transduct Target Ther. (2022) 7:199. doi: 10.1038/s41392-022-01056-1
111. Sinha M, Ghosh N, Wijesinghe DS, Mathew-Steiner SS, Das A, Singh K, et al. Pseudomonas aeruginosa theft biofilm require host lipids of cutaneous wound. Ann Surg. (2023) 277:e634–47. doi: 10.1097/SLA.0000000000005252
112. Chadha J, Harjai K, Chhibber S. Revisiting the virulence hallmarks of Pseudomonas aeruginosa: a chronicle through the perspective of quorum sensing. Environ Microbiol. (2022) 24:2630–56. doi: 10.1111/1462-2920.15784
113. Jia J, Wang Y, Zhou L, Jin S. Expression of Pseudomonas aeruginosa toxin ExoS effectively induces apoptosis in host cells. Infect Immun. (2006) 74:6557–70. doi: 10.1128/IAI.00591-06
114. Arhin A, Boucher C. The outer membrane protein OprQ and adherence of Pseudomonas aeruginosa to human fibronectin. Microbiology. (2010) 156:1415–23. doi: 10.1099/mic.0.033472-0
115. Sugawara E, Nagano K, Nikaido H. Alternative folding pathways of the major porin OprF of Pseudomonas aeruginosa. FEBS J. (2012) 279:910–8. doi: 10.1111/j.1742-4658.2012.08481.x
116. Mishra M, Ressler A, Schlesinger LS, Wozniak DJ. Identification of OprF as a complement component C3 binding acceptor molecule on the surface of Pseudomonas aeruginosa. Infect Immun. (2015) 83:3006–14. doi: 10.1128/IAI.00081-15
117. Matsumoto K. Role of bacterial proteases in pseudomonal and serratial keratitis. Biol Chem. (2004) 385:1007–16. doi: 10.1515/BC.2004.131
118. Phuong MS, Hernandez RE, Wolter DJ, Hoffman LR, Sad S. Impairment in inflammasome signaling by the chronic Pseudomonas aeruginosa isolates from cystic fibrosis patients results in an increase in inflammatory response. Cell Death Dis. (2021) 12:241. doi: 10.1038/s41419-021-03526-w
119. Lee JH, Jeon J, Bai F, Wu W, Ha UH. Negative regulation of interleukin 1beta expression in response to DnaK from Pseudomonas aeruginosa via the PI3K/PDK1/FoxO1 pathways. Comp Immunol Microbiol Infect Dis. (2020) 73:101543. doi: 10.1016/j.cimid.2020.101543
120. Colombo C, Costantini D, Rocchi A, Cariani L, Garlaschi ML, Tirelli S, et al. Cytokine levels in sputum of cystic fibrosis patients before and after antibiotic therapy. Pediatr Pulmonol. (2005) 40:15–21. doi: 10.1002/ppul.20237
Keywords: P. aeruginosa, host-pathogen interaction, bacterial infection, disease map, sepsis
Citation: Messina F, Rotondo C, Ladeira L, Crosetti S, Properzi M, Dimartino V, Riccitelli B, Staumont B, Chillemi G, Geris L, Bocci MG and Fontana C (2025) Molecular exploration of host-pathogen interactions in severe Pseudomonas aeruginosa infection through a multi-level data integration approach. Front. Med. 12:1600509. doi: 10.3389/fmed.2025.1600509
Received: 27 March 2025; Accepted: 22 September 2025;
Published: 14 October 2025.
Edited by:
Alessandro Perrella, Hospital of the Hills, ItalyReviewed by:
Alvaro Mourenza Flórez, University of Southern California, United StatesShifu Aggarwal, Massachusetts General Hospital and Harvard Medical School, United States
Copyright © 2025 Messina, Rotondo, Ladeira, Crosetti, Properzi, Dimartino, Riccitelli, Staumont, Chillemi, Geris, Bocci and Fontana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Carla Fontana, Y2FybGEuZm9udGFuYUBpbm1pLml0
†These authors have contributed equally to this work